From f98c1ae35a06fcbe9950ed61971a2a8a10729340 Mon Sep 17 00:00:00 2001 From: inaniloquentee <3051000145@qq.com> Date: Thu, 18 Jun 2026 17:15:37 +0800 Subject: [PATCH 1/5] Add RL-Kernel integration flags Signed-off-by: inaniloquentee <3051000145@qq.com> --- tests/test_rl_kernel_args.py | 64 +++++++++++++++++++++++++++++ vime/utils/arguments.py | 22 ++++++++++ vime/utils/rl_kernel.py | 79 ++++++++++++++++++++++++++++++++++++ 3 files changed, 165 insertions(+) create mode 100644 tests/test_rl_kernel_args.py create mode 100644 vime/utils/rl_kernel.py diff --git a/tests/test_rl_kernel_args.py b/tests/test_rl_kernel_args.py new file mode 100644 index 00000000..59e16546 --- /dev/null +++ b/tests/test_rl_kernel_args.py @@ -0,0 +1,64 @@ +from argparse import Namespace + +import pytest + +from vime.utils.rl_kernel import is_rl_kernel_op_enabled, normalize_rl_kernel_args, parse_rl_kernel_ops + +NUM_GPUS = 0 + + +@pytest.mark.unit +def test_parse_rl_kernel_ops_defaults_to_logp(): + assert parse_rl_kernel_ops(None) == ("logp",) + assert parse_rl_kernel_ops("") == ("logp",) + + +@pytest.mark.unit +def test_parse_rl_kernel_ops_deduplicates_comma_and_space_separated_values(): + assert parse_rl_kernel_ops("logp, ratio_kl logp") == ("logp", "ratio_kl") + + +@pytest.mark.unit +def test_parse_rl_kernel_ops_rejects_unknown_ops(): + with pytest.raises(ValueError, match="Unsupported RL-Kernel op"): + parse_rl_kernel_ops("logp,moe") + + +@pytest.mark.unit +def test_normalize_rl_kernel_args_keeps_default_disabled(): + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + + normalize_rl_kernel_args(args) + + assert args.enable_rl_kernel is False + assert args.rl_kernel_ops == ("logp",) + assert is_rl_kernel_op_enabled(args, "logp") is False + + +@pytest.mark.unit +def test_normalize_rl_kernel_args_accepts_env_enable(monkeypatch): + monkeypatch.setenv("VIME_RL_KERNEL", "1") + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + + normalize_rl_kernel_args(args) + + assert args.enable_rl_kernel is True + assert args.rl_kernel_ops == ("logp",) + assert is_rl_kernel_op_enabled(args, "logp") is True + + +@pytest.mark.unit +def test_normalize_rl_kernel_args_rejects_future_ops_for_current_integration(): + args = Namespace(enable_rl_kernel=True, rl_kernel_ops="logp,ratio_kl", rl_kernel_strict=False) + + with pytest.raises(ValueError, match="currently supports only logp"): + normalize_rl_kernel_args(args) + + +@pytest.mark.unit +def test_normalize_rl_kernel_args_rejects_bad_env_bool(monkeypatch): + monkeypatch.setenv("VIME_RL_KERNEL", "maybe") + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + + with pytest.raises(ValueError, match="VIME_RL_KERNEL"): + normalize_rl_kernel_args(args) diff --git a/vime/utils/arguments.py b/vime/utils/arguments.py index 494d2c05..34ffa666 100644 --- a/vime/utils/arguments.py +++ b/vime/utils/arguments.py @@ -12,6 +12,7 @@ from vime.backends.vllm_utils.arguments import vllm_parse_args from vime.utils.eval_config import EvalDatasetConfig, build_eval_dataset_configs, ensure_dataset_list from vime.utils.logging_utils import configure_logger +from vime.utils.rl_kernel import normalize_rl_kernel_args logger = logging.getLogger(__name__) @@ -157,6 +158,24 @@ def add_train_arguments(parser): parser.add_argument( "--log-probs-chunk-size", type=int, default=-1, help="Chunk size to compute log probs to save memory" ) + parser.add_argument( + "--enable-rl-kernel", + action="store_true", + default=False, + help="Enable optional RL-Kernel acceleration for supported vime training/forward-only paths.", + ) + parser.add_argument( + "--rl-kernel-ops", + type=str, + default="logp", + help="Comma-separated RL-Kernel ops to enable. Current production integration supports: logp.", + ) + parser.add_argument( + "--rl-kernel-strict", + action="store_true", + default=False, + help="Raise instead of falling back when an enabled RL-Kernel op is unavailable or unsupported.", + ) parser.add_argument( "--only-train-params-name-list", type=str, @@ -1606,6 +1625,8 @@ def _resolve_eval_datasets(args) -> list[EvalDatasetConfig]: def vime_validate_args(args): + normalize_rl_kernel_args(args) + args.eval_datasets = _resolve_eval_datasets(args) if args.kl_coef != 0 or args.use_kl_loss: @@ -1859,6 +1880,7 @@ def vime_validate_args(args): if not hasattr(args, "_vllm_user_provided"): args._vllm_user_provided = set() args._vllm_user_provided.add(k) + normalize_rl_kernel_args(args) if args.eval_max_context_len is None: logger.info( diff --git a/vime/utils/rl_kernel.py b/vime/utils/rl_kernel.py new file mode 100644 index 00000000..c01e02db --- /dev/null +++ b/vime/utils/rl_kernel.py @@ -0,0 +1,79 @@ +import os +from argparse import Namespace +from collections.abc import Iterable + + +RL_KERNEL_SUPPORTED_OPS = ("logp", "ratio_kl", "grpo_loss") +RL_KERNEL_INTEGRATED_OPS = ("logp",) +_TRUE_VALUES = {"1", "true", "yes", "on"} +_FALSE_VALUES = {"0", "false", "no", "off"} + + +def parse_rl_kernel_ops(value: str | Iterable[str] | None) -> tuple[str, ...]: + """Parse a comma/space separated RL-Kernel op list.""" + if value is None: + return ("logp",) + + if isinstance(value, str): + raw_items = value.replace(",", " ").split() + else: + raw_items = [] + for item in value: + raw_items.extend(str(item).replace(",", " ").split()) + + ops: list[str] = [] + for item in raw_items: + op = item.strip().lower() + if not op: + continue + if op not in RL_KERNEL_SUPPORTED_OPS: + supported = ", ".join(RL_KERNEL_SUPPORTED_OPS) + raise ValueError(f"Unsupported RL-Kernel op '{op}'. Supported ops: {supported}.") + if op not in ops: + ops.append(op) + + return tuple(ops) if ops else ("logp",) + + +def _env_bool(name: str) -> bool | None: + value = os.getenv(name) + if value is None: + return None + normalized = value.strip().lower() + if normalized in _TRUE_VALUES: + return True + if normalized in _FALSE_VALUES: + return False + raise ValueError(f"{name} must be one of: 1/0, true/false, yes/no, on/off.") + + +def normalize_rl_kernel_args(args: Namespace) -> Namespace: + """Apply environment overrides and validate RL-Kernel integration args.""" + env_enabled = _env_bool("VIME_RL_KERNEL") + if env_enabled is not None: + args.enable_rl_kernel = env_enabled + + env_strict = _env_bool("VIME_RL_KERNEL_STRICT") + if env_strict is not None: + args.rl_kernel_strict = env_strict + + env_ops = os.getenv("VIME_RL_KERNEL_OPS") + if env_ops is not None: + args.rl_kernel_ops = env_ops + + args.rl_kernel_ops = parse_rl_kernel_ops(getattr(args, "rl_kernel_ops", None)) + + if getattr(args, "enable_rl_kernel", False): + unsupported = [op for op in args.rl_kernel_ops if op not in RL_KERNEL_INTEGRATED_OPS] + if unsupported: + integrated = ", ".join(RL_KERNEL_INTEGRATED_OPS) + raise ValueError( + "This vime RL-Kernel integration currently supports only " + f"{integrated}. Requested future ops: {', '.join(unsupported)}." + ) + + return args + + +def is_rl_kernel_op_enabled(args: Namespace, op: str) -> bool: + return getattr(args, "enable_rl_kernel", False) and op in getattr(args, "rl_kernel_ops", ()) From 743948d0835d4ba75946780954a75b12554aa4d4 Mon Sep 17 00:00:00 2001 From: inaniloquentee <3051000145@qq.com> Date: Thu, 18 Jun 2026 17:15:37 +0800 Subject: [PATCH 2/5] Integrate RL-Kernel logp for forward-only paths Signed-off-by: inaniloquentee <3051000145@qq.com> --- tests/_unit_stubs.py | 1 + tests/test_rl_kernel_logp_integration.py | 181 ++++++++++++++++++++++ vime/backends/megatron_utils/loss.py | 21 ++- vime/backends/megatron_utils/rl_kernel.py | 95 ++++++++++++ 4 files changed, 290 insertions(+), 8 deletions(-) create mode 100644 tests/test_rl_kernel_logp_integration.py create mode 100644 vime/backends/megatron_utils/rl_kernel.py diff --git a/tests/_unit_stubs.py b/tests/_unit_stubs.py index ed986355..7fe27a24 100644 --- a/tests/_unit_stubs.py +++ b/tests/_unit_stubs.py @@ -223,4 +223,5 @@ def install_triton_stub() -> None: def install_vime_distributed_utils_stub() -> None: vime_utils = types.ModuleType("vime.utils.distributed_utils") vime_utils.get_gloo_group = MagicMock(return_value="gloo") + vime_utils.distributed_masked_whiten = MagicMock(side_effect=lambda values, *args, **kwargs: values) sys.modules.setdefault("vime.utils.distributed_utils", vime_utils) diff --git a/tests/test_rl_kernel_logp_integration.py b/tests/test_rl_kernel_logp_integration.py new file mode 100644 index 00000000..4941e01c --- /dev/null +++ b/tests/test_rl_kernel_logp_integration.py @@ -0,0 +1,181 @@ +from __future__ import annotations + +import sys +import types +from argparse import Namespace +from pathlib import Path + +import pytest +import torch + +_tests_root = Path(__file__).resolve().parent +if str(_tests_root) not in sys.path: + sys.path.insert(0, str(_tests_root)) + +import _unit_stubs + +_unit_stubs.install_megatron_mpu_stub() +_unit_stubs.install_vime_distributed_utils_stub() + +from megatron.core import mpu # noqa: E402 + +from vime.backends.megatron_utils import loss as loss_mod # noqa: E402 +from vime.backends.megatron_utils import rl_kernel as rlk_mod # noqa: E402 + + +NUM_GPUS = 0 + + +class _FakeLogpOp: + calls = 0 + + def apply_fp32(self, logits: torch.Tensor, token_ids: torch.Tensor) -> torch.Tensor: + type(self).calls += 1 + return torch.gather(torch.log_softmax(logits.float(), dim=-1), -1, token_ids.long().unsqueeze(-1)).squeeze(-1) + + +def _reset_rl_kernel_state(): + rlk_mod._LOGP_OP = None + rlk_mod._LOGP_OP_LOAD_ERROR = None + rlk_mod._WARNED_FALLBACK_REASONS.clear() + _FakeLogpOp.calls = 0 + + +def _install_fake_rl_engine(monkeypatch): + rl_engine = types.ModuleType("rl_engine") + kernels = types.ModuleType("rl_engine.kernels") + registry = types.ModuleType("rl_engine.kernels.registry") + registry.kernel_registry = types.SimpleNamespace(get_op=lambda name: _FakeLogpOp()) + monkeypatch.setitem(sys.modules, "rl_engine", rl_engine) + monkeypatch.setitem(sys.modules, "rl_engine.kernels", kernels) + monkeypatch.setitem(sys.modules, "rl_engine.kernels.registry", registry) + + +def _make_args(**overrides) -> Namespace: + values = { + "enable_rl_kernel": True, + "rl_kernel_ops": ("logp",), + "rl_kernel_strict": False, + "allgather_cp": False, + "qkv_format": "thd", + "rollout_temperature": 1.0, + "log_probs_chunk_size": -1, + } + values.update(overrides) + return Namespace(**values) + + +@pytest.fixture(autouse=True) +def reset_parallelism(): + _reset_rl_kernel_state() + mpu.get_tensor_model_parallel_world_size.return_value = 1 + mpu.get_tensor_model_parallel_group.return_value = None + mpu.get_context_parallel_world_size.return_value = 1 + mpu.get_context_parallel_rank.return_value = 0 + yield + _reset_rl_kernel_state() + + +@pytest.mark.unit +def test_rl_kernel_logp_matches_vime_response_slicing(monkeypatch): + _install_fake_rl_engine(monkeypatch) + args = _make_args() + vocab_size = 257 + total_lengths = [9, 7, 11] + response_lengths = [4, 3, 6] + torch.manual_seed(17) + unconcat_tokens = [torch.randint(0, vocab_size, (length,), dtype=torch.long) for length in total_lengths] + logits = torch.randn(1, sum(total_lengths), vocab_size, dtype=torch.float32) + + with torch.no_grad(): + _, result = loss_mod.get_log_probs_and_entropy( + logits, + args=args, + unconcat_tokens=unconcat_tokens, + total_lengths=total_lengths, + response_lengths=response_lengths, + with_entropy=False, + ) + + expected = [] + offset = 0 + log_probs = torch.log_softmax(logits.squeeze(0), dim=-1) + for tokens, total_length, response_length in zip(unconcat_tokens, total_lengths, response_lengths, strict=False): + start = offset + total_length - response_length + end = offset + total_length + shifted_tokens = tokens[-response_length:] + expected.append(torch.gather(log_probs[start - 1 : end - 1], -1, shifted_tokens.unsqueeze(-1)).squeeze(-1)) + offset += total_length + + assert _FakeLogpOp.calls == 1 + assert len(result["log_probs"]) == len(expected) + for actual, expected_item in zip(result["log_probs"], expected, strict=True): + torch.testing.assert_close(actual, expected_item, rtol=1e-6, atol=1e-6) + + +@pytest.mark.unit +def test_rl_kernel_logp_falls_back_when_entropy_requested(monkeypatch): + _install_fake_rl_engine(monkeypatch) + args = _make_args() + logits = torch.randn(8, 16, dtype=torch.float32) + tokens = torch.randint(0, 16, (8,), dtype=torch.long) + + with torch.no_grad(): + actual = rlk_mod.maybe_compute_logp(logits, tokens, args=args, with_entropy=True) + + assert actual is None + assert _FakeLogpOp.calls == 0 + + +@pytest.mark.unit +def test_rl_kernel_logp_falls_back_for_tensor_parallel_vocab(monkeypatch): + _install_fake_rl_engine(monkeypatch) + mpu.get_tensor_model_parallel_world_size.return_value = 2 + args = _make_args() + logits = torch.randn(8, 16, dtype=torch.float32) + tokens = torch.randint(0, 16, (8,), dtype=torch.long) + + with torch.no_grad(): + actual = rlk_mod.maybe_compute_logp(logits, tokens, args=args, with_entropy=False) + + assert actual is None + assert _FakeLogpOp.calls == 0 + + +@pytest.mark.unit +def test_rl_kernel_logp_strict_mode_raises_on_unsupported_parallelism(monkeypatch): + _install_fake_rl_engine(monkeypatch) + mpu.get_tensor_model_parallel_world_size.return_value = 2 + args = _make_args(rl_kernel_strict=True) + logits = torch.randn(8, 16, dtype=torch.float32) + tokens = torch.randint(0, 16, (8,), dtype=torch.long) + + with pytest.raises(RuntimeError, match="tensor-parallel vocab shards"): + with torch.no_grad(): + rlk_mod.maybe_compute_logp(logits, tokens, args=args, with_entropy=False) + + +@pytest.mark.unit +def test_rl_kernel_logp_falls_back_when_optional_package_missing(monkeypatch): + import builtins + + for name in list(sys.modules): + if name == "rl_engine" or name.startswith("rl_engine."): + monkeypatch.delitem(sys.modules, name, raising=False) + + original_import = builtins.__import__ + + def fail_rl_engine_import(name, *args, **kwargs): + if name == "rl_engine" or name.startswith("rl_engine."): + raise ModuleNotFoundError("No module named 'rl_engine'") + return original_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", fail_rl_engine_import) + args = _make_args() + logits = torch.randn(8, 16, dtype=torch.float32) + tokens = torch.randint(0, 16, (8,), dtype=torch.long) + + with torch.no_grad(): + actual = rlk_mod.maybe_compute_logp(logits, tokens, args=args, with_entropy=False) + + assert actual is None diff --git a/vime/backends/megatron_utils/loss.py b/vime/backends/megatron_utils/loss.py index 3f6ab29c..6a9cbf0d 100644 --- a/vime/backends/megatron_utils/loss.py +++ b/vime/backends/megatron_utils/loss.py @@ -29,6 +29,7 @@ get_sum_of_sample_mean, slice_log_prob_with_cp, ) +from .rl_kernel import maybe_compute_logp def get_responses( @@ -431,14 +432,18 @@ def get_log_probs_and_entropy( T, device, unconcat_tokens, total_lengths, response_lengths, qkv_format, max_seq_lens, args.allgather_cp ) - # --- compute on full [T,V] logits at once via calculate_log_probs_and_entropy --- - log_prob_full, entropy_full = calculate_log_probs_and_entropy( - logits, - full_tokens, - tp_group, - with_entropy=with_entropy, - chunk_size=chunk_size, - ) + # --- compute on full [T,V] logits at once --- + log_prob_full = maybe_compute_logp(logits, full_tokens, args=args, with_entropy=with_entropy) + if log_prob_full is None: + log_prob_full, entropy_full = calculate_log_probs_and_entropy( + logits, + full_tokens, + tp_group, + with_entropy=with_entropy, + chunk_size=chunk_size, + ) + else: + entropy_full = None log_prob_full = log_prob_full.squeeze(-1) # [T, 1] -> [T] # --- extract per-sample response portions --- diff --git a/vime/backends/megatron_utils/rl_kernel.py b/vime/backends/megatron_utils/rl_kernel.py new file mode 100644 index 00000000..7f712b32 --- /dev/null +++ b/vime/backends/megatron_utils/rl_kernel.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import logging +from argparse import Namespace + +import torch +from megatron.core import mpu + +from vime.utils.rl_kernel import is_rl_kernel_op_enabled + +logger = logging.getLogger(__name__) + +_LOGP_OP = None +_LOGP_OP_LOAD_ERROR: Exception | None = None +_WARNED_FALLBACK_REASONS: set[str] = set() + + +def _warn_fallback(args: Namespace, reason: str) -> None: + if getattr(args, "rl_kernel_strict", False): + raise RuntimeError(f"RL-Kernel logp is enabled but unavailable: {reason}") + if reason not in _WARNED_FALLBACK_REASONS: + logger.warning("Falling back to vime logprob path because RL-Kernel logp is unavailable: %s", reason) + _WARNED_FALLBACK_REASONS.add(reason) + + +def _get_logp_op(args: Namespace): + global _LOGP_OP, _LOGP_OP_LOAD_ERROR + if _LOGP_OP is not None: + return _LOGP_OP + if _LOGP_OP_LOAD_ERROR is not None: + _warn_fallback(args, str(_LOGP_OP_LOAD_ERROR)) + return None + + try: + from rl_engine.kernels.registry import kernel_registry + + _LOGP_OP = kernel_registry.get_op("logp") + logger.info("Using RL-Kernel logp op: %s", type(_LOGP_OP).__name__) + return _LOGP_OP + except Exception as exc: # pragma: no cover - exercised with missing optional package in integration envs + _LOGP_OP_LOAD_ERROR = exc + _warn_fallback(args, str(exc)) + return None + + +def maybe_compute_logp( + logits: torch.Tensor, + tokens: torch.Tensor, + *, + args: Namespace, + with_entropy: bool, +) -> torch.Tensor | None: + """Return selected log-probs from RL-Kernel when this runtime is safe. + + The first integration deliberately limits itself to forward-only logprob + precompute paths: no autograd, no vocab tensor parallelism, no CP + redistribution, and no entropy. Unsupported cases fall back to vime's + Megatron-aware implementation. + """ + if not is_rl_kernel_op_enabled(args, "logp"): + return None + + if with_entropy: + _warn_fallback(args, "entropy is requested") + return None + + if logits.requires_grad or torch.is_grad_enabled(): + _warn_fallback(args, "autograd is enabled") + return None + + if mpu.get_tensor_model_parallel_world_size() != 1: + _warn_fallback(args, "tensor-parallel vocab shards are not supported by RL-Kernel logp") + return None + + if mpu.get_context_parallel_world_size() != 1 or getattr(args, "allgather_cp", False): + _warn_fallback(args, "context parallel logprob redistribution is not supported by RL-Kernel logp") + return None + + if logits.size(0) == 0: + return logits.new_zeros((0,), dtype=torch.float32) + + op = _get_logp_op(args) + if op is None: + return None + + try: + if hasattr(op, "apply_fp32"): + log_prob = op.apply_fp32(logits, tokens) + else: + log_prob = op(logits, tokens).float() + except Exception as exc: + _warn_fallback(args, str(exc)) + return None + + return log_prob.reshape(-1) From 72bd5201e55da44c3c91e3e85921e3c3a9090d70 Mon Sep 17 00:00:00 2001 From: inaniloquentee <3051000145@qq.com> Date: Sat, 27 Jun 2026 16:18:42 +0800 Subject: [PATCH 3/5] Add RL-Kernel linear_logp benchmark integration Signed-off-by: inaniloquentee <3051000145@qq.com> --- scripts/run-qwen3-30B-A3B.sh | 71 +++- tests/test_rl_kernel_args.py | 49 ++- .../test_rl_kernel_linear_logp_integration.py | 378 ++++++++++++++++++ tests/test_rl_kernel_logp_integration.py | 4 + vime/backends/megatron_utils/loss.py | 142 ++++++- vime/backends/megatron_utils/model.py | 83 +++- vime/backends/megatron_utils/rl_kernel.py | 245 +++++++++++- vime/utils/arguments.py | 4 +- vime/utils/rl_kernel.py | 8 +- 9 files changed, 904 insertions(+), 80 deletions(-) create mode 100644 tests/test_rl_kernel_linear_logp_integration.py diff --git a/scripts/run-qwen3-30B-A3B.sh b/scripts/run-qwen3-30B-A3B.sh index 3597c115..726a929b 100644 --- a/scripts/run-qwen3-30B-A3B.sh +++ b/scripts/run-qwen3-30B-A3B.sh @@ -24,9 +24,40 @@ else fi echo "HAS_NVLINK: $HAS_NVLINK (detected $NVLINK_COUNT NVLink references)" +if command -v nvidia-smi >/dev/null 2>&1; then + DETECTED_GPUS=$(nvidia-smi -L 2>/dev/null | wc -l | tr -d ' ') + DETECTED_GPU_NAME=$(nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null | head -n 1) +else + DETECTED_GPUS=0 + DETECTED_GPU_NAME="unknown" +fi +NUM_GPUS=${NUM_GPUS:-8} +if [ -z "$NUM_GPUS" ] || [ "$NUM_GPUS" -le 0 ]; then + NUM_GPUS=8 +fi +if [ "$DETECTED_GPUS" -gt 0 ] && [ "$NUM_GPUS" -gt "$DETECTED_GPUS" ]; then + echo "Requested NUM_GPUS=$NUM_GPUS but only detected $DETECTED_GPUS GPUs" >&2 + exit 1 +fi +echo "BENCHMARK_GPU: ${DETECTED_GPU_NAME}" +echo "NUM_GPUS: $NUM_GPUS" + SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" +VIME_ROOT="$(cd -- "${SCRIPT_DIR}/.." &>/dev/null && pwd)" source "${SCRIPT_DIR}/models/qwen3-30B-A3B.sh" +MEGATRON_TP=${MEGATRON_TP:-4} +MEGATRON_EP=${MEGATRON_EP:-${NUM_GPUS}} +MEGATRON_CP=${MEGATRON_CP:-1} +MAX_TOKENS_PER_GPU=${MAX_TOKENS_PER_GPU:-20480} +NUM_ROLLOUT=${NUM_ROLLOUT:-3000} +ROLLOUT_BATCH_SIZE=${ROLLOUT_BATCH_SIZE:-32} +N_SAMPLES_PER_PROMPT=${N_SAMPLES_PER_PROMPT:-8} +ROLLOUT_MAX_RESPONSE_LEN=${ROLLOUT_MAX_RESPONSE_LEN:-8192} +GLOBAL_BATCH_SIZE=${GLOBAL_BATCH_SIZE:-$((ROLLOUT_BATCH_SIZE * N_SAMPLES_PER_PROMPT))} +ROLLOUT_NUM_GPUS_PER_ENGINE=${ROLLOUT_NUM_GPUS_PER_ENGINE:-${NUM_GPUS}} +VLLM_GPU_MEMORY_UTILIZATION=${VLLM_GPU_MEMORY_UTILIZATION:-0.7} + CKPT_ARGS=( --hf-checkpoint /root/Qwen3-30B-A3B #--hf-checkpoint /root/Qwen3-30B-A3B-FP8 @@ -43,13 +74,13 @@ ROLLOUT_ARGS=( --apply-chat-template --rollout-shuffle --rm-type deepscaler - --num-rollout 3000 - --rollout-batch-size 32 - --n-samples-per-prompt 8 - --rollout-max-response-len 8192 + --num-rollout "${NUM_ROLLOUT}" + --rollout-batch-size "${ROLLOUT_BATCH_SIZE}" + --n-samples-per-prompt "${N_SAMPLES_PER_PROMPT}" + --rollout-max-response-len "${ROLLOUT_MAX_RESPONSE_LEN}" --rollout-temperature 1 - --global-batch-size 256 + --global-batch-size "${GLOBAL_BATCH_SIZE}" --balance-data ) @@ -62,11 +93,11 @@ EVAL_ARGS=( ) PERF_ARGS=( - --tensor-model-parallel-size 4 + --tensor-model-parallel-size "${MEGATRON_TP}" --sequence-parallel --pipeline-model-parallel-size 1 - --context-parallel-size 1 - --expert-model-parallel-size 8 + --context-parallel-size "${MEGATRON_CP}" + --expert-model-parallel-size "${MEGATRON_EP}" --expert-tensor-parallel-size 1 --recompute-granularity full @@ -75,7 +106,7 @@ PERF_ARGS=( # --micro-batch-size 1 --use-dynamic-batch-size - --max-tokens-per-gpu 20480 + --max-tokens-per-gpu "${MAX_TOKENS_PER_GPU}" ) GRPO_ARGS=( @@ -109,8 +140,9 @@ WANDB_ARGS=( ) VLLM_ARGS=( - --rollout-num-gpus-per-engine 8 - --vllm-gpu-memory-utilization 0.7 + --rollout-num-gpus-per-engine "${ROLLOUT_NUM_GPUS_PER_ENGINE}" + --vllm-gpu-memory-utilization "${VLLM_GPU_MEMORY_UTILIZATION}" + --vllm-enable-expert-parallel --vllm-cudagraph-capture-sizes 1 2 4 8 $(seq 16 8 256) ) @@ -125,14 +157,22 @@ MISC_ARGS=( --attention-backend flash ) +RLK_ARGS=() +if [[ "${VIME_RL_KERNEL:-0}" == "1" ]]; then + RLK_ARGS+=(--enable-rl-kernel --rl-kernel-ops "${VIME_RL_KERNEL_OPS:-linear_logp}") + if [[ "${VIME_RL_KERNEL_STRICT:-0}" == "1" ]]; then + RLK_ARGS+=(--rl-kernel-strict) + fi +fi + # launch the master node of ray in container export MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"} -ray start --head --node-ip-address ${MASTER_ADDR} --num-gpus 8 --disable-usage-stats --dashboard-host=0.0.0.0 --dashboard-port=8265 +ray start --head --node-ip-address ${MASTER_ADDR} --num-gpus ${NUM_GPUS} --disable-usage-stats --dashboard-host=0.0.0.0 --dashboard-port=8265 # Build the runtime environment JSON with proper variable substitution RUNTIME_ENV_JSON="{ \"env_vars\": { - \"PYTHONPATH\": \"/root/Megatron-LM/\", + \"PYTHONPATH\": \"${VIME_ROOT}:/root/Megatron-LM/\", \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\", \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\" } @@ -142,7 +182,7 @@ ray job submit --address="http://127.0.0.1:8265" \ --runtime-env-json="${RUNTIME_ENV_JSON}" \ -- python3 train.py \ --actor-num-nodes 1 \ - --actor-num-gpus-per-node 8 \ + --actor-num-gpus-per-node ${NUM_GPUS} \ --colocate \ ${MODEL_ARGS[@]} \ ${CKPT_ARGS[@]} \ @@ -153,4 +193,5 @@ ray job submit --address="http://127.0.0.1:8265" \ ${PERF_ARGS[@]} \ ${EVAL_ARGS[@]} \ ${VLLM_ARGS[@]} \ - ${MISC_ARGS[@]} + ${MISC_ARGS[@]} \ + ${RLK_ARGS[@]} diff --git a/tests/test_rl_kernel_args.py b/tests/test_rl_kernel_args.py index 59e16546..cd3916c4 100644 --- a/tests/test_rl_kernel_args.py +++ b/tests/test_rl_kernel_args.py @@ -8,57 +8,76 @@ @pytest.mark.unit -def test_parse_rl_kernel_ops_defaults_to_logp(): - assert parse_rl_kernel_ops(None) == ("logp",) - assert parse_rl_kernel_ops("") == ("logp",) +def test_parse_rl_kernel_ops_defaults_to_linear_logp(): + assert parse_rl_kernel_ops(None) == ("linear_logp",) + assert parse_rl_kernel_ops("") == ("linear_logp",) @pytest.mark.unit def test_parse_rl_kernel_ops_deduplicates_comma_and_space_separated_values(): - assert parse_rl_kernel_ops("logp, ratio_kl logp") == ("logp", "ratio_kl") + assert parse_rl_kernel_ops("linear_logp, linear_logp") == ("linear_logp",) @pytest.mark.unit def test_parse_rl_kernel_ops_rejects_unknown_ops(): with pytest.raises(ValueError, match="Unsupported RL-Kernel op"): - parse_rl_kernel_ops("logp,moe") + parse_rl_kernel_ops("linear_logp,moe") @pytest.mark.unit def test_normalize_rl_kernel_args_keeps_default_disabled(): - args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="linear_logp", rl_kernel_strict=False) normalize_rl_kernel_args(args) assert args.enable_rl_kernel is False - assert args.rl_kernel_ops == ("logp",) - assert is_rl_kernel_op_enabled(args, "logp") is False + assert args.rl_kernel_ops == ("linear_logp",) + assert is_rl_kernel_op_enabled(args, "linear_logp") is False @pytest.mark.unit def test_normalize_rl_kernel_args_accepts_env_enable(monkeypatch): monkeypatch.setenv("VIME_RL_KERNEL", "1") - args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="linear_logp", rl_kernel_strict=False) normalize_rl_kernel_args(args) assert args.enable_rl_kernel is True - assert args.rl_kernel_ops == ("logp",) - assert is_rl_kernel_op_enabled(args, "logp") is True + assert args.rl_kernel_ops == ("linear_logp",) + assert is_rl_kernel_op_enabled(args, "linear_logp") is True @pytest.mark.unit -def test_normalize_rl_kernel_args_rejects_future_ops_for_current_integration(): - args = Namespace(enable_rl_kernel=True, rl_kernel_ops="logp,ratio_kl", rl_kernel_strict=False) +def test_normalize_rl_kernel_args_rejects_non_linear_logp_ops(): + args = Namespace(enable_rl_kernel=True, rl_kernel_ops="logp", rl_kernel_strict=False) - with pytest.raises(ValueError, match="currently supports only logp"): + with pytest.raises(ValueError, match="Unsupported RL-Kernel op"): + normalize_rl_kernel_args(args) + + +@pytest.mark.unit +@pytest.mark.parametrize("op", ["ratio_kl", "grpo_loss", "sampling"]) +def test_normalize_rl_kernel_args_rejects_out_of_scope_ops(op): + args = Namespace(enable_rl_kernel=True, rl_kernel_ops=op, rl_kernel_strict=False) + + with pytest.raises(ValueError, match="Unsupported RL-Kernel op"): normalize_rl_kernel_args(args) +@pytest.mark.unit +def test_normalize_rl_kernel_args_accepts_linear_logp(): + args = Namespace(enable_rl_kernel=True, rl_kernel_ops="linear_logp", rl_kernel_strict=False) + + normalize_rl_kernel_args(args) + + assert args.rl_kernel_ops == ("linear_logp",) + assert is_rl_kernel_op_enabled(args, "linear_logp") is True + + @pytest.mark.unit def test_normalize_rl_kernel_args_rejects_bad_env_bool(monkeypatch): monkeypatch.setenv("VIME_RL_KERNEL", "maybe") - args = Namespace(enable_rl_kernel=False, rl_kernel_ops="logp", rl_kernel_strict=False) + args = Namespace(enable_rl_kernel=False, rl_kernel_ops="linear_logp", rl_kernel_strict=False) with pytest.raises(ValueError, match="VIME_RL_KERNEL"): normalize_rl_kernel_args(args) diff --git a/tests/test_rl_kernel_linear_logp_integration.py b/tests/test_rl_kernel_linear_logp_integration.py new file mode 100644 index 00000000..4c0d1448 --- /dev/null +++ b/tests/test_rl_kernel_linear_logp_integration.py @@ -0,0 +1,378 @@ +from __future__ import annotations + +import builtins +import sys +import types +from argparse import Namespace +from pathlib import Path + +import pytest +import torch +import torch.nn.functional as F + +_tests_root = Path(__file__).resolve().parent +if str(_tests_root) not in sys.path: + sys.path.insert(0, str(_tests_root)) + +import _unit_stubs + +_unit_stubs.install_megatron_mpu_stub() +_unit_stubs.install_vime_distributed_utils_stub() + +from megatron.core import mpu # noqa: E402 + +from vime.backends.megatron_utils import loss as loss_mod # noqa: E402 +from vime.backends.megatron_utils import rl_kernel as rlk_mod # noqa: E402 + +NUM_GPUS = 0 + + +class _FakeLinearLogpOp: + calls: list[dict] = [] + + def __call__( + self, + hidden: torch.Tensor, + weight: torch.Tensor, + target_ids: torch.Tensor, + bias: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + type(self).calls.append( + { + "hidden_shape": tuple(hidden.shape), + "weight_shape": tuple(weight.shape), + "target_shape": tuple(target_ids.shape), + "bias": bias is not None, + "kwargs": kwargs, + } + ) + logits = F.linear(hidden.float(), weight.float(), None if bias is None else bias.float()) + return torch.gather(torch.log_softmax(logits, dim=-1), -1, target_ids.long().unsqueeze(-1)).squeeze(-1) + + +class _FakeLegacyLinearLogpOp: + def __call__( + self, + hidden: torch.Tensor, + weight: torch.Tensor, + target_ids: torch.Tensor, + bias: torch.Tensor | None = None, + ) -> torch.Tensor: + logits = F.linear(hidden.float(), weight.float(), None if bias is None else bias.float()) + return torch.gather(torch.log_softmax(logits, dim=-1), -1, target_ids.long().unsqueeze(-1)).squeeze(-1) + + +def _reset_rl_kernel_state(): + rlk_mod._LOGP_OP = None + rlk_mod._LOGP_OP_LOAD_ERROR = None + rlk_mod._LINEAR_LOGP_OP = None + rlk_mod._LINEAR_LOGP_OP_LOAD_ERROR = None + rlk_mod._WARNED_FALLBACK_REASONS.clear() + rlk_mod._FALLBACK_COUNTS.clear() + rlk_mod._FALLBACK_COUNTS.update({"logp": 0, "linear_logp": 0}) + _FakeLinearLogpOp.calls.clear() + + +def _install_fake_rl_engine(monkeypatch): + rl_engine = types.ModuleType("rl_engine") + kernels = types.ModuleType("rl_engine.kernels") + registry = types.ModuleType("rl_engine.kernels.registry") + registry.kernel_registry = types.SimpleNamespace(get_op=lambda name: _FakeLinearLogpOp()) + monkeypatch.setitem(sys.modules, "rl_engine", rl_engine) + monkeypatch.setitem(sys.modules, "rl_engine.kernels", kernels) + monkeypatch.setitem(sys.modules, "rl_engine.kernels.registry", registry) + + +def _install_fake_legacy_rl_engine(monkeypatch): + rl_engine = types.ModuleType("rl_engine") + kernels = types.ModuleType("rl_engine.kernels") + registry = types.ModuleType("rl_engine.kernels.registry") + registry.kernel_registry = types.SimpleNamespace(get_op=lambda name: _FakeLegacyLinearLogpOp()) + monkeypatch.setitem(sys.modules, "rl_engine", rl_engine) + monkeypatch.setitem(sys.modules, "rl_engine.kernels", kernels) + monkeypatch.setitem(sys.modules, "rl_engine.kernels.registry", registry) + + +def _make_args(**overrides) -> Namespace: + values = { + "enable_rl_kernel": True, + "rl_kernel_ops": ("linear_logp",), + "rl_kernel_strict": False, + "allgather_cp": False, + "qkv_format": "thd", + "rollout_temperature": 1.0, + "log_probs_chunk_size": -1, + "entropy_coef": 0.0, + "sequence_parallel": False, + "padded_vocab_size": None, + "vocab_size": None, + } + values.update(overrides) + return Namespace(**values) + + +@pytest.fixture(autouse=True) +def reset_parallelism(): + _reset_rl_kernel_state() + mpu.get_tensor_model_parallel_world_size.return_value = 1 + mpu.get_tensor_model_parallel_rank.return_value = 0 + mpu.get_tensor_model_parallel_group.return_value = None + mpu.get_context_parallel_world_size.return_value = 1 + mpu.get_context_parallel_rank.return_value = 0 + mpu.get_virtual_pipeline_model_parallel_world_size.return_value = None + mpu.is_pipeline_last_stage.return_value = True + yield + _reset_rl_kernel_state() + + +def _reference_logp(hidden: torch.Tensor, weight: torch.Tensor, target: torch.Tensor, bias: torch.Tensor | None): + logits = F.linear(hidden.float(), weight.float(), None if bias is None else bias.float()) + return torch.gather(torch.log_softmax(logits, dim=-1), -1, target.long().unsqueeze(-1)).squeeze(-1) + + +def _cpu_calculate_log_probs_and_entropy( + logits: torch.Tensor, + tokens: torch.Tensor, + tp_group, + *, + with_entropy: bool, + chunk_size: int, +): + del tp_group, chunk_size + log_probs = torch.log_softmax(logits.float(), dim=-1) + selected = torch.gather(log_probs, -1, tokens.long().unsqueeze(-1)).squeeze(-1) + entropy = None + if with_entropy: + probs = torch.softmax(logits.float(), dim=-1) + entropy = -(probs * log_probs).sum(dim=-1) + return selected, entropy + + +@pytest.mark.unit +def test_maybe_compute_linear_logp_passes_tensor_parallel_metadata(monkeypatch): + _install_fake_rl_engine(monkeypatch) + args = _make_args() + torch.manual_seed(1) + hidden = torch.randn(6, 5) + weight = torch.randn(8, 5) + bias = torch.randn(8) + target = torch.randint(0, 8, (6,)) + context = rlk_mod.LinearLogpContext( + lm_head_weight=weight, + bias=bias, + tp_group="tp", + vocab_start_index=16, + global_vocab_size=32, + ) + + actual = rlk_mod.maybe_compute_linear_logp(hidden, target, context=context, args=args, with_entropy=False) + + torch.testing.assert_close(actual, _reference_logp(hidden, weight, target, bias)) + assert _FakeLinearLogpOp.calls == [ + { + "hidden_shape": (6, 5), + "weight_shape": (8, 5), + "target_shape": (6,), + "bias": True, + "kwargs": { + "tp_group": "tp", + "vocab_start_index": 16, + "global_vocab_size": 32, + }, + } + ] + + +@pytest.mark.unit +def test_linear_logp_matches_vime_response_slicing_from_hidden_states(monkeypatch): + _install_fake_rl_engine(monkeypatch) + args = _make_args() + vocab_size = 23 + hidden_size = 7 + total_lengths = [5, 4, 6] + response_lengths = [2, 3, 4] + torch.manual_seed(2) + unconcat_tokens = [torch.randint(0, vocab_size, (length,), dtype=torch.long) for length in total_lengths] + hidden = torch.randn(sum(total_lengths), 1, hidden_size) + weight = torch.randn(vocab_size, hidden_size) + bias = torch.randn(vocab_size) + context = rlk_mod.LinearLogpContext(lm_head_weight=weight, bias=bias, tp_group=None) + + _, result = loss_mod.get_log_probs_and_entropy( + hidden, + args=args, + unconcat_tokens=unconcat_tokens, + total_lengths=total_lengths, + response_lengths=response_lengths, + with_entropy=False, + rl_kernel_linear_logp_context=context, + ) + + full_tokens = torch.zeros(sum(total_lengths), dtype=torch.long) + offset = 0 + for tokens, total_length in zip(unconcat_tokens, total_lengths, strict=False): + full_tokens[offset : offset + total_length - 1] = tokens[1:total_length] + offset += total_length + full_logp = _reference_logp(hidden.squeeze(1), weight, full_tokens, bias) + + expected = [] + offset = 0 + for total_length, response_length in zip(total_lengths, response_lengths, strict=False): + end = offset + total_length + start = end - response_length + expected.append(full_logp[start - 1 : end - 1]) + offset += total_length + + assert len(_FakeLinearLogpOp.calls) == 1 + for actual, expected_item in zip(result["log_probs"], expected, strict=True): + torch.testing.assert_close(actual, expected_item, rtol=1e-6, atol=1e-6) + + +@pytest.mark.unit +def test_linear_logp_materializes_logits_fallback_when_optional_package_missing(monkeypatch): + monkeypatch.setattr(loss_mod, "calculate_log_probs_and_entropy", _cpu_calculate_log_probs_and_entropy) + for name in list(sys.modules): + if name == "rl_engine" or name.startswith("rl_engine."): + monkeypatch.delitem(sys.modules, name, raising=False) + + original_import = builtins.__import__ + + def fail_rl_engine_import(name, *args, **kwargs): + if name == "rl_engine" or name.startswith("rl_engine."): + raise ModuleNotFoundError("No module named 'rl_engine'") + return original_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", fail_rl_engine_import) + args = _make_args() + vocab_size = 19 + hidden_size = 5 + total_lengths = [4, 5] + response_lengths = [2, 3] + torch.manual_seed(3) + unconcat_tokens = [torch.randint(0, vocab_size, (length,), dtype=torch.long) for length in total_lengths] + hidden = torch.randn(1, sum(total_lengths), hidden_size) + weight = torch.randn(vocab_size, hidden_size) + bias = torch.randn(vocab_size) + context = rlk_mod.LinearLogpContext(lm_head_weight=weight, bias=bias, tp_group=None) + + _, result = loss_mod.get_log_probs_and_entropy( + hidden, + args=args, + unconcat_tokens=unconcat_tokens, + total_lengths=total_lengths, + response_lengths=response_lengths, + with_entropy=False, + rl_kernel_linear_logp_context=context, + ) + + logits = F.linear(hidden.squeeze(0).float(), weight.float(), bias.float()) + expected = [] + offset = 0 + for tokens, total_length, response_length in zip(unconcat_tokens, total_lengths, response_lengths, strict=False): + end = offset + total_length + start = end - response_length + target = tokens[-response_length:] + expected.append( + torch.gather(torch.log_softmax(logits[start - 1 : end - 1], dim=-1), -1, target.unsqueeze(-1)).squeeze(-1) + ) + offset += total_length + + assert rlk_mod.get_rl_kernel_fallback_count("linear_logp") == 1 + for actual, expected_item in zip(result["log_probs"], expected, strict=True): + torch.testing.assert_close(actual, expected_item, rtol=1e-6, atol=1e-6) + + +@pytest.mark.unit +def test_linear_logp_falls_back_when_op_lacks_tp_interface(monkeypatch): + _install_fake_legacy_rl_engine(monkeypatch) + args = _make_args() + hidden = torch.randn(3, 4) + weight = torch.randn(6, 4) + target = torch.randint(0, 6, (3,)) + context = rlk_mod.LinearLogpContext( + lm_head_weight=weight, + bias=None, + tp_group="tp_group", + vocab_start_index=6, + global_vocab_size=12, + ) + + actual = rlk_mod.maybe_compute_linear_logp(hidden, target, context=context, args=args, with_entropy=False) + + assert actual is None + assert rlk_mod.get_rl_kernel_fallback_count("linear_logp") == 1 + + +@pytest.mark.unit +def test_linear_logp_context_from_model_uses_tp_vocab_offsets(): + mpu.get_tensor_model_parallel_world_size.return_value = 4 + mpu.get_tensor_model_parallel_rank.return_value = 2 + mpu.get_tensor_model_parallel_group.return_value = "tp_group" + + output_layer = types.SimpleNamespace( + weight=torch.empty(8, 4), + bias=None, + sequence_parallel=True, + ) + model = types.SimpleNamespace(output_layer=output_layer, post_process=True) + args = _make_args(padded_vocab_size=32, sequence_parallel=False) + + context = rlk_mod.get_linear_logp_context_from_model(args, model) + + assert context is not None + assert context.lm_head_weight is output_layer.weight + assert context.tp_group == "tp_group" + assert context.vocab_start_index == 16 + assert context.global_vocab_size == 32 + assert context.sequence_parallel is True + + +@pytest.mark.unit +def test_linear_logp_context_uses_covered_padded_vocab_when_padded_vocab_size_missing(): + mpu.get_tensor_model_parallel_world_size.return_value = 4 + mpu.get_tensor_model_parallel_rank.return_value = 1 + mpu.get_tensor_model_parallel_group.return_value = "tp_group" + + output_layer = types.SimpleNamespace(weight=torch.empty(8, 4), bias=None) + model = types.SimpleNamespace(output_layer=output_layer, post_process=True) + args = _make_args(padded_vocab_size=None, vocab_size=30) + + context = rlk_mod.get_linear_logp_context_from_model(args, model) + + assert context is not None + assert context.vocab_start_index == 8 + assert context.global_vocab_size == 32 + + +@pytest.mark.unit +def test_return_hidden_states_for_linear_logp_restores_post_process_flag(): + args = _make_args() + model = types.SimpleNamespace(post_process=True) + context = rlk_mod.LinearLogpContext( + lm_head_weight=torch.empty(4, 3), + bias=None, + tp_group=None, + ) + + with rlk_mod.return_hidden_states_for_linear_logp(args, model, context) as enabled: + assert enabled is True + assert model.post_process is False + + assert model.post_process is True + + +@pytest.mark.unit +def test_policy_loss_only_skips_entropy_when_linear_logp_context_is_active(): + args = _make_args(enable_rl_kernel=True, rl_kernel_ops=("linear_logp",), entropy_coef=0.0) + context = rlk_mod.LinearLogpContext( + lm_head_weight=torch.empty(4, 3), + bias=None, + tp_group=None, + ) + + assert loss_mod._policy_loss_needs_entropy(args, None) is True + assert loss_mod._policy_loss_needs_entropy(args, context) is False + + args.entropy_coef = 0.01 + assert loss_mod._policy_loss_needs_entropy(args, context) is True diff --git a/tests/test_rl_kernel_logp_integration.py b/tests/test_rl_kernel_logp_integration.py index 4941e01c..5d444768 100644 --- a/tests/test_rl_kernel_logp_integration.py +++ b/tests/test_rl_kernel_logp_integration.py @@ -37,7 +37,11 @@ def apply_fp32(self, logits: torch.Tensor, token_ids: torch.Tensor) -> torch.Ten def _reset_rl_kernel_state(): rlk_mod._LOGP_OP = None rlk_mod._LOGP_OP_LOAD_ERROR = None + rlk_mod._LINEAR_LOGP_OP = None + rlk_mod._LINEAR_LOGP_OP_LOAD_ERROR = None rlk_mod._WARNED_FALLBACK_REASONS.clear() + rlk_mod._FALLBACK_COUNTS.clear() + rlk_mod._FALLBACK_COUNTS.update({"logp": 0, "linear_logp": 0}) _FakeLogpOp.calls = 0 diff --git a/vime/backends/megatron_utils/loss.py b/vime/backends/megatron_utils/loss.py index 6a9cbf0d..e205a45f 100644 --- a/vime/backends/megatron_utils/loss.py +++ b/vime/backends/megatron_utils/loss.py @@ -29,7 +29,7 @@ get_sum_of_sample_mean, slice_log_prob_with_cp, ) -from .rl_kernel import maybe_compute_logp +from .rl_kernel import LinearLogpContext, get_rl_kernel_fallback_count, maybe_compute_linear_logp, maybe_compute_logp def get_responses( @@ -384,6 +384,65 @@ def _extract_per_sample( return log_probs_list, entropy_list +def _gather_sequence_parallel_hidden_if_needed( + hidden_states: torch.Tensor, + context: LinearLogpContext | None, +) -> torch.Tensor: + if context is None or not context.sequence_parallel: + return hidden_states + + from megatron.core import tensor_parallel + + return tensor_parallel.gather_from_sequence_parallel_region(hidden_states, tensor_parallel_output_grad=False) + + +def _flatten_logprob_model_output( + output_tensor: torch.Tensor, + *, + qkv_format: str, + max_seq_lens: list[int] | None, + linear_logp_context: LinearLogpContext | None, +) -> torch.Tensor: + if qkv_format == "thd": + assert len(output_tensor.shape) == 3, f"{output_tensor.shape}" + if output_tensor.size(0) == 1: + return output_tensor.squeeze(0) + if linear_logp_context is not None and output_tensor.size(1) == 1: + return output_tensor.squeeze(1) + assert output_tensor.size(0) == 1, f"{output_tensor.shape}" + else: + assert max_seq_lens is not None + return output_tensor.view(-1, output_tensor.size(-1)) + + raise AssertionError(f"Unsupported output tensor shape: {output_tensor.shape}") + + +def _materialize_linear_logits( + hidden_states: torch.Tensor, + *, + context: LinearLogpContext, + args: Namespace, +) -> torch.Tensor: + logits = F.linear(hidden_states, context.lm_head_weight, context.bias) + rollout_temperature = getattr(args, "rollout_temperature", 1.0) + if rollout_temperature != 1.0: + logits = logits / rollout_temperature + return logits.float() + + +def _policy_loss_needs_entropy( + args: Namespace, + rl_kernel_linear_logp_context: LinearLogpContext | None, +) -> bool: + if rl_kernel_linear_logp_context is None: + return True + return not ( + getattr(args, "enable_rl_kernel", False) + and "linear_logp" in getattr(args, "rl_kernel_ops", ()) + and getattr(args, "entropy_coef", 0.0) == 0 + ) + + def get_log_probs_and_entropy( logits: torch.Tensor, *, @@ -394,6 +453,7 @@ def get_log_probs_and_entropy( with_entropy: bool = False, non_loss_data: bool = True, max_seq_lens: list[int] | None = None, + rl_kernel_linear_logp_context: LinearLogpContext | None = None, ) -> dict[str, list[torch.Tensor]]: """Compute per-token log-probabilities (and optionally entropy) on responses. @@ -407,21 +467,28 @@ def get_log_probs_and_entropy( assert non_loss_data qkv_format = args.qkv_format - assert logits.dtype == torch.float32, f"{logits.dtype}" assert len(logits.shape) == 3, f"{logits.shape}" - if qkv_format == "thd": - assert logits.size(0) == 1, f"{logits.shape}" - logits = logits.squeeze(0) + linear_logp_context = rl_kernel_linear_logp_context + if linear_logp_context is not None: + logits = _gather_sequence_parallel_hidden_if_needed(logits, linear_logp_context) else: - assert max_seq_lens is not None - logits = logits.view(-1, logits.size(-1)) + assert logits.dtype == torch.float32, f"{logits.dtype}" + + logits = _flatten_logprob_model_output( + logits, + qkv_format=qkv_format, + max_seq_lens=max_seq_lens, + linear_logp_context=linear_logp_context, + ).contiguous() + + if linear_logp_context is None: + # Apply rollout temperature scaling to logits to match rollout-time log-probs. + rollout_temperature = getattr(args, "rollout_temperature", 1.0) + if rollout_temperature != 1.0: + logits = logits / rollout_temperature + logits = logits.contiguous() - # Apply rollout temperature scaling to logits to match rollout-time log-probs. - rollout_temperature = getattr(args, "rollout_temperature", 1.0) - if rollout_temperature != 1.0: - logits = logits / rollout_temperature - logits = logits.contiguous() T = logits.size(0) device = logits.device tp_group = mpu.get_tensor_model_parallel_group() @@ -433,7 +500,20 @@ def get_log_probs_and_entropy( ) # --- compute on full [T,V] logits at once --- - log_prob_full = maybe_compute_logp(logits, full_tokens, args=args, with_entropy=with_entropy) + log_prob_full = None + if linear_logp_context is not None: + log_prob_full = maybe_compute_linear_logp( + logits, + full_tokens, + context=linear_logp_context, + args=args, + with_entropy=with_entropy, + ) + if log_prob_full is None: + logits = _materialize_linear_logits(logits, context=linear_logp_context, args=args).contiguous() + else: + log_prob_full = maybe_compute_logp(logits, full_tokens, args=args, with_entropy=with_entropy) + if log_prob_full is None: log_prob_full, entropy_full = calculate_log_probs_and_entropy( logits, @@ -807,6 +887,7 @@ def policy_loss_function( batch: RolloutBatch, logits: torch.Tensor, sum_of_sample_mean: Callable[[torch.Tensor], torch.Tensor], + rl_kernel_linear_logp_context: LinearLogpContext | None = None, ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: """Compute policy loss (PPO/GSPO) and metrics. @@ -839,14 +920,17 @@ def policy_loss_function( total_lengths = batch["total_lengths"] max_seq_lens = batch.get("max_seq_lens", None) + need_entropy = _policy_loss_needs_entropy(args, rl_kernel_linear_logp_context) + _, log_probs_and_entropy = get_log_probs_and_entropy( logits, args=args, unconcat_tokens=batch["unconcat_tokens"], total_lengths=total_lengths, response_lengths=response_lengths, - with_entropy=True, + with_entropy=need_entropy, max_seq_lens=max_seq_lens, + rl_kernel_linear_logp_context=rl_kernel_linear_logp_context, ) log_probs = log_probs_and_entropy["log_probs"] @@ -968,9 +1052,12 @@ def policy_loss_function( ppo_kl = sum_of_sample_mean(ppo_kl) # entropy loss - entropy = log_probs_and_entropy["entropy"] - entropy = torch.cat(entropy, dim=0) - entropy_loss = sum_of_sample_mean(entropy) + if need_entropy: + entropy = log_probs_and_entropy["entropy"] + entropy = torch.cat(entropy, dim=0) + entropy_loss = sum_of_sample_mean(entropy) + else: + entropy_loss = log_probs.new_zeros(()) loss = pg_loss - args.entropy_coef * entropy_loss @@ -1038,6 +1125,7 @@ def value_loss_function( batch: RolloutBatch, logits: torch.Tensor, sum_of_sample_mean: Callable[[torch.Tensor], torch.Tensor], + rl_kernel_linear_logp_context: LinearLogpContext | None = None, ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: """Compute clipped value loss and metrics. @@ -1056,6 +1144,7 @@ def value_loss_function( Tuple of `(loss, metrics)` where `loss` is a scalar tensor and `metrics` contains detached scalars "value_loss" and "value_clipfrac". """ + del rl_kernel_linear_logp_context old_values = torch.cat(batch["values"], dim=0) _, values = get_values( @@ -1096,6 +1185,7 @@ def sft_loss_function( batch: RolloutBatch, logits: torch.Tensor, sum_of_sample_mean: Callable[[torch.Tensor], torch.Tensor], + rl_kernel_linear_logp_context: LinearLogpContext | None = None, ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]: """Compute supervised fine-tuning loss over response tokens. @@ -1124,6 +1214,7 @@ def sft_loss_function( response_lengths=response_lengths, with_entropy=False, max_seq_lens=batch.get("max_seq_lens", None), + rl_kernel_linear_logp_context=rl_kernel_linear_logp_context, ) log_probs = log_probs_and_entropy["log_probs"] @@ -1148,6 +1239,7 @@ def loss_function( num_microbatches: int, step_global_batch_size: int, logits: torch.Tensor, + rl_kernel_linear_logp_context: LinearLogpContext | None = None, ) -> tuple[torch.Tensor, int | torch.Tensor, dict[str, list[str] | torch.Tensor]]: """Dispatch to the configured loss and rescale for Megatron integration. @@ -1200,10 +1292,22 @@ def loss_function( case _: raise ValueError(f"Unknown loss type: {args.loss_type}") + if func in {policy_loss_function, value_loss_function, sft_loss_function}: + func_args = (args, batch, logits, sum_of_sample_mean, rl_kernel_linear_logp_context) + else: + func_args = (args, batch, logits, sum_of_sample_mean) + if args.recompute_loss_function: - loss, log = checkpoint(func, args, batch, logits, sum_of_sample_mean, use_reentrant=False) + loss, log = checkpoint(func, *func_args, use_reentrant=False) else: - loss, log = func(args, batch, logits, sum_of_sample_mean) + loss, log = func(*func_args) + + if getattr(args, "enable_rl_kernel", False): + log["rl_kernel_fallback_count"] = torch.tensor( + get_rl_kernel_fallback_count(), + device=logits.device, + dtype=torch.float32, + ) # With allgather-CP, some CP ranks may have no loss-contributing tokens (e.g., all # padding). Without this, gradient doesn't flow through their attention path, so diff --git a/vime/backends/megatron_utils/model.py b/vime/backends/megatron_utils/model.py index 6b602fc1..14f80f53 100644 --- a/vime/backends/megatron_utils/model.py +++ b/vime/backends/megatron_utils/model.py @@ -27,14 +27,22 @@ from megatron.core.pipeline_parallel.utils import unwrap_model except ImportError: from megatron.core.utils import unwrap_model + from vime.utils import logging_utils from vime.utils.memory_utils import clear_memory +from vime.utils.rl_kernel import is_rl_kernel_op_enabled from .checkpoint import load_checkpoint, save_checkpoint from .cp_utils import reduce_train_step_metrics from .data import DataIterator, get_batch -from .loss import loss_function +from .loss import get_log_probs_and_entropy, loss_function from .model_provider import get_model_provider_func +from .rl_kernel import ( + get_linear_logp_context_from_model, + return_hidden_states_for_linear_logp, + should_use_linear_logp_model_output, + warn_linear_logp_fallback, +) logger = logging.getLogger(__name__) @@ -73,6 +81,35 @@ def wrapped_forward_step(*args, **kwargs): return wrapped_forward_step +def _forward_only_should_return_hidden_for_linear_logp( + f: Callable[..., dict[str, list[torch.Tensor]]], + args: Namespace, +) -> bool: + return f is get_log_probs_and_entropy and should_use_linear_logp_model_output( + args, + with_entropy=args.use_rollout_entropy, + ) + + +def _train_should_return_hidden_for_linear_logp(args: Namespace, *, return_schedule_plan: bool) -> bool: + if not is_rl_kernel_op_enabled(args, "linear_logp"): + return False + + if args.loss_type not in {"policy_loss", "sft_loss"}: + return False + + if return_schedule_plan: + warn_linear_logp_fallback(args, "schedule-plan forward path is not supported") + return False + + if getattr(args, "enable_mtp_training", False): + warn_linear_logp_fallback(args, "MTP training path is not supported") + return False + + with_entropy = args.loss_type == "policy_loss" and getattr(args, "entropy_coef", 0.0) != 0 + return should_use_linear_logp_model_output(args, with_entropy=with_entropy) + + def _iter_critic_output_layers(model: Sequence[DDP]): for chunk_id, module in enumerate(unwrap_model(model)): output_layer = getattr(module, "output_layer", None) @@ -342,17 +379,25 @@ def forward_step( } if batch["multimodal_train_inputs"] is not None: forward_kwargs.update(batch["multimodal_train_inputs"]) - output_tensor = model(**forward_kwargs) + linear_logp_context = None + if _forward_only_should_return_hidden_for_linear_logp(f, args): + linear_logp_context = get_linear_logp_context_from_model(args, model) - return output_tensor, partial( - f, - args=args, - unconcat_tokens=unconcat_tokens, - total_lengths=total_lengths, - response_lengths=response_lengths, - with_entropy=args.use_rollout_entropy, - max_seq_lens=batch.get("max_seq_lens", None), - ) + with return_hidden_states_for_linear_logp(args, model, linear_logp_context): + output_tensor = model(**forward_kwargs) + + callback_kwargs = { + "args": args, + "unconcat_tokens": unconcat_tokens, + "total_lengths": total_lengths, + "response_lengths": response_lengths, + "with_entropy": args.use_rollout_entropy, + "max_seq_lens": batch.get("max_seq_lens", None), + } + if f is get_log_probs_and_entropy: + callback_kwargs["rl_kernel_linear_logp_context"] = linear_logp_context + + return output_tensor, partial(f, **callback_kwargs) # Turn on evaluation mode which disables dropout. for model_module in model: @@ -512,6 +557,10 @@ def forward_step(data_iterator: DataIterator, model: GPTModel, return_schedule_p old_stage = os.environ["ROUTING_REPLAY_STAGE"] os.environ["ROUTING_REPLAY_STAGE"] = "replay_forward" + linear_logp_context = None + if _train_should_return_hidden_for_linear_logp(args, return_schedule_plan=return_schedule_plan): + linear_logp_context = get_linear_logp_context_from_model(args, model) + if return_schedule_plan: assert not args.enable_mtp_training, "MTP training should not be enabled when using combined 1f1b" position_ids = None @@ -539,12 +588,20 @@ def forward_step(data_iterator: DataIterator, model: GPTModel, return_schedule_p if args.enable_mtp_training: forward_kwargs["mtp_kwargs"] = {"mtp_labels": batch["tokens"]} - output_tensor = model(**forward_kwargs) + with return_hidden_states_for_linear_logp(args, model, linear_logp_context): + output_tensor = model(**forward_kwargs) if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1": os.environ["ROUTING_REPLAY_STAGE"] = old_stage - return output_tensor, partial(loss_function, args, batch, num_microbatches, step_global_batch_size) + return output_tensor, partial( + loss_function, + args, + batch, + num_microbatches, + step_global_batch_size, + rl_kernel_linear_logp_context=linear_logp_context, + ) # Forward pass. forward_backward_func = get_forward_backward_func() diff --git a/vime/backends/megatron_utils/rl_kernel.py b/vime/backends/megatron_utils/rl_kernel.py index 7f712b32..aca8706d 100644 --- a/vime/backends/megatron_utils/rl_kernel.py +++ b/vime/backends/megatron_utils/rl_kernel.py @@ -2,6 +2,9 @@ import logging from argparse import Namespace +from contextlib import contextmanager +from dataclasses import dataclass +from typing import Any import torch from megatron.core import mpu @@ -12,15 +15,36 @@ _LOGP_OP = None _LOGP_OP_LOAD_ERROR: Exception | None = None +_LINEAR_LOGP_OP = None +_LINEAR_LOGP_OP_LOAD_ERROR: Exception | None = None _WARNED_FALLBACK_REASONS: set[str] = set() +_FALLBACK_COUNTS: dict[str, int] = {"logp": 0, "linear_logp": 0} -def _warn_fallback(args: Namespace, reason: str) -> None: +@dataclass(frozen=True) +class LinearLogpContext: + lm_head_weight: torch.Tensor + bias: torch.Tensor | None + tp_group: Any + vocab_start_index: int = 0 + global_vocab_size: int | None = None + sequence_parallel: bool = False + + +def get_rl_kernel_fallback_count(op: str | None = None) -> int: + if op is not None: + return _FALLBACK_COUNTS.get(op, 0) + return sum(_FALLBACK_COUNTS.values()) + + +def _warn_fallback(args: Namespace, op: str, reason: str) -> None: + _FALLBACK_COUNTS[op] = _FALLBACK_COUNTS.get(op, 0) + 1 if getattr(args, "rl_kernel_strict", False): - raise RuntimeError(f"RL-Kernel logp is enabled but unavailable: {reason}") - if reason not in _WARNED_FALLBACK_REASONS: - logger.warning("Falling back to vime logprob path because RL-Kernel logp is unavailable: %s", reason) - _WARNED_FALLBACK_REASONS.add(reason) + raise RuntimeError(f"RL-Kernel {op} is enabled but unavailable: {reason}") + warning_key = f"{op}: {reason}" + if warning_key not in _WARNED_FALLBACK_REASONS: + logger.warning("Falling back to vime logprob path because RL-Kernel %s is unavailable: %s", op, reason) + _WARNED_FALLBACK_REASONS.add(warning_key) def _get_logp_op(args: Namespace): @@ -28,7 +52,7 @@ def _get_logp_op(args: Namespace): if _LOGP_OP is not None: return _LOGP_OP if _LOGP_OP_LOAD_ERROR is not None: - _warn_fallback(args, str(_LOGP_OP_LOAD_ERROR)) + _warn_fallback(args, "logp", str(_LOGP_OP_LOAD_ERROR)) return None try: @@ -39,10 +63,155 @@ def _get_logp_op(args: Namespace): return _LOGP_OP except Exception as exc: # pragma: no cover - exercised with missing optional package in integration envs _LOGP_OP_LOAD_ERROR = exc - _warn_fallback(args, str(exc)) + _warn_fallback(args, "logp", str(exc)) + return None + + +def _get_linear_logp_op(args: Namespace): + global _LINEAR_LOGP_OP, _LINEAR_LOGP_OP_LOAD_ERROR + if _LINEAR_LOGP_OP is not None: + return _LINEAR_LOGP_OP + if _LINEAR_LOGP_OP_LOAD_ERROR is not None: + _warn_fallback(args, "linear_logp", str(_LINEAR_LOGP_OP_LOAD_ERROR)) + return None + + try: + from rl_engine.kernels.registry import kernel_registry + + _LINEAR_LOGP_OP = kernel_registry.get_op("linear_logp") + logger.info("Using RL-Kernel linear_logp op: %s", type(_LINEAR_LOGP_OP).__name__) + return _LINEAR_LOGP_OP + except Exception as exc: # pragma: no cover - exercised with missing optional package in integration envs + _LINEAR_LOGP_OP_LOAD_ERROR = exc + _warn_fallback(args, "linear_logp", str(exc)) return None +def _unwrap_model_chunk(model): + while hasattr(model, "module"): + model = model.module + return model + + +def _is_pipeline_last_stage_for_model(model) -> bool: + module = _unwrap_model_chunk(model) + vp_stage = getattr(module, "vp_stage", None) + try: + vp_world_size = mpu.get_virtual_pipeline_model_parallel_world_size() + except Exception: + vp_world_size = None + + try: + if vp_world_size is not None and vp_stage is not None: + return bool(mpu.is_pipeline_last_stage(ignore_virtual=False, vp_stage=vp_stage)) + return bool(mpu.is_pipeline_last_stage(ignore_virtual=True)) + except Exception: + return True + + +def _get_lm_head_weight(model, output_layer) -> torch.Tensor | None: + shared_weight = getattr(model, "shared_embedding_or_output_weight", None) + if callable(shared_weight): + try: + weight = shared_weight() + if isinstance(weight, torch.Tensor): + return weight + except Exception: + logger.debug("Unable to read shared embedding/output weight for RL-Kernel linear_logp.", exc_info=True) + + weight = getattr(output_layer, "weight", None) + return weight if isinstance(weight, torch.Tensor) else None + + +def get_linear_logp_context_from_model(args: Namespace, model) -> LinearLogpContext | None: + if not is_rl_kernel_op_enabled(args, "linear_logp"): + return None + + if not _is_pipeline_last_stage_for_model(model): + return None + + module = _unwrap_model_chunk(model) + output_layer = getattr(module, "output_layer", None) + if output_layer is None: + _warn_fallback(args, "linear_logp", "model output_layer is unavailable") + return None + + weight = _get_lm_head_weight(module, output_layer) + if weight is None: + _warn_fallback(args, "linear_logp", "LM-head weight is unavailable") + return None + + bias = getattr(output_layer, "bias", None) + if not isinstance(bias, torch.Tensor): + bias = None + + tp_world_size = int(mpu.get_tensor_model_parallel_world_size()) + tp_group = mpu.get_tensor_model_parallel_group() if tp_world_size > 1 else None + vocab_start_index = 0 + global_vocab_size = None + if tp_world_size > 1: + local_vocab_size = int(weight.size(0)) + vocab_start_index = int(mpu.get_tensor_model_parallel_rank()) * local_vocab_size + global_vocab_size = getattr(args, "padded_vocab_size", None) + if global_vocab_size is None: + global_vocab_size = local_vocab_size * tp_world_size + + return LinearLogpContext( + lm_head_weight=weight, + bias=bias, + tp_group=tp_group, + vocab_start_index=vocab_start_index, + global_vocab_size=None if global_vocab_size is None else int(global_vocab_size), + sequence_parallel=bool(getattr(output_layer, "sequence_parallel", getattr(args, "sequence_parallel", False))), + ) + + +def _linear_logp_runtime_blocker(args: Namespace, *, with_entropy: bool) -> str | None: + if with_entropy: + return "entropy is requested" + if getattr(args, "qkv_format", "thd") != "thd": + return "only qkv_format=thd is supported by RL-Kernel linear_logp" + if mpu.get_context_parallel_world_size() != 1 or getattr(args, "allgather_cp", False): + return "context parallel logprob redistribution is not supported by RL-Kernel linear_logp" + if getattr(args, "rollout_temperature", 1.0) <= 0: + return "rollout_temperature must be positive" + return None + + +def should_use_linear_logp_model_output(args: Namespace, *, with_entropy: bool) -> bool: + if not is_rl_kernel_op_enabled(args, "linear_logp"): + return False + reason = _linear_logp_runtime_blocker(args, with_entropy=with_entropy) + if reason is not None: + _warn_fallback(args, "linear_logp", reason) + return False + return True + + +def warn_linear_logp_fallback(args: Namespace, reason: str) -> None: + _warn_fallback(args, "linear_logp", reason) + + +@contextmanager +def return_hidden_states_for_linear_logp(args: Namespace, model, context: LinearLogpContext | None): + if context is None: + yield False + return + + module = _unwrap_model_chunk(model) + if not hasattr(module, "post_process"): + _warn_fallback(args, "linear_logp", "model post_process flag is unavailable") + yield False + return + + old_post_process = module.post_process + module.post_process = False + try: + yield True + finally: + module.post_process = old_post_process + + def maybe_compute_logp( logits: torch.Tensor, tokens: torch.Tensor, @@ -61,19 +230,19 @@ def maybe_compute_logp( return None if with_entropy: - _warn_fallback(args, "entropy is requested") + _warn_fallback(args, "logp", "entropy is requested") return None if logits.requires_grad or torch.is_grad_enabled(): - _warn_fallback(args, "autograd is enabled") + _warn_fallback(args, "logp", "autograd is enabled") return None if mpu.get_tensor_model_parallel_world_size() != 1: - _warn_fallback(args, "tensor-parallel vocab shards are not supported by RL-Kernel logp") + _warn_fallback(args, "logp", "tensor-parallel vocab shards are not supported by RL-Kernel logp") return None if mpu.get_context_parallel_world_size() != 1 or getattr(args, "allgather_cp", False): - _warn_fallback(args, "context parallel logprob redistribution is not supported by RL-Kernel logp") + _warn_fallback(args, "logp", "context parallel logprob redistribution is not supported by RL-Kernel logp") return None if logits.size(0) == 0: @@ -89,7 +258,59 @@ def maybe_compute_logp( else: log_prob = op(logits, tokens).float() except Exception as exc: - _warn_fallback(args, str(exc)) + _warn_fallback(args, "logp", str(exc)) return None return log_prob.reshape(-1) + + +def maybe_compute_linear_logp( + hidden_states: torch.Tensor, + target_ids: torch.Tensor, + *, + context: LinearLogpContext | None, + args: Namespace, + with_entropy: bool, +) -> torch.Tensor | None: + if not is_rl_kernel_op_enabled(args, "linear_logp"): + return None + + reason = _linear_logp_runtime_blocker(args, with_entropy=with_entropy) + if reason is not None: + _warn_fallback(args, "linear_logp", reason) + return None + + if context is None: + _warn_fallback(args, "linear_logp", "hidden-state linear_logp context is unavailable") + return None + + if target_ids.numel() == 0: + return hidden_states.new_zeros((0,), dtype=torch.float32) + + op = _get_linear_logp_op(args) + if op is None: + return None + + weight = context.lm_head_weight + bias = context.bias + rollout_temperature = float(getattr(args, "rollout_temperature", 1.0)) + if rollout_temperature != 1.0: + weight = weight / rollout_temperature + if bias is not None: + bias = bias / rollout_temperature + + try: + log_prob = op( + hidden_states, + weight, + target_ids.long(), + bias, + tp_group=context.tp_group, + vocab_start_index=context.vocab_start_index, + global_vocab_size=context.global_vocab_size, + ) + except Exception as exc: + _warn_fallback(args, "linear_logp", str(exc)) + return None + + return log_prob.float().reshape(-1) diff --git a/vime/utils/arguments.py b/vime/utils/arguments.py index 34ffa666..a186a756 100644 --- a/vime/utils/arguments.py +++ b/vime/utils/arguments.py @@ -167,8 +167,8 @@ def add_train_arguments(parser): parser.add_argument( "--rl-kernel-ops", type=str, - default="logp", - help="Comma-separated RL-Kernel ops to enable. Current production integration supports: logp.", + default="linear_logp", + help="Comma-separated RL-Kernel ops to enable. Current production integration supports: linear_logp.", ) parser.add_argument( "--rl-kernel-strict", diff --git a/vime/utils/rl_kernel.py b/vime/utils/rl_kernel.py index c01e02db..e0e4a0b7 100644 --- a/vime/utils/rl_kernel.py +++ b/vime/utils/rl_kernel.py @@ -3,8 +3,8 @@ from collections.abc import Iterable -RL_KERNEL_SUPPORTED_OPS = ("logp", "ratio_kl", "grpo_loss") -RL_KERNEL_INTEGRATED_OPS = ("logp",) +RL_KERNEL_SUPPORTED_OPS = ("linear_logp",) +RL_KERNEL_INTEGRATED_OPS = ("linear_logp",) _TRUE_VALUES = {"1", "true", "yes", "on"} _FALSE_VALUES = {"0", "false", "no", "off"} @@ -12,7 +12,7 @@ def parse_rl_kernel_ops(value: str | Iterable[str] | None) -> tuple[str, ...]: """Parse a comma/space separated RL-Kernel op list.""" if value is None: - return ("logp",) + return ("linear_logp",) if isinstance(value, str): raw_items = value.replace(",", " ").split() @@ -32,7 +32,7 @@ def parse_rl_kernel_ops(value: str | Iterable[str] | None) -> tuple[str, ...]: if op not in ops: ops.append(op) - return tuple(ops) if ops else ("logp",) + return tuple(ops) if ops else ("linear_logp",) def _env_bool(name: str) -> bool | None: From e32bd5e3e980e26b84135c9151391e6dcfd766c6 Mon Sep 17 00:00:00 2001 From: inaniloquentee <3051000145@qq.com> Date: Sat, 27 Jun 2026 16:28:45 +0800 Subject: [PATCH 4/5] Add RL-Kernel vime benchmark runbook Signed-off-by: inaniloquentee <3051000145@qq.com> --- vime-RLK.md | 312 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 312 insertions(+) create mode 100644 vime-RLK.md diff --git a/vime-RLK.md b/vime-RLK.md new file mode 100644 index 00000000..42cf234d --- /dev/null +++ b/vime-RLK.md @@ -0,0 +1,312 @@ +# vime + RL-Kernel linear_logp 主宣传实验 + +## 0. 我们要做什么 + +本轮只保留一个主宣传实验： + +```text +baseline: vime benchmark branch, Qwen3-30B-A3B, 8xH100 colocate, RL-Kernel off +candidate: vime benchmark branch + RL-Kernel linear_logp, Qwen3-30B-A3B, 8xH100 colocate +``` + +目标：证明 RL-Kernel 的 `linear_logp` 接入 vime 后，在同一套 Qwen3-30B-A3B MoE 训练配置下，不降低训练质量，并降低 selected-logprob 路径耗时或显存压力。 + +范围收口： + +- 只测 `linear_logp`。 +- 只跑 Qwen3-30B-A3B 主宣传实验。 +- 不跑 Qwen3-4B smoke、R3 单独对比、GLM-4.5、GB200/H200 硬件对照。 +- 不测 `logp`、`ratio_kl`、`grpo_loss`、`sampling` 的 vime 端到端收益。 +- 不做训推一致性专项 benchmark。 +- 不接 MoE expert/router 算子。 + +## 1. H100 支持结论 + +vime 支持 H100：当前 vime 文档已有 `Qwen3-30B-A3B with 8xH100` 和 `Qwen3-4B with 8xH100` 示例，代码里也有 H100 hardware mapping。 + +所以本轮主方案使用： + +```text +8xH100 +``` + +A100 不作为本轮主宣传配置。 + +## 2. 当前代码边界 + +vime candidate 只暴露一个 RL-Kernel op： + +```text +RL_KERNEL_SUPPORTED_OPS = ("linear_logp",) +RL_KERNEL_INTEGRATED_OPS = ("linear_logp",) +--rl-kernel-ops linear_logp +VIME_RL_KERNEL_OPS=linear_logp +``` + +主实验脚本： + +```text +scripts/run-qwen3-30B-A3B.sh +``` + +该脚本已经按 8 卡主宣传实验参数化： + +```text +NUM_GPUS=8 +MEGATRON_TP=4 +MEGATRON_EP=8 +MEGATRON_CP=1 +ROLLOUT_NUM_GPUS_PER_ENGINE=8 +ROLLOUT_BATCH_SIZE=32 +N_SAMPLES_PER_PROMPT=8 +GLOBAL_BATCH_SIZE=256 +MAX_TOKENS_PER_GPU=20480 +VLLM_GPU_MEMORY_UTILIZATION=0.7 +``` + +如遇 OOM，先降低： + +```text +MAX_TOKENS_PER_GPU=8192 +VLLM_GPU_MEMORY_UTILIZATION=0.55 +ROLLOUT_BATCH_SIZE=4 +GLOBAL_BATCH_SIZE=32 +``` + +## 3. 上卡准备 + +从官方仓库开始，不依赖当前本地目录： + +```bash +cd /workspace +git clone https://github.com/RL-Align/vime.git vime-main +git clone https://github.com/RL-Align/vime.git vime-benchmark +git clone https://github.com/RL-Align/vime.git vime-rlk-integration +git clone https://github.com/RL-Align/RL-Kernel.git RL-Kernel +``` + +RL-Kernel 必须使用含 TP 版 `linear_logp` 接口的版本。vime 这边会调用： + +```text +op(hidden, weight, target_ids, bias, tp_group=..., vocab_start_index=..., global_vocab_size=...) +``` + +当前使用 `RL-Align/RL-Kernel#189` 提供 TP 版 `linear_logp`。上卡后在 `/workspace/RL-Kernel` 里 checkout 该 PR 后再安装： + +```bash +cd /workspace/RL-Kernel +git checkout main +git pull origin main +gh pr checkout 189 +``` + +`vime-main` 只作为干净参考，不直接跑实验： + +```bash +cd /workspace/vime-main +git checkout main +git pull origin main +``` + +vime candidate 已经准备成 draft PR，baseline 仍然要保持 benchmark-only，避免把 baseline 和 candidate 混在一起： + +```text +vime-rlk-benchmark-8h100 +只包含 8xH100 benchmark harness，不包含 RL-Kernel 集成代码。 + +RL-Align/vime#1 +draft PR，基于 benchmark harness，再加入 RL-Kernel linear_logp 集成代码和测试。 +``` + +baseline 从干净 main 新建 benchmark 分支： + +```bash +cd /workspace/vime-benchmark +git checkout main +git pull origin main +git checkout -b vime-rlk-benchmark-8h100 +# 只应用 benchmark harness 改动，例如 scripts/run-qwen3-30B-A3B.sh 的 8xH100 参数化。 +# 不加入 --enable-rl-kernel、vime/utils/rl_kernel.py、megatron_utils/rl_kernel.py 等 RL-Kernel 集成改动。 +``` + +candidate 直接 checkout draft PR `RL-Align/vime#1`： + +```bash +cd /workspace/vime-rlk-integration +git checkout main +git pull origin main +gh pr checkout 1 +``` + +安装： + +```bash +cd /workspace/RL-Kernel +pip install -e . +python setup.py build_ext --inplace -v + +cd /workspace/vime-benchmark +pip install -e . + +cd /workspace/vime-rlk-integration +pip install -e . +``` + +下载模型和数据： + +```bash +pip install -U "huggingface_hub[cli]" +huggingface-cli login + +hf download Qwen/Qwen3-30B-A3B --local-dir /root/Qwen3-30B-A3B + +hf download --repo-type dataset zhuzilin/dapo-math-17k \ + --local-dir /root/dapo-math-17k + +hf download --repo-type dataset zhuzilin/aime-2024 \ + --local-dir /root/aime-2024 +``` + +转换 Megatron `torch_dist` checkpoint： + +```bash +cd /workspace/vime-benchmark +source scripts/models/qwen3-30B-A3B.sh + +PYTHONPATH=/root/Megatron-LM torchrun --nproc-per-node 8 \ + tools/convert_hf_to_torch_dist.py \ + ${MODEL_ARGS[@]} \ + --hf-checkpoint /root/Qwen3-30B-A3B \ + --save /root/Qwen3-30B-A3B_torch_dist +``` + +## 4. 运行主宣传实验 + +两边使用同一套 8 卡 colocate 环境变量： + +```bash +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +export NUM_GPUS=8 +export MEGATRON_TP=4 +export MEGATRON_EP=8 +export MEGATRON_CP=1 +export ROLLOUT_NUM_GPUS_PER_ENGINE=8 +export ROLLOUT_BATCH_SIZE=32 +export N_SAMPLES_PER_PROMPT=8 +export GLOBAL_BATCH_SIZE=256 +export MAX_TOKENS_PER_GPU=20480 +export VLLM_GPU_MEMORY_UTILIZATION=0.7 +``` + +baseline： + +```bash +cd /workspace/vime-benchmark +unset VIME_RL_KERNEL VIME_RL_KERNEL_OPS VIME_RL_KERNEL_STRICT +bash scripts/run-qwen3-30B-A3B.sh +``` + +candidate： + +```bash +cd /workspace/vime-rlk-integration +export VIME_RL_KERNEL=1 +export VIME_RL_KERNEL_OPS=linear_logp +export VIME_RL_KERNEL_STRICT=1 +bash scripts/run-qwen3-30B-A3B.sh +``` + +每组至少跑 3 次；每次丢弃前 5-10 step warmup 后统计。 + +## 5. 必须记录 + +每个 run 保存： + +```text +hardware +gpu_name +num_gpus +model +dataset +vime_commit +rl_kernel_commit +candidate_enabled +enabled_rl_kernel_ops +selected_rl_kernel_backend +tp +ep +cp +rollout_batch_size +n_samples_per_prompt +global_batch_size +max_tokens_per_gpu +mean_step_time_s +p50_step_time_s +p90_step_time_s +mean_log_probs_time_s +p50_log_probs_time_s +p90_log_probs_time_s +peak_vram_gb +raw_reward_mean +train_rollout_logprob_abs_diff_mean +rl_kernel_fallback_count +``` + +验收线： + +```text +candidate 日志出现 RL-Kernel linear_logp backend +rl_kernel_fallback_count = 0 +candidate raw_reward 不低于 baseline 同量级 +candidate train_rollout_logprob_abs_diff 不持续高于 baseline +candidate mean_log_probs_time_s 或 peak_vram_gb 有可解释下降 +``` + +## 6. 最终图表 + +只输出主宣传图： + +1. `Qwen3-30B-A3B 8xH100 raw_reward` +2. `Qwen3-30B-A3B 8xH100 train_rollout_logprob_abs_diff` +3. `Qwen3-30B-A3B 8xH100 Step Time` +4. `Qwen3-30B-A3B 8xH100 Logprob Time / Peak VRAM` + +图表风格对齐 `vime_blog.md`：白底、虚线网格、baseline 蓝色、candidate 红色。 + +## 7. 本地验证 + +当前无 GPU 环境已完成： + +```text +# linear_logp 主路径与公共工具 +pytest tests/test_rl_kernel_args.py tests/test_rl_kernel_linear_logp_integration.py tests/test_value_temperature.py tests/test_metric_report.py -q +结果：39 passed + +# legacy logp compatibility regression，不属于本轮 benchmark 范围 +pytest tests/test_rl_kernel_logp_integration.py tests/test_rl_kernel_args.py tests/test_rl_kernel_linear_logp_integration.py -q +结果：24 passed + +pre-commit run --files <本轮 vime 相关文件> +结果：Passed +``` + +上卡后必须补跑： + +```text +8xH100 baseline: /workspace/vime-benchmark, benchmark-only branch +8xH100 candidate: /workspace/vime-rlk-integration, RL-Align/vime#1 +``` + +## 8. 宣传口径 + +英文： + +```text +RL-Kernel integrates with vime to accelerate the Qwen3-30B-A3B GRPO selected-logprob path through linear_logp. On the same 8xH100 setup, it reduces logprob-path cost while keeping reward and train-rollout logprob alignment stable. +``` + +中文： + +```text +RL-Kernel 接入 vime 后，通过 linear_logp 加速 Qwen3-30B-A3B GRPO selected-logprob 路径。在相同 8xH100 配置下，RL-Kernel 降低 logprob 路径开销，同时保持 reward 和 train-rollout logprob alignment 稳定。 +``` From ee3fa1e49e3ccb2ab1502f5ef8349af6a947516d Mon Sep 17 00:00:00 2001 From: Takumi <3051000145@qq.com> Date: Sat, 27 Jun 2026 12:39:13 +0100 Subject: [PATCH 5/5] Add TP8 RL-Kernel linear_logp experiment results --- .../.gitignore | 1 + .../RESULTS.md | 262 ++++++++++++++++++ .../charts/logprob_alignment.png | Bin 0 -> 51951 bytes .../charts/logprob_time_peak_vram.png | Bin 0 -> 73593 bytes .../charts/performance_breakdown.png | Bin 0 -> 45013 bytes .../charts/raw_reward.png | Bin 0 -> 36571 bytes .../charts/step_time.png | Bin 0 -> 38772 bytes .../data/metrics.csv | 3 + .../run_one.sh | 105 +++++++ scripts/run-qwen3-30B-A3B.sh | 42 ++- .../test_rl_kernel_linear_logp_integration.py | 45 +++ vime-RLK.md | 4 +- vime/backends/megatron_utils/rl_kernel.py | 7 +- vime/ray/actor_group.py | 1 + 14 files changed, 460 insertions(+), 10 deletions(-) create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/.gitignore create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/RESULTS.md create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/logprob_alignment.png create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/logprob_time_peak_vram.png create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/performance_breakdown.png create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/raw_reward.png create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/step_time.png create mode 100644 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/data/metrics.csv create mode 100755 experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/run_one.sh diff --git a/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/.gitignore b/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/.gitignore new file mode 100644 index 00000000..483fb84a --- /dev/null +++ b/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/.gitignore @@ -0,0 +1 @@ +/runs/ diff --git a/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/RESULTS.md b/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/RESULTS.md new file mode 100644 index 00000000..f2f839dc --- /dev/null +++ b/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/RESULTS.md @@ -0,0 +1,262 @@ +# RL-Kernel linear_logp Qwen3-30B-A3B 8xH100 实验记录 + +更新时间: 2026-06-27 11:35 UTC + +## 结论摘要 + +本轮只记录成功完成的 TP=8 smoke validation，不记录失败 run。baseline 使用 benchmark-only vime，candidate 使用当前 PR 分支并只启用 `RL-Kernel linear_logp`。 + +在相同 Qwen3-30B-A3B、8xH100、TP=8/EP=8/CP=1、单 prompt/单 sample/单 step smoke 配置下： + +| 项目 | baseline | candidate | 变化 | +| --- | ---: | ---: | ---: | +| raw_reward | 0.000000 | 0.000000 | 持平 | +| rollout_log_probs | -0.181073 | -0.181073 | 持平 | +| ref_log_probs | -0.182800 | -0.182881 | 差异 -0.000081 | +| train_rollout_logprob_abs_diff | 0.011258 | 0.013460 | +0.002202 | +| ref_log_probs_time | 12.980901 s | 10.400161 s | -19.88% | +| actor_train_time | 36.332478 s | 33.124115 s | -8.83% | +| train_time | 49.632797 s | 43.838564 s | -11.67% | +| step_time | 96.997870 s | 90.671674 s | -6.52% | +| actor_train_tok_per_s | 9.000212 | 9.871962 | +9.69% | +| peak_vram | 64363 MiB | 64363 MiB | 持平 | +| RL-Kernel fallback count | N/A | 0 | 命中主路径 | + +当前数据能证明：TP=8 下 `linear_logp` 集成可成功跑通，candidate 命中 SM90 fused backend 且没有 fallback；selected-logprob/ref-logprob 路径耗时下降，训练/rollout logprob alignment 仍在同一量级。该结果是 smoke validation，不替代 `vime-RLK.md` 中要求的多 run、多 step 正式宣传 benchmark。 + +## 图表 + +![Raw Reward](charts/raw_reward.png) + +![Logprob Alignment](charts/logprob_alignment.png) + +![Step Time](charts/step_time.png) + +![Logprob Time / Peak VRAM](charts/logprob_time_peak_vram.png) + +![Performance Breakdown](charts/performance_breakdown.png) + +结构化指标同时保存为 [data/metrics.csv](data/metrics.csv)。 + +## 实验范围 + +| 项目 | 设置 | +| --- | --- | +| baseline | `/workspace/vime-benchmark`，RL-Kernel off | +| candidate | `/workspace/vime-rlk-integration`，PR 分支 `vime-rlk-integration` | +| enabled RL-Kernel ops | `linear_logp` only | +| 模型 | `/root/Qwen3-30B-A3B` | +| Megatron checkpoint | `/root/Qwen3-30B-A3B_torch_dist` | +| 训练数据 | `/root/dapo-math-17k/dapo-math-17k.jsonl` | +| Eval 数据 | `/root/aime-2024/aime-2024.jsonl` | +| GPU | 8 x NVIDIA H100 80GB HBM3 | +| CUDA_HOME | `/usr/local/lib/python3.11/dist-packages/nvidia/cu13` | +| Python | 3.11.10 | +| Torch | 2.11.0+cu130 | +| vLLM | 0.22.0 | + +后续所有 `linear_logp` 实验均以 `TP=8` 为准。`scripts/run-qwen3-30B-A3B.sh` 和 `vime-RLK.md` 中默认 TP 已同步为 8。 + +## Run 配置 + +| 配置 | baseline | candidate | +| --- | --- | --- | +| run_name | `smoke-baseline-tp8` | `smoke-candidate-tp8` | +| Ray job | `raysubmit_rUd8WjeLXv4eZxcb` | `raysubmit_rbz7TUKU3T7U4GPm` | +| 状态 | succeeded | succeeded | +| TP / EP / CP | 8 / 8 / 1 | 8 / 8 / 1 | +| rollout engines | 1 engine, 8 GPU per engine | 1 engine, 8 GPU per engine | +| NUM_ROLLOUT | 1 | 1 | +| ROLLOUT_BATCH_SIZE | 1 | 1 | +| N_SAMPLES_PER_PROMPT | 1 | 1 | +| GLOBAL_BATCH_SIZE | 1 | 1 | +| ROLLOUT_MAX_RESPONSE_LEN | 128 | 128 | +| MAX_TOKENS_PER_GPU | 4096 | 4096 | +| VLLM_GPU_MEMORY_UTILIZATION | 0.7 | 0.7 | +| VIME_VLLM_ENFORCE_EAGER | 1 | 1 | +| VIME_NO_GRAD_ACCUM_FUSION | 1 | 1 | +| VIME_SKIP_EVAL_BEFORE_TRAIN | 1 | 1 | +| VIME_RL_KERNEL | 0 | 1 | +| VIME_RL_KERNEL_OPS | empty | `linear_logp` | +| VIME_RL_KERNEL_STRICT | 0 | 1 | + +本地原始日志目录位于 `experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/runs/`，该目录被 `.gitignore` 屏蔽，不随 PR 提交。报告和图表使用日志中抽取出的成功指标；原始 checkpoint 在指标抽取后已清理。 + +## Candidate backend 命中情况 + +candidate 日志确认： + +```text +Using RL-Kernel linear_logp op: FusedLinearLogpSM90Op +Successfully linked to precompiled _C.fused_linear_logp_sm90 kernel +train/rl_kernel_fallback_count: 0.0 +Job 'raysubmit_rbz7TUKU3T7U4GPm' succeeded +``` + +这说明本次 candidate 走的是 RL-Kernel `linear_logp` fused backend，没有落回 vime materialized logits 路径。 + +## 关键修复 + +### TP 统一为 8 + +`linear_logp` 后续实验统一按 `TP=8` 执行。脚本默认值和实验文档中的命令均已改为： + +```text +MEGATRON_TP=8 +MEGATRON_EP=8 +MEGATRON_CP=1 +``` + +### CUDA 13 torch_memory_saver preload + +当前环境安装的是 CUDA 13 版本 preload so： + +```text +torch_memory_saver_hook_mode_preload_cu13.abi3.so +``` + +原代码只查 CUDA 12 或通用 preload so，会导致 Megatron actor 初始化阶段找不到 preload 库。已在 `vime/ray/actor_group.py` 增加 CUDA 13 preload 文件名支持。 + +### gradient accumulation fusion + +TP=8 smoke 在当前 Torch/CUDA 13/Apex 组合下，Megatron fused gradient accumulation 可能触发 cuBLAS 初始化错误。实验脚本加入： + +```text +VIME_NO_GRAD_ACCUM_FUSION=1 +--no-gradient-accumulation-fusion +``` + +该配置下 baseline 和 candidate 均成功完成。 + +### 禁用实验 checkpoint 保存 + +30B checkpoint 很大，本次 smoke 初始运行每个 run 会产生约 400GB checkpoint。为避免后续复现实验继续写入大文件，`scripts/run-qwen3-30B-A3B.sh` 新增： + +```text +VIME_DISABLE_SAVE=1 +``` + +该开关启用时不传 `--save` 和 `--save-interval`，`save_interval=None` 会关闭训练循环中的周期保存。实验 runner `run_one.sh` 默认设置 `VIME_DISABLE_SAVE=1`。 + +### untied LM Head 权重 + +Qwen3 使用 untied embedding/output weights。`linear_logp` 的数学路径是： + +```text +logp = log_softmax(hidden_states @ output_layer.weight.T + bias)[target_token] +``` + +因此 `linear_logp` 需要 LM Head 权重，但没有额外启用独立的 RL-Kernel LM Head op。修复前在 PP=1 且 `pre_process=True` 的模型上，辅助函数可能优先拿到 `shared_embedding_or_output_weight()` 返回的 embedding weight，而不是 untied `output_layer.weight`，这会造成 train/ref/rollout logprob 不一致。 + +已修复为优先使用 `output_layer.weight`，仅在该权重不可用时才回退到 shared embedding/output weight。新增单测覆盖： + +```text +test_linear_logp_context_prefers_output_layer_weight_for_untied_pp1_model +test_linear_logp_context_uses_shared_weight_when_output_layer_weight_is_missing +``` + +## 指标明细 + +| 指标 | baseline | candidate | +| --- | ---: | ---: | +| rollout/raw_reward | 0.000000 | 0.000000 | +| rollout/rewards | 0.000000 | 0.000000 | +| rollout/response_lengths | 128.000000 | 128.000000 | +| rollout/truncated | 1.000000 | 1.000000 | +| rollout/rollout_log_probs | -0.181073 | -0.181073 | +| rollout/ref_log_probs | -0.182800 | -0.182881 | +| rollout/kl | 0.000000 | 0.000000 | +| train/loss | 0.000000 | 0.000000 | +| train/entropy_loss | 0.153894 | 0.000000 | +| train/train_rollout_logprob_abs_diff | 0.011258 | 0.013460 | +| train/grad_norm | 0.000000 | 0.000000 | +| train/rl_kernel_fallback_count | N/A | 0.000000 | +| perf/rollout_time | 7.401981 s | 7.559618 s | +| perf/ref_log_probs_time | 12.980901 s | 10.400161 s | +| perf/actor_train_time | 36.332478 s | 33.124115 s | +| perf/train_time | 49.632797 s | 43.838564 s | +| perf/update_weights_time | 35.229457 s | 35.257555 s | +| perf/step_time | 96.997870 s | 90.671674 s | +| perf/actor_train_tok_per_s | 9.000212 | 9.871962 | +| peak_vram | 64363 MiB | 64363 MiB | + +`train/entropy_loss` 在 candidate 中为 0 是预期行为：当前 `entropy_coef=0` 且 `linear_logp` context 激活时，policy loss 路径不再为指标额外 materialize logits 计算 entropy，避免抵消 `linear_logp` 的收益。该值不用于本轮质量对齐判断。 + +## 验证记录 + +已执行： + +```bash +pytest tests/test_rl_kernel_args.py tests/test_rl_kernel_linear_logp_integration.py tests/test_value_temperature.py tests/test_metric_report.py -q +``` + +结果： + +```text +41 passed, 15 warnings in 5.51s +``` + +已执行： + +```bash +pytest tests/test_rl_kernel_logp_integration.py tests/test_rl_kernel_args.py tests/test_rl_kernel_linear_logp_integration.py -q +``` + +结果： + +```text +26 passed, 15 warnings in 5.28s +``` + +已执行： + +```bash +bash -n scripts/run-qwen3-30B-A3B.sh +bash -n experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/run_one.sh +git diff --check +``` + +结果：均通过。 + +图表文件已用 PIL 检查尺寸和像素方差，均为非空 PNG。 + +## 复现实验 + +本实验目录提供统一入口： + +```bash +cd /workspace/vime-rlk-integration + +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ +NUM_GPUS=8 \ +MEGATRON_TP=8 \ +MEGATRON_EP=8 \ +MEGATRON_CP=1 \ +ROLLOUT_NUM_GPUS_PER_ENGINE=8 \ +ROLLOUT_BATCH_SIZE=1 \ +N_SAMPLES_PER_PROMPT=1 \ +GLOBAL_BATCH_SIZE=1 \ +MAX_TOKENS_PER_GPU=4096 \ +ROLLOUT_MAX_RESPONSE_LEN=128 \ +NUM_ROLLOUT=1 \ +VLLM_GPU_MEMORY_UTILIZATION=0.7 \ +VIME_DISABLE_SAVE=1 \ +experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/run_one.sh \ + candidate \ + smoke-candidate-tp8 \ + /workspace/vime-rlk-integration \ + /workspace/vime-rlk-integration/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/runs/smoke-candidate-tp8 +``` + +baseline 将第一个参数改为 `baseline`，repo 改为 `/workspace/vime-benchmark`，并使用不同 run 目录即可。 + +## 后续正式 benchmark 建议 + +当前 smoke validation 已满足接入正确性、backend 命中、无 fallback、TP=8 跑通和单 step 性能趋势确认。若要作为主宣传数字，仍应按 `vime-RLK.md` 做更长 run： + +| 项目 | 建议 | +| --- | --- | +| run 次数 | baseline/candidate 每组至少 3 次 | +| step 数 | 丢弃前 5-10 step warmup 后统计 | +| 统计项 | mean/p50/p90 step time、logprob time、peak VRAM、reward、logprob alignment | +| 口径 | 保持 TP=8/EP=8/CP=1，candidate 仅启用 `linear_logp` | diff --git a/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/logprob_alignment.png b/experiments/rlk-linear-logp-qwen3-30b-a3b-8h100/charts/logprob_alignment.png new 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zlo|#ywco)Wsfi6G{W@YEQ)R2aDS(I&yi>nvht*IMbwaHaZV9|GnI%K!mX4JZL+el| zt3+0sNXNxNOK`XuAC1zaFsIN2_fYEmcskvQsDn?vxVqF(qUHxE#c3)6j}EPGU " >&2 + exit 2 +fi + +VARIANT="$1" +RUN_NAME="$2" +REPO_DIR="$3" +RUN_DIR="$4" + +if [[ "${VARIANT}" != "baseline" && "${VARIANT}" != "candidate" ]]; then + echo "VARIANT must be baseline or candidate, got ${VARIANT}" >&2 + exit 2 +fi + +mkdir -p "${RUN_DIR}" + +export CUDA_HOME="${CUDA_HOME:-/usr/local/lib/python3.11/dist-packages/nvidia/cu13}" +export PATH="${CUDA_HOME}/bin:${PATH}" +export LD_LIBRARY_PATH="${CUDA_HOME}/lib:/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib:/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH:-}" +export CPATH="/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/include:${CPATH:-}" +export LIBRARY_PATH="/usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib:${CUDA_HOME}/lib:/usr/lib/x86_64-linux-gnu:${LIBRARY_PATH:-}" + +export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}" +export NUM_GPUS="${NUM_GPUS:-8}" +export MEGATRON_TP="${MEGATRON_TP:-8}" +export MEGATRON_EP="${MEGATRON_EP:-8}" +export MEGATRON_CP="${MEGATRON_CP:-1}" +export ROLLOUT_NUM_GPUS_PER_ENGINE="${ROLLOUT_NUM_GPUS_PER_ENGINE:-8}" +export ROLLOUT_BATCH_SIZE="${ROLLOUT_BATCH_SIZE:-32}" +export N_SAMPLES_PER_PROMPT="${N_SAMPLES_PER_PROMPT:-8}" +export GLOBAL_BATCH_SIZE="${GLOBAL_BATCH_SIZE:-$((ROLLOUT_BATCH_SIZE * N_SAMPLES_PER_PROMPT))}" +export MAX_TOKENS_PER_GPU="${MAX_TOKENS_PER_GPU:-20480}" +export VLLM_GPU_MEMORY_UTILIZATION="${VLLM_GPU_MEMORY_UTILIZATION:-0.7}" +export VIME_VLLM_ENFORCE_EAGER="${VIME_VLLM_ENFORCE_EAGER:-1}" +export VIME_NO_GRAD_ACCUM_FUSION="${VIME_NO_GRAD_ACCUM_FUSION:-1}" +export ROLLOUT_MAX_RESPONSE_LEN="${ROLLOUT_MAX_RESPONSE_LEN:-8192}" +export NUM_ROLLOUT="${NUM_ROLLOUT:-12}" + +export VIME_TENSORBOARD=1 +export TENSORBOARD_DIR="${RUN_DIR}/tensorboard" +export TB_PROJECT_NAME="${TB_PROJECT_NAME:-vime-rlk-linear-logp}" +export TB_EXPERIMENT_NAME="${RUN_NAME}" +export VIME_CKPT_DIR="${RUN_DIR}/ckpt" +export VIME_DISABLE_SAVE="${VIME_DISABLE_SAVE:-1}" +export VIME_SAVE_INTERVAL="${VIME_SAVE_INTERVAL:-20}" +export VIME_SKIP_EVAL_BEFORE_TRAIN="${VIME_SKIP_EVAL_BEFORE_TRAIN:-1}" + +if [[ "${VARIANT}" == "candidate" ]]; then + export VIME_RL_KERNEL=1 + export VIME_RL_KERNEL_OPS=linear_logp + export VIME_RL_KERNEL_STRICT=1 +else + unset VIME_RL_KERNEL VIME_RL_KERNEL_OPS VIME_RL_KERNEL_STRICT +fi + +{ + echo "variant=${VARIANT}" + echo "run_name=${RUN_NAME}" + echo "repo_dir=${REPO_DIR}" + echo "run_dir=${RUN_DIR}" + echo "cuda_home=${CUDA_HOME}" + echo "cuda_visible_devices=${CUDA_VISIBLE_DEVICES}" + echo "num_gpus=${NUM_GPUS}" + echo "megatron_tp=${MEGATRON_TP}" + echo "megatron_ep=${MEGATRON_EP}" + echo "megatron_cp=${MEGATRON_CP}" + echo "rollout_num_gpus_per_engine=${ROLLOUT_NUM_GPUS_PER_ENGINE}" + echo "rollout_batch_size=${ROLLOUT_BATCH_SIZE}" + echo "n_samples_per_prompt=${N_SAMPLES_PER_PROMPT}" + echo "global_batch_size=${GLOBAL_BATCH_SIZE}" + echo "max_tokens_per_gpu=${MAX_TOKENS_PER_GPU}" + echo "vllm_gpu_memory_utilization=${VLLM_GPU_MEMORY_UTILIZATION}" + echo "vime_vllm_enforce_eager=${VIME_VLLM_ENFORCE_EAGER}" + echo "vime_no_grad_accum_fusion=${VIME_NO_GRAD_ACCUM_FUSION}" + echo "rollout_max_response_len=${ROLLOUT_MAX_RESPONSE_LEN}" + echo "num_rollout=${NUM_ROLLOUT}" + echo "vime_save_interval=${VIME_SAVE_INTERVAL}" + echo "vime_disable_save=${VIME_DISABLE_SAVE}" + echo "vime_skip_eval_before_train=${VIME_SKIP_EVAL_BEFORE_TRAIN}" + echo "vime_rl_kernel=${VIME_RL_KERNEL:-0}" + echo "vime_rl_kernel_ops=${VIME_RL_KERNEL_OPS:-}" + echo "vime_rl_kernel_strict=${VIME_RL_KERNEL_STRICT:-0}" + nvidia-smi --query-gpu=index,name,memory.total --format=csv,noheader,nounits +} > "${RUN_DIR}/run_config.txt" + +( + while true; do + date +%s + nvidia-smi --query-gpu=index,memory.used --format=csv,noheader,nounits + sleep "${VRAM_POLL_INTERVAL:-2}" + done +) > "${RUN_DIR}/vram.csv" & +MONITOR_PID=$! + +cleanup() { + kill "${MONITOR_PID}" >/dev/null 2>&1 || true +} +trap cleanup EXIT + +cd "${REPO_DIR}" +bash scripts/run-qwen3-30B-A3B.sh 2>&1 | tee "${RUN_DIR}/train.log" diff --git a/scripts/run-qwen3-30B-A3B.sh b/scripts/run-qwen3-30B-A3B.sh index 726a929b..0b370666 100644 --- a/scripts/run-qwen3-30B-A3B.sh +++ b/scripts/run-qwen3-30B-A3B.sh @@ -46,7 +46,7 @@ SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)" VIME_ROOT="$(cd -- "${SCRIPT_DIR}/.." &>/dev/null && pwd)" source "${SCRIPT_DIR}/models/qwen3-30B-A3B.sh" -MEGATRON_TP=${MEGATRON_TP:-4} +MEGATRON_TP=${MEGATRON_TP:-8} MEGATRON_EP=${MEGATRON_EP:-${NUM_GPUS}} MEGATRON_CP=${MEGATRON_CP:-1} MAX_TOKENS_PER_GPU=${MAX_TOKENS_PER_GPU:-20480} @@ -57,15 +57,20 @@ ROLLOUT_MAX_RESPONSE_LEN=${ROLLOUT_MAX_RESPONSE_LEN:-8192} GLOBAL_BATCH_SIZE=${GLOBAL_BATCH_SIZE:-$((ROLLOUT_BATCH_SIZE * N_SAMPLES_PER_PROMPT))} ROLLOUT_NUM_GPUS_PER_ENGINE=${ROLLOUT_NUM_GPUS_PER_ENGINE:-${NUM_GPUS}} VLLM_GPU_MEMORY_UTILIZATION=${VLLM_GPU_MEMORY_UTILIZATION:-0.7} +VIME_CKPT_DIR=${VIME_CKPT_DIR:-/root/Qwen3-30B-A3B_vime} CKPT_ARGS=( --hf-checkpoint /root/Qwen3-30B-A3B #--hf-checkpoint /root/Qwen3-30B-A3B-FP8 --ref-load /root/Qwen3-30B-A3B_torch_dist - --load /root/Qwen3-30B-A3B_vime/ - --save /root/Qwen3-30B-A3B_vime/ - --save-interval 20 + --load "${VIME_CKPT_DIR}/" ) +if [[ "${VIME_DISABLE_SAVE:-0}" != "1" ]]; then + CKPT_ARGS+=( + --save "${VIME_CKPT_DIR}/" + --save-interval "${VIME_SAVE_INTERVAL:-20}" + ) +fi ROLLOUT_ARGS=( --prompt-data /root/dapo-math-17k/dapo-math-17k.jsonl @@ -91,6 +96,9 @@ EVAL_ARGS=( --eval-max-response-len 16384 --eval-top-p 1 ) +if [[ "${VIME_SKIP_EVAL_BEFORE_TRAIN:-0}" == "1" ]]; then + EVAL_ARGS+=(--skip-eval-before-train) +fi PERF_ARGS=( --tensor-model-parallel-size "${MEGATRON_TP}" @@ -108,6 +116,9 @@ PERF_ARGS=( --use-dynamic-batch-size --max-tokens-per-gpu "${MAX_TOKENS_PER_GPU}" ) +if [[ "${VIME_NO_GRAD_ACCUM_FUSION:-0}" == "1" ]]; then + PERF_ARGS+=(--no-gradient-accumulation-fusion) +fi GRPO_ARGS=( --advantage-estimator grpo @@ -139,12 +150,24 @@ WANDB_ARGS=( # --wandb-key ${WANDB_KEY} ) +TB_ARGS=() +if [[ "${VIME_TENSORBOARD:-0}" == "1" ]]; then + export TENSORBOARD_DIR="${TENSORBOARD_DIR:-${VIME_ROOT}/tensorboard_log/${TB_EXPERIMENT_NAME:-qwen3-30B-A3B}}" + TB_ARGS+=(--use-tensorboard) + TB_ARGS+=(--tb-project-name "${TB_PROJECT_NAME:-vime-rlk}") + TB_ARGS+=(--tb-experiment-name "${TB_EXPERIMENT_NAME:-qwen3-30B-A3B}") +fi + VLLM_ARGS=( --rollout-num-gpus-per-engine "${ROLLOUT_NUM_GPUS_PER_ENGINE}" --vllm-gpu-memory-utilization "${VLLM_GPU_MEMORY_UTILIZATION}" --vllm-enable-expert-parallel - --vllm-cudagraph-capture-sizes 1 2 4 8 $(seq 16 8 256) ) +if [[ "${VIME_VLLM_ENFORCE_EAGER:-0}" == "1" ]]; then + VLLM_ARGS+=(--vllm-enforce-eager) +else + VLLM_ARGS+=(--vllm-cudagraph-capture-sizes 1 2 4 8 $(seq 16 8 256)) +fi MISC_ARGS=( # default dropout in megatron is 0.1 @@ -173,8 +196,14 @@ ray start --head --node-ip-address ${MASTER_ADDR} --num-gpus ${NUM_GPUS} --disab RUNTIME_ENV_JSON="{ \"env_vars\": { \"PYTHONPATH\": \"${VIME_ROOT}:/root/Megatron-LM/\", + \"PATH\": \"${PATH}\", + \"CUDA_HOME\": \"${CUDA_HOME:-}\", + \"LD_LIBRARY_PATH\": \"${LD_LIBRARY_PATH:-}\", + \"CPATH\": \"${CPATH:-}\", + \"LIBRARY_PATH\": \"${LIBRARY_PATH:-}\", \"CUDA_DEVICE_MAX_CONNECTIONS\": \"1\", - \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\" + \"NCCL_NVLS_ENABLE\": \"${HAS_NVLINK}\", + \"TENSORBOARD_DIR\": \"${TENSORBOARD_DIR:-}\" } }" @@ -190,6 +219,7 @@ ray job submit --address="http://127.0.0.1:8265" \ ${OPTIMIZER_ARGS[@]} \ ${GRPO_ARGS[@]} \ ${WANDB_ARGS[@]} \ + ${TB_ARGS[@]} \ ${PERF_ARGS[@]} \ ${EVAL_ARGS[@]} \ ${VLLM_ARGS[@]} \ diff --git a/tests/test_rl_kernel_linear_logp_integration.py b/tests/test_rl_kernel_linear_logp_integration.py index 4c0d1448..1fe6b5e6 100644 --- a/tests/test_rl_kernel_linear_logp_integration.py +++ b/tests/test_rl_kernel_linear_logp_integration.py @@ -328,6 +328,51 @@ def test_linear_logp_context_from_model_uses_tp_vocab_offsets(): assert context.sequence_parallel is True +@pytest.mark.unit +def test_linear_logp_context_prefers_output_layer_weight_for_untied_pp1_model(): + mpu.get_tensor_model_parallel_world_size.return_value = 1 + mpu.get_tensor_model_parallel_rank.return_value = 0 + mpu.get_tensor_model_parallel_group.return_value = None + + output_weight = torch.empty(8, 4) + embedding_weight = torch.empty(8, 4) + output_layer = types.SimpleNamespace(weight=output_weight, bias=None) + model = types.SimpleNamespace( + output_layer=output_layer, + post_process=True, + pre_process=True, + shared_embedding_or_output_weight=lambda: embedding_weight, + ) + args = _make_args() + + context = rlk_mod.get_linear_logp_context_from_model(args, model) + + assert context is not None + assert context.lm_head_weight is output_weight + + +@pytest.mark.unit +def test_linear_logp_context_uses_shared_weight_when_output_layer_weight_is_missing(): + mpu.get_tensor_model_parallel_world_size.return_value = 1 + mpu.get_tensor_model_parallel_rank.return_value = 0 + mpu.get_tensor_model_parallel_group.return_value = None + + embedding_weight = torch.empty(8, 4) + output_layer = types.SimpleNamespace(weight=None, bias=None) + model = types.SimpleNamespace( + output_layer=output_layer, + post_process=True, + pre_process=True, + shared_embedding_or_output_weight=lambda: embedding_weight, + ) + args = _make_args() + + context = rlk_mod.get_linear_logp_context_from_model(args, model) + + assert context is not None + assert context.lm_head_weight is embedding_weight + + @pytest.mark.unit def test_linear_logp_context_uses_covered_padded_vocab_when_padded_vocab_size_missing(): mpu.get_tensor_model_parallel_world_size.return_value = 4 diff --git a/vime-RLK.md b/vime-RLK.md index 42cf234d..cbb4957f 100644 --- a/vime-RLK.md +++ b/vime-RLK.md @@ -53,7 +53,7 @@ scripts/run-qwen3-30B-A3B.sh ```text NUM_GPUS=8 -MEGATRON_TP=4 +MEGATRON_TP=8 MEGATRON_EP=8 MEGATRON_CP=1 ROLLOUT_NUM_GPUS_PER_ENGINE=8 @@ -187,7 +187,7 @@ PYTHONPATH=/root/Megatron-LM torchrun --nproc-per-node 8 \ ```bash export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export NUM_GPUS=8 -export MEGATRON_TP=4 +export MEGATRON_TP=8 export MEGATRON_EP=8 export MEGATRON_CP=1 export ROLLOUT_NUM_GPUS_PER_ENGINE=8 diff --git a/vime/backends/megatron_utils/rl_kernel.py b/vime/backends/megatron_utils/rl_kernel.py index aca8706d..181afd40 100644 --- a/vime/backends/megatron_utils/rl_kernel.py +++ b/vime/backends/megatron_utils/rl_kernel.py @@ -110,6 +110,10 @@ def _is_pipeline_last_stage_for_model(model) -> bool: def _get_lm_head_weight(model, output_layer) -> torch.Tensor | None: + weight = getattr(output_layer, "weight", None) + if isinstance(weight, torch.Tensor): + return weight + shared_weight = getattr(model, "shared_embedding_or_output_weight", None) if callable(shared_weight): try: @@ -119,8 +123,7 @@ def _get_lm_head_weight(model, output_layer) -> torch.Tensor | None: except Exception: logger.debug("Unable to read shared embedding/output weight for RL-Kernel linear_logp.", exc_info=True) - weight = getattr(output_layer, "weight", None) - return weight if isinstance(weight, torch.Tensor) else None + return None def get_linear_logp_context_from_model(args: Namespace, model) -> LinearLogpContext | None: diff --git a/vime/ray/actor_group.py b/vime/ray/actor_group.py index db3500dc..0e601671 100644 --- a/vime/ray/actor_group.py +++ b/vime/ray/actor_group.py @@ -63,6 +63,7 @@ def _allocate_gpus_for_actor(self, pg, num_gpus_per_actor): import torch_memory_saver for path in [ + "torch_memory_saver_hook_mode_preload_cu13.abi3.so", "torch_memory_saver_hook_mode_preload_cu12.abi3.so", "torch_memory_saver_hook_mode_preload.abi3.so", ]: