feat(dcp): add native depth-N CKV prefetch#160
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📝 WalkthroughWalkthroughThis PR adds DCP-replicated sparse-indexer support, lockstep MLA KV-cache allocation, and workspace-scoped B12X CKV prefetching. It updates indexer metadata and cache coordination paths, adds environment controls, and expands coverage across replication, allocation, workspace lifecycle, and attention behavior. ChangesDCP-replicated indexer and lockstep MLA allocation
Estimated code review effort: 5 (Critical) | ~120 minutes Sequence Diagram(s)sequenceDiagram
participant Config
participant IndexerCache
participant IndexerMetadata
participant SparseIndexer
Config->>IndexerCache: evaluate replication policy
IndexerCache->>IndexerCache: create replicated cache spec
IndexerCache->>SparseIndexer: pass dcp_replicated
IndexerMetadata->>IndexerMetadata: use global sequence lengths
SparseIndexer-->>IndexerMetadata: retain global top-k IDs
sequenceDiagram
participant MetadataBuilder
participant CKVRegistry
participant B12xForward
participant CUDAStream
MetadataBuilder->>CKVRegistry: begin workspace step
B12xForward->>CKVRegistry: borrow ring workspace
B12xForward->>CUDAStream: enqueue lookahead gather
CUDAStream-->>CKVRegistry: record completion event
B12xForward->>CKVRegistry: wait and append gathered history
Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Signed-off-by: FujitsuPolycom <87842395+FujitsuPolycom@users.noreply.github.com>
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Validated on ai01 (4x RTX PRO 6000, TP4/DCP4/MTP3) against the RAM-only LMCache integration. The original failure occurred when a 189,033-token request resumed from a 187,136-token LMCache prefix and B12X returned a valid token-major projection buffer. The previous guard accepted only head-major storage and terminated all workers. Post-fix reproducer:
Exact-image unit gate: 30 passed. The projection still requires the expected shape/dtypes and borrowed-storage alias; only the two dense layouts consumed correctly by the existing transpose/copy are accepted. |
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🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
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Inline comments:
In `@vllm/model_executor/models/deepseek_v2.py`:
- Around line 691-703: Update Indexer.__init__() to pass the cache
specification’s effective replicated flag, including the draft_replicated result
from DeepseekV32IndexerCache.get_kv_cache_spec(), into SparseAttnIndexer. Reuse
the MLAAttentionSpec.dcp_replicated value rather than only
self.k_cache.dcp_replicated, keeping metadata and custom-op dcp world-size
initialization consistent.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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📒 Files selected for processing (17)
tests/model_executor/layers/test_sparse_attn_indexer_b12x.pytests/models/test_dcp_shard_draft_defaults.pytests/v1/attention/test_b12x_ckv_prefetch_policy.pytests/v1/attention/test_b12x_mla_dcp_workspace.pytests/v1/attention/test_indexer_dcp_localize.pytests/v1/core/test_kv_cache_utils.pytests/v1/core/test_prefix_caching.pytests/v1/worker/test_attn_utils.pytests/v1/worker/test_gpu_block_table.pyvllm/envs.pyvllm/model_executor/layers/sparse_attn_indexer.pyvllm/model_executor/models/deepseek_v2.pyvllm/v1/attention/backends/mla/b12x_mla_sparse.pyvllm/v1/attention/backends/mla/indexer.pyvllm/v1/core/block_pool.pyvllm/v1/core/kv_cache_coordinator.pyvllm/v1/core/kv_cache_utils.py
| draft_replicated = ( | ||
| not shard_draft | ||
| and layer_id is not None | ||
| and num_hidden_layers is not None | ||
| and int(layer_id) >= int(num_hidden_layers) | ||
| ) | ||
| return MLAAttentionSpec( # Only has one vector instead of K + V | ||
| block_size=self.cache_config.block_size, | ||
| block_size=block_size, | ||
| num_kv_heads=1, | ||
| head_size=self.head_dim, | ||
| dtype=self.dtype, | ||
| dcp_replicated=dcp_replicated, | ||
| dcp_replicated=self.dcp_replicated or draft_replicated, | ||
| ) |
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🗄️ Data Integrity & Integration | 🟠 Major | 🏗️ Heavy lift
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
# Locate where dcp_local_seq_lens is populated for draft/MTP attention metadata
# and whether it is gated on VLLM_DCP_SHARD_DRAFT for replicated drafts.
rg -n "dcp_local_seq_lens" --type=py -g '!tests/*' | rg -v "\.pyc"Repository: local-inference-lab/vllm
Length of output: 162
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== Candidate files =="
git ls-files | rg 'vllm/(model_executor/models/deepseek_v2\.py|attention/.*|sparse_attn.*|indexer|.*DCP.*|.*dcp.*)\.py|tests/.*deepseek.*py|tests/.*indexer.*py|tests/.*dcp.*py' | sed -n '1,200p'
echo
echo "== deepseek_v2.py outline around relevant symbols =="
ast-grep outline vllm/model_executor/models/deepseek_v2.py --match DeepseekV32IndexerCache --view expanded || true
ast-grep outline vllm/model_executor/models/deepseek_v2.py --match get_kv_cache_spec --view expanded || true
ast-grep outline vllm/model_executor/models/deepseek_v2.py --match Indexer --view expanded || true
echo
echo "== deepseek_v2.py relevant lines =="
sed -n '620,835p' vllm/model_executor/models/deepseek_v2.py
echo
echo "== Targeted searches for flag/env names =="
rg -n "VLLM_DCP_(SHARD_DRAFT|REPLICATE_INDEXER_CACHE|DCP_WORLD_SIZE|SHARD)|dcp_replicated|dcp_world_size|draft_replicated|_replicate_indexer_cache_under_dcp|SparseAttnIndexer|DeepseekV32IndexerMetadataBuilder" --type=py -g '!tests/*' | sed -n '1,240p'Repository: local-inference-lab/vllm
Length of output: 11288
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== Locate SparseAttnIndexer / DeepseekV32IndexerMetadataBuilder =="
rg -n "class SparseAttnIndexer|class .*IndexerMetadataBuilder|SparseAttnIndexer\(" --type=py --glob '*.py' | sed -n '1,200p'
echo
echo "== Candidate non-test source files =="
git ls-files '*.py' | rg 'vllm/(v1|lora|model_executor|kernels)' | rg -i 'sparse|mha|index|indexer|attention|dcp' | sed -n '1,260p'
echo
echo "== Relevant tests =="
git ls-files 'tests/**/*.py' | rg -i 'sparse_indexer|indexer|deepseek_v2|mtp|dc|dcp' | sed -n '1,260p'Repository: local-inference-lab/vllm
Length of output: 229
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== deepseek_v2.py top imports =="
sed -n '1,180p' vllm/model_executor/models/deepseek_v2.py
echo
echo "== All SparseAttnIndexer/indexer metadata occurrences =="
rg -n "SparseAttnIndexer|DeepseekV32IndexerMetadataBuilder|MLAAttentionSpec|dcp_replicated|dcp_world_size|B12xNonCompressedIndexerBackend|DeepseekV32IndexerBackend" --glob '*.py' | sed -n '1,260p'
echo
echo "== v1 attention backend index files =="
find vllm -path '*attention*' -name '*.py' -print | sed -n '1,200p'Repository: local-inference-lab/vllm
Length of output: 6833
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== sparse_attn_indexer source =="
find . -path '*/vllm/model_executor/layers/sparse_attn_indexer.py' -print -exec wc -l {} \;
sed -n '1,260p' vllm/model_executor/layers/sparse_attn_indexer.py
echo
echo "== sparse_attn_indexer tests =="
find tests -name '*sparse*indexer*' -o -name '*deepseek*index*' | sed -n '1,120p'
for f in $(find tests -name '*sparse*indexer*' -o -name '*deepseek*index*' | head -20); do
echo "--- $f ---"
wc -l "$f"
rg -n "SparseAttnIndexer|dcp_replicated|draft|VLLM_DCP_SHARD_DRAFT" "$f" || true
done
echo
echo "== Related envs =="
sed -n '1,220p' vllm/envs.py | rg -n "DCP|SHARD_DRAFT|REPLICATE|DCP_WORLD_SIZE" -C 2 || trueRepository: local-inference-lab/vllm
Length of output: 10087
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== SparseAttnIndexer class and __init__ =="
sed -n '220,450p' vllm/model_executor/layers/sparse_attn_indexer.py
echo
echo "== DeepseekV32IndexerMetadata/DeepseekV32IndexerMetadataBuilder =="
rg -n "class DeepseekV32IndexerMetadata|class DeepseekV32IndexerMetadataBuilder|dcp_world_size|dcp_replicated|dcp_local_seq_lens" vllm/model_executor/layers/sparse_attn_indexer.py vllm/v1/attention vllm/model_executor/layers/sparse_attn_indexer.py | sed -n '1,240p'
echo
echo "== Relevant backend classes =="
sed -n '1,420p' vllm/v1/attention/backends/mla/indexer.py
echo
echo "== Behavioral verifier for draft_replicated vs k_cache.dcp_replicated =="
python3 - <<'PY'
from dataclasses import dataclass
`@dataclass`
class ParallelConfig:
decode_context_parallel_size: int
prefill_context_parallel_size: int
`@dataclass`
class Config:
envs_shard_draft = None
layer_id = None
num_hidden_layers = None
dcp_replicated = False
use_v2_model_runner = False
dcp_size = 4
pcp_size = 1
def _replicate_indexer_cache_under_dcp(cfg):
if cfg.layer_id is None or cfg.num_hidden_layers is None:
return False
if cfg.dcp_size * cfg.pcp_size <= 1:
return False
if int(cfg.layer_id) >= int(cfg.num_hidden_layers):
return False
requested = cfg.envs_shard_draft
return requested
for env in [None, "1", "true", "0", "false"]:
cfg = Config(envs_shard_draft=env, layer_id=10, num_hidden_layers=8)
cache_flag = _replicate_indexer_cache_under_dcp(cfg)
shard_draft = ("1" if cfg.envs_shard_draft is None else cfg.envs_shard_draft).lower() in ("1", "true", "yes")
spec_flag = cache_flag or (not shard_draft and cfg.layer_id is not None and cfg.num_hidden_layers is not None and int(cfg.layer_id) >= int(cfg.num_hidden_layers))
print({
"VLLM_DCP_SHARD_DRAFT": env,
"k_cache.dcp_replicated": cache_flag,
"MLAAttentionSpec.dcp_replicated": spec_flag,
"Indexer passes to SparseAttnIndexer": cache_flag,
"diverges": cache_flag != spec_flag,
})
for layer_id in [6, 7, 8, 9]:
cfg = Config(envs_shard_draft="0", layer_id=layer_id, num_hidden_layers=8)
cache_flag = _replicate_indexer_cache_under_dcp(cfg)
shard_draft = ("1" if cfg.envs_shard_draft is None else cfg.envs_shard_draft).lower() in ("1", "true", "yes")
spec_flag = cache_flag or (not shard_draft and cfg.layer_id is not None and cfg.num_hidden_layers is not None and int(cfg.layer_id) >= int(cfg.num_hidden_layers))
print({
"layer_id": layer_id,
"k_cache.dcp_replicated": cache_flag,
"MLAAttentionSpec.dcp_replicated": spec_flag,
"diverges": cache_flag != spec_flag,
})
PYRepository: local-inference-lab/vllm
Length of output: 48310
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== SparseAttnIndexer constructor and build metadata =="
sed -n '2144,2270p' vllm/model_executor/layers/sparse_attn_indexer.py
sed -n '2268,2296p' vllm/model_executor/layers/sparse_attn_indexer.py
echo
echo "== SparseAttnIndexer call sites from indexers =="
rg -n "SparseAttnIndexer\(" --glob '*.py' | sed -n '1,120p'
echo
echo "== DeepseekV32IndexerMetadataBuilder build calls and local seq lens =="
sed -n '420,550p' vllm/v1/attention/backends/mla/indexer.py
sed -n '860,1100p' vllm/v1/attention/backends/mla/indexer.py
rg -n "build_indexer_metadata|get_attn_metadata|deepseek_v2_indexer|SparseAttnIndexer" --glob '*.py' | sed -n '1,200p'
echo
echo "== Behavioral verifier for draft_replicated vs k_cache.dcp_replicated =="
python3 - <<'PY'
def replicate_indexer_cache(layer_id, num_hide, shard_draft_env):
if layer_id is None or num_hide is None:
return False
raw = shard_draft_env
shard_draft = ("1" if raw is None else raw).lower() in ("1", "true", "yes")
return not shard_draft
def spec_draft_layer_flag(layer_id, num_hide, shard_draft_env, replicate_flag):
shard_draft = ("1" if shard_draft_env is None else shard_draft_env).lower() in ("1", "true", "yes")
draft_replicated = (
not shard_draft
and layer_id is not None
and num_hide is not None
and int(layer_id) >= int(num_hide)
)
return replicate_flag or draft_replicated
print("draft=True, VLLM_DCP_SHARD_DRAFT unset:", spec_draft_layer_flag(10, 8, None, 0), "k_cache=0")
print("draft=True, VLLM_DCP_SHARD_DRAFT=false:", spec_draft_layer_flag(10, 8, "0", 0), "k_cache=1")
for layer_id in [7, 8, 9]:
k_cache = replicate_indexer_cache(layer_id, 8, "0")
spec = spec_draft_layer_flag(layer_id, 8, "0", k_cache)
print(f"layer_id={layer_id}: k_cache={k_cache}, spec={spec}, diverges={k_cache != spec}")
PYRepository: local-inference-lab/vllm
Length of output: 6438
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== All occurrences of sparse_attn_indexer =="
rg -n "sparse_attn_indexer|sparse_attn_indexer_cuda|sparse attention indexer|sparse_attn_topk" --glob '*.py' --glob '*.cu' --glob '*.cpp' --glob '*.cc' | sed -n '1,300p'
echo
echo "== Test files =="
git ls-files 'tests/**/*.py' | rg -i 'test_sparse_attn_indexer|test_deepseek_v2_index|test_dcp' | sed -n '1,200p'
echo
echo "== Tests involving dcp_replicated or draft behavior =="
rg -n "dcp_replicated|VLLM_DCP_SHARD_DRAFT|draft_replicated|dc|dcp|next_n|speculative" tests --glob '*.py' | rg -i "indexer|sparse|deepseek|mtp|draft" | sed -n '1,320p'Repository: local-inference-lab/vllm
Length of output: 323
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== Files containing vllm.sparse_attn_indexer =="
python3 - <<'PY'
import subprocess
result = subprocess.run(['git','grep','-Il','vllm.sparse_attn_indexer','vllm','tests'], capture_output=True, text=True)
print(result.stdout.strip() or '(no matches)')
print('stderr=', result.stderr.strip()[:500], sep='\n')
PY
echo
echo "== Any source mentioning dcp_world_size and dcp_replicated in same file =="
for f in $(git ls-files '*.py' | xargs rg -l 'dcp_world_size|dcp_replicated'); do
if rg -q 'dcp_world_size' "$f" && rg -q 'dcp_replicated' "$f"; then
echo "--- $f ---"
wc -l "$f"
fi
done
echo
echo "== Read relevant backend metadata and common seq lens implementation =="
sed -n '840,900p' vllm/v1/attention/backends/utils.py
sed -n '960,1080p' vllm/v1/attention/backends/mla/indexer.py
echo
echo "== Read relevant backward/topk path in sparse_attn_indexer =="
sed -n '1620,1945p' vllm/model_executor/layers/sparse_attn_indexer.py
sed -n '1940,2030p' vllm/model_executor/layers/sparse_attn_indexer.pyRepository: local-inference-lab/vllm
Length of output: 29259
🏁 Script executed:
#!/bin/bash
set -euo pipefail
echo "== Builder helpers for local DCP seq lens =="
rg -n "_use_dcp_local_kv|_dcp_localize_decode_seq_lens|_prepare_decode_tensors|build_prefill_chunk_metadata" vllm/v1/attention/backends/mla/indexer.py -C 4 | sed -n '1,260p'
echo
echo "== Relevant source slices =="
sed -n '1210,1375p' vllm/v1/attention/backends/mla/indexer.py
sed -n '1375,1470p' vllm/v1/attention/backends/mla/indexer.py
echo
echo "== Read metadata builder's build_decode_tensors implementation =="
sed -n '1022,1205p' vllm/v1/attention/backends/mla/indexer.py
echo
echo "== Read build_prefill_chunk_metadata implementation =="
sed -n '900,1015p' vllm/v1/attention/backends/mla/indexer.pyRepository: local-inference-lab/vllm
Length of output: 25489
Pass the same replicated-cache flag to SparseAttnIndexer.
DeepseekV32IndexerCache.get_kv_cache_spec() marks draft layers replicated with self.dcp_replicated or draft_replicated, but Indexer.__init__() only forwards self.k_cache.dcp_replicated. For the legacy replicated-draft case, the metadata builder then uses dcp_world_size=1 while the indexer op is initialized with dcp_world_size=configured_dcp_world_size, so the custom op can miss its local-localized KV bounds/top-k ranges. Pass the cache-spec replicated flag through to SparseAttnIndexer.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@vllm/model_executor/models/deepseek_v2.py` around lines 691 - 703, Update
Indexer.__init__() to pass the cache specification’s effective replicated flag,
including the draft_replicated result from
DeepseekV32IndexerCache.get_kv_cache_spec(), into SparseAttnIndexer. Reuse the
MLAAttentionSpec.dcp_replicated value rather than only
self.k_cache.dcp_replicated, keeping metadata and custom-op dcp world-size
initialization consistent.
|
The prefetch portion has now been rebuilt as a standalone PR directly against current #177 removes the replicated-indexer stack, preallocates the bounded workspace pool before KV-cache memory planning, isolates target/draft and DBO lanes, and includes the measured memory cost plus depth-0/1/2/3 results. It therefore avoids the optimistic KV accounting caused by lazy post-profile ring allocation while preserving the validated depth-1 overlap. I am leaving this PR open for the author/maintainer to disposition; #177 is the clean merge candidate for CKV layer prefetch only. |
2 participants
Summary
Add feature-gated full-CKV DCP prefill gather with native depth-N cross-layer lookahead.
For eligible pure-prefill steps, each rank gathers native paged CKV history on a dedicated communicator and runs its local query heads over global CKV. A dedicated ring and side stream can queue future layers; the destination layer patches current-chunk records written after prefetch began before attention consumes the workspace.
Scope
VLLM_B12X_MLA_CKV_GATHERVLLM_B12X_MLA_CKV_PREFETCH_DEPTHLookahead supports
fp8_ds_mlaandnvfp4_ds_mla. All new behavior defaults off.Stack note
This PR is stacked on #159, so GitHub temporarily shows the allocator/indexer prerequisite commits too. The new review unit in this PR is
d6545696ff(feat(dcp): add depth-N native CKV prefetch).Validation
On the final TP4/DCP4/MTP3 integration:
Those figures include the complete integration stack and are evidence of compatibility, not an isolated claim for this commit alone.
Compact-record validation:
Design
GLM-5.2 Sparse CKV under Decode Context Parallelism
Credits
The lookahead design originates from Koush's CKV gather and shared-layer prefetch work and builds on Luke Alonso's B12X/Sparkinfer infrastructure.
Implementation, tests, and documentation were developed with OpenAI Codex assistance and manually reviewed and GPU-validated.
Summary by CodeRabbit
New Features
Bug Fixes