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…unk to satisfy DCP divisibility asserts Round determine_chunked_prefill_workspace_size down to a multiple of dcp_world_size, and round per-step max_context_chunk down to lcm(page_size, dcp_world_size) (or just dcp_world_size on the non-CUDA aot_schedule=False path). Both rounds are down-only; worst-case shrink is lcm(page_size, dcp_world_size) - 1 slots (<= 0.1% of a 64K workspace). This makes the init-time assert at line 1617 and the per-step assert at line 1942 in MLACommonMetadataBuilder provably unreachable. The init-time case is the regression flagged by gemini-code-assist on vllm-project#25478; the per-step case is reproducibly hit on ROCm (where aot_schedule=False so the page-size round-down doesn't run) by shared-prefix-cached prefix + many concurrent short tails workloads. Signed-off-by: Jin Tao <jin.tao@amd.com>
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Code Review
This pull request introduces alignment logic for Decode Context Parallelism (DCP) within the MLA attention implementation. It ensures that the chunked prefill workspace size and the maximum context chunk size are divisible by the DCP world size to satisfy downstream assertions and prevent runtime errors. Specifically, it adds rounding logic to the workspace size determination and introduces a helper method to align context chunks based on the least common multiple of the page size and DCP world size. I have no feedback to provide as there were no review comments to evaluate.
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Closing this as obsolete — both divisibility issues have since been fixed upstream by the Per-step assert ( Init-time assert ( Verified by rebasing this branch onto |
Purpose
What is the bug like?
On 8× MI300X with
--decode-context-parallel-size 8 --enable-expert-parallel, ourKimi-K2.5-W4A8engine intermittently died with all 8 DCP ranks hittingassert max_context_chunk % self.dcp_world_size == 0inmla_attention.py:1942simultaneously. The crash is triggered by their workload pattern — a shared prefix-cached prefix plus many concurrent short tails — which drivesnum_prefills_with_context_cputo values like {3,5,6,7,9,10,11,13}, wherechunked_prefill_workspace_size // nlands on integers that are not multiples of 8. It only reproduces on the ROCm path because thereaot_schedule=False, so the existinground_down(max_context_chunk, page_size)step that would have masked the bad divisor on CUDA never runs.Two divisibility asserts in
vllm/model_executor/layers/attention/mla_attention.pycan fire when--decode-context-parallel-size > 1:assert self.chunked_prefill_workspace_size % self.dcp_world_size == 0MLACommonMetadataBuilder.build):assert max_context_chunk % self.dcp_world_size == 0The init-time case is the regression gemini-code-assist flagged on #25478 (the
max(workspace_size, max_num_seqs * block_size)clamp) but never fixed. The per-step case is reproducibly hit on ROCm — whereaot_schedule=Falseso the page-size round-down doesn't run — by shared-prefix-cached prefix + many concurrent short tails (same crash family as #28476; #28526 only fixed the adjacentreorg_kvcacheshape assert).Concretely: with
chunked_prefill_workspace_size=65536,dcp_world_size=8, no page-size round-down, andnum_prefills_with_context_cpu ∈ {3,5,6,7,9,10,11,13}, the floor-divide produces21845 / 13107 / 10922 / 9362 / 7281 / 6553 / 5957 / 5041— none divisible by 8 — and the per-step assert kills all DCP ranks.This PR makes both asserts provably unreachable with two surgical edits, both rounding down only — no allocation grows; worst-case shrink is
lcm(page_size, dcp_world_size) − 1slots (≤ 0.1% on a 64K workspace):determine_chunked_prefill_workspace_size: round the returned size down to a multiple ofdcp_world_sizewhen DCP > 1._align_max_context_chunk_for_dcpstatic helper, called frombuild()after the existing page-sizeround_down. Rounds tolcm(page_size, dcp_world_size), or justdcp_world_sizewhenaot_schedule=False.Test Plan
Customer-shaped repro that drives
num_prefills_with_context_cpuinto the bad-divisor range. Run command:VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_FP4BMM=0 VLLM_ROCM_USE_AITER_MLA=0 \ VLLM_ATTENTION_BACKEND=TRITON_MLA VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=0 \ vllm serve amd/Kimi-K2.5-W4A8 \ --tensor-parallel-size 8 --decode-context-parallel-size 8 \ --enable-expert-parallel --trust-remote-code \ --gpu-memory-utilization 0.9Stress driver (1× MI300X, 8 GPUs): prime an 80K shared prefix, then 6 rounds × 16 concurrent partial-prefills (
<80K prefix> + <unique 64-tok tail>, total 96 requests).Test Result
Un-patched upstream (ROCm
aot_schedule=Falsepath): reliably crashes within the first few rounds with all 8 DCP ranks asserting onmla_attention.py:1942simultaneously, engine dead.With this PR: 96 / 96 requests returned 200,
/healthstayed 200 throughout, no engine restarts. Earlier instrumented runs counted 80 distinct events where the newlcm(page_size, dcp_world_size)rounding actually changedmax_context_chunk— i.e. 80 places where un-patched code would have asserted — withnum_prefills_with_context_cpuobserved in 3..13.Performance neutrality — back-to-back c=32 saturation sweep (random dataset,
--ignore-eos, 128 prompts, identical seeds) on three workloads:The round-down (≤ 7 slots out of ~13K for
max_context_chunk, ≤ 7 of 65 536 for the workspace) has no measurable cost.Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing