[Attention] Remove DCP indexer interleave guard and test TP1 output parity by stu-cao · Pull Request #55802 · vllm-project/vllm

stu-cao · 2026-09-08T03:12:00Z

Purpose

Remove the sparse indexer's rejection of DCP with cp_kv_cache_interleave_size > 1, and add direct attention-output regression tests against complete, unsharded TP1 KV. This enables review of configurations such as DCP with NIXL, which uses interleave 64.

Draft: the tested attention outputs agree within the stated FP8 tolerances. Full-model outputs are numerically sensitive, and accuracy equivalence is not established. The original single GSM8K pair scored 11 questions lower at interleave64. Three further controlled pairs scored 23 higher, 3 higher, and 10 lower; the lower score is not consistent across repeats. The investigation below causally identifies attention rounding and index-order variability as sources of changed generations.

The production change is exactly the nine-line guard deletion in DeepseekV32IndexerMetadataBuilder. It is generic because this builder implements interleave/ownership arithmetic independently of model identity or KV-transfer connector. There is no GLM/NIXL allowlist or backend capability override. Separate backend and MTP compatibility checks remain unchanged. The evidence is limited to the tested implementations/configurations, not every configuration admitted by deleting the guard.

The original guard in #46076 (c5200d3565) cited GSM8K failure. That historical failure has not been root-caused. The pure-DCP defects reported in #50095 and fixed by #50005, and the non-owner-rank prefill fix in #54908, are plausible contributors; both fixes are in this branch's base. Neither establishes why interleave1 passed while larger interleaves failed. The historical result used GLM-5.2-FP8/lm_eval at concurrency128; the current GLM-5.3-NVFP4 evaluation is not a direct reproduction.

Duplicate-work searches for DCP/interleave and #50095 found no other open PR combining this guard deletion with TP1 attention-output comparisons. #50005/#54908 are merged; #54541 is a backport and #52472 adds a different backend.

Test Plan

Tests are committed to the existing vLLM attention suites. Each comparison uses identical generated inputs: TP1/DCP1 has complete KV; TP4/DCP4 shards the same KV with interleave64 and combines actual distributed attention outputs. The real indexer builder is constructed from the DCP configuration and build() is called. Restoring the guard fails its constructor before attention executes.

test_sparse_dcp4_interleave64_attention_matches_tp1[flashinfer,flashmla] exercises real indexer quantization, logits, local/global sparse selection, prefill/decode metadata, query gathering, attention kernels and distributed output merging. FLASHINFER_MLA_SPARSE resolves to the TRTLLM sparse implementation with FP8 Q/KV and A2A; FLASHMLA_SPARSE uses BF16 Q, fp8_ds_mla and ag_rs.
test_trtllm_ragged_dcp4_interleave64_attention_matches_tp1 exercises the internal TRTLLM_RAGGED dense MLA prefill implementation through production chunk metadata and forward_mha: FP8 cache gathering/dequantization, KV projection, causal suffix/context attention, continuation chunks and combined outputs.

Nine cases per sparse backend cover two seeds, decode and four causal query rows (MTP3), two scale/input-magnitude settings, top-k2048, contexts through8193, 64/256-token boundaries, shuffled pages, populated future tokens and empty shards. The ordinary sparse-prefill case uses64 query tokens and lengths65/8193. Its position keys and one-hot indexer queries give exact, strictly ordered scores, allowing independent assertions of the expected causal top-k. Eight dense-prefill cases cover two seeds, suffix lengths4/65, scales1/0.25, contexts0/1/63/64/65/255/256/257/2049 and multiple context chunks. Tested geometry is64 heads, KV rank512, NoPE192, RoPE64, and dense value dimension256.

The tests retain actual TP1/TP4 outputs, input hashes, quantized query bytes, indices and causal bounds in pytest's temporary directory. They assert exact input/index/bound equality. The FP32 oracle validates attention given the real indexer's selections and catches shared attention-kernel errors; it cannot detect a top-k error shared by both paths. The independently known prefill ranking checks selection separately.

Sparse tolerances follow the existing backend budget: elementwise rtol=0.065, atol=0.05, plus per-query relative L2 below0.065 against TP1 and the oracle. Dense prefill uses rtol=atol=0.01 and per-query relative L2 below0.01. Sparse natural-log LSE uses rtol=atol=1e-4. These are numerical tolerances, not bitwise equality.

export VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE=2147483648
export NCCL_IB_DISABLE=1 NCCL_SOCKET_IFNAME=lo GLOO_SOCKET_IFNAME=lo
export OMP_NUM_THREADS=8
PYTHONPATH=/work/source /work/.venv/bin/python -m pytest \
  /work/source/tests/v1/attention/test_indexer_dcp_localize.py \
  /work/source/tests/v1/attention/test_mla_backends.py::test_trtllm_ragged_dcp4_interleave64_attention_matches_tp1 \
  --basetemp=/work/pytest-prefill-final \
  --junitxml=/work/pytest-prefill-final.xml -q -s

Test Result

79 tests passed in109.02s with standard parent conftest/autouse fixtures, without --confcutdir, on four GB200 GPUs. This includes26 actual output comparisons with the guard completely removed.

Implementation	Cases	Max absolute difference	Worst query relative L2	Index-set mismatches
`FLASHINFER_MLA_SPARSE` / TRTLLM	9	0.03125	1.93911%	0
`FLASHMLA_SPARSE`	9	0.0078125	0.45789%	0
`TRTLLM_RAGGED`	8	0.00390625	0.23980%	N/A

Maximum sparse natural-log LSE difference was 9.536743e-7, including comparison with the FP32 oracle. The tests populate caches directly; fused model-level cache population is exercised separately by the model investigation. Kernel MTP cases do not validate the full MTP scheduler or relax its separate compatibility checks.

Runtime: vllm/vllm-openai@sha256:275cf4900543d71daaf43b008619c79372e139c0e4f4b643202b3fec97886c19, torch2.13.0+cu130, CUDA13.0, FlashInfer0.6.18, CUTLASS DSL4.6.2, Triton3.7.1. Checkout Python source is 9005dcfd50; native artifacts are from d9105ea800, with native source unchanged between those commits. Production source is unchanged from the original model run at ce03ef5450. Actual numerical-test tensors are retained in a verified archive, SHA256 50edbd7dbc1262d945d681bdc2879a8e5b95b6b228c938100d71d95a9c8e56d5.

Applicable manual-stage pre-commit hooks, including Python3.10–3.13 mypy, passed locally for the changed files; git diff --check passed. GitHub's contributor-eligibility pre-check blocks its pre-commit job; no eligibility/workflow bypass is included. The standard-conftest run does not reproduce every Buildkite environment setting. No DCP-specific end-to-end CI step is added.

Model evaluation and root-cause investigation

All model runs are aggregated TP4/DCP4 serving, with Inferact/GLM-5.3-NVFP4 revision ce67b36f3669192b5bb233819f0fda6c8a9837f8, the configured FlashInfer sparse backend/internal TRTLLM prefill, FP8 KV, block64, prefix caching, max length16384, max batched tokens8192 and max sequences32. MTP and P/D/NIXL transport are not configured. The unchanged five-shot GSM8K prompt set contains all1319 questions; scoring uses this checkout's GSM8K helper, greedy temperature0, seed0 and max1024 completion tokens.

Run	Interleave1 correct /1319	Interleave64 correct /1319	I64 minus I1
Original pair	1206 (91.43%)	1195 (90.60%)	−11
Controlled repeat0	1194 (90.52%)	1217 (92.27%)	+23
Controlled repeat1	1205 (91.36%)	1208 (91.58%)	+3
Controlled repeat2	1214 (92.04%)	1204 (91.28%)	−10

The controlled repeats use identical integer prompts, graph execution, concurrency32, a fresh shared-prefix cache salt for each repeat, and the same saved MoE kernel tactics on both hosts. Independently selected tactics initially differed in10/21 entries; pinning removes that confound within these repeats. Batching remains asynchronous. Hosts were not swapped. I1 varied by20 correct answers and I64 by13 across repeats; only129–151/1319 full sequences matched in within-configuration comparisons. Mean I64-minus-I1 accuracy was+0.404 percentage points. A descriptive two-level bootstrap over these three runs and shared questions gives a95% percentile interval of[−1.137,+1.996] points. This does not prove accuracy equivalence or uniquely explain the original11-question decrease.

The original pair had53 correct→wrong and42 wrong→correct, with no request errors, invalid answers or truncations. The new pairs had31/54,45/48 and46/36 flips respectively. All7914 requests completed with no invalid parsed answers; one I1 response in repeat1 reached the token limit while repeating an incorrect calculation and scored wrong. All raw responses and scores are retained locally in a verified archive, SHA256 c0c4470b0e5ee1a16c5dd5be49b56eec7c9c701deef7c511cec147167be692b2.

The layer traces and causal interventions use eager serving, one in-flight request per server, fresh per-request cache salts, and recorded token IDs/top20 logprobs. They separate selection/addressing from arithmetic:

The DCP top-k merge uses stable score/token-ID membership keys but an atomic counter to assign output positions. Repeated cold eager generations matched only1/8 within each interleave; diagnostic canonical index ordering made8/8 full sequences and returned logprobs exact within each configuration. Sorting did not establish equality between interleaves.
On a traced first decode, layer0 Q/KV bytes were identical between interleaves and selected IDs were exactly all causal tokens. Native combined outputs differed by0.39761% relative L2. Query/current-token KV differences began at layer1; all prompt KV bytes stayed identical through78 layers. The model difference reached27.67625% at layer77 after its inputs had diverged.
A fixed numerical model of128-key tiles, FP8 softmax-probability rounding and BF16 unnormalized partial rounding explains98.96% of the squared first-layer interleave difference. It matches about99.75% of elements exactly across448 separate saved rank-local GPU outputs, with worst residual below0.057%. This isolates a numerical mechanism; it is not an instruction-level kernel reconstruction.
With common MoE tactics, rank-local FP64 attention and FP64 DCP combination matched complete-KV FP64 at all78 traced combined outputs and on both selected full-answer cases (13/15). Explicit synchronization and a discarded complete-KV gather did not remove native divergence. Changing only the merge to FP32 also did not remove it. Complete-KV FP8 likewise matched across interleaves, but produced different answers from FP64. Agreement is distinct from answer correctness.
Rank-local FP32 attention/merge differed in only2/32768 layer0 BF16 values near rounding midpoints, yet its model generations diverged. An additional controlled perturbation of one layer0 BF16 value to its adjacent representable value changed subsequent generated wording while every attention computation used FP64; the input hashes matched, and both interleaves agreed under each perturbation. The parsed answer stayed unchanged in that isolated test.

A BF16-kernel intervention preserved the existing FP8-grid Q/KV values, selected physical rows, scales and DCP combination. It reduced same-input interleave output difference from0.397685% to0.261527% in a GPU replay, but did not remove serving divergence on cases13/15; both modes were exact within each configuration across two cold repeats. Case15 changed from I1/I64 answers125/2971 with native FP8 to29/125 with BF16 (label125). This selected subset is not an accuracy estimate or a proposed production fix.

All original GSM8K sequences were below top-k2048, so equal-score cutoff ambiguity above top-k cannot explain that corpus. The failed initial random prefill fixture had equal cutoff scores and was replaced with the independently ranked fixture described above. No causal/KV-addressing defect was found in the captured cases. Conditional checks against FP64 attention on each layer's own captured inputs gave maximum whole-output errors of0.9553% (I1) and2.4428% (I64); these do not assert equality of later model activations.

Actual combined outputs for all78 layers across the five traced modes/two interleaves, Q/KV bytes, indices and representative partials/LSE are retained in a verified archive, SHA256 272db45212eda347596ce069ced856b432dbca2ca923a7bb2aa01b75050e8206. Diagnostic interventions are isolated runtime code, not production changes in this PR. Raw artifacts are retained locally and are not attached here.

Graph-enabled server command (set CP_INTERLEAVE to1 or64, with the environment variables above and PYTHONHASHSEED=0):

PYTHONPATH=/work/source /work/.venv/bin/python -m vllm.entrypoints.cli.main serve \
  /models/models--Inferact--GLM-5.3-NVFP4/snapshots/ce67b36f3669192b5bb233819f0fda6c8a9837f8 \
  --served-model-name dcp-gsm8k-review --host 0.0.0.0 --port 8000 \
  --tensor-parallel-size 4 --decode-context-parallel-size 4 \
  --cp-kv-cache-interleave-size "$CP_INTERLEAVE" \
  --attention-backend FLASHINFER_MLA_SPARSE --kv-cache-dtype fp8 \
  --block-size 64 --enable-prefix-caching --max-model-len 16384 \
  --max-num-batched-tokens 8192 --max-num-seqs 32 \
  --gpu-memory-utilization 0.87 --distributed-executor-backend mp \
  --seed 0 --no-enable-log-requests

For the original evaluation protocol against either graph-enabled server:

PYTHONPATH=/work/source /work/.venv/bin/python \
  /work/source/tests/evals/gsm8k/gsm8k_eval.py \
  --host http://127.0.0.1 --port 8000 --num-questions 1319 --num-shots 5 \
  --max-tokens 1024 --temperature 0 --seed 0 --max-concurrency 32 \
  --save-results /work/gsm8k-summary.json

The repeated-run wrapper additionally freezes integer prompt IDs, asserts returned prompt IDs, sets per-repeat cache salts, pins kernel tactics, and saves every response. The helper command alone does not implement those additional controls.

AI assistance

OpenAI Codex assisted with code, GPU tests, investigation and PR preparation under the submitter's direction. Commits include attribution and sign-off. Human review of every changed line and the generic guard removal remains outstanding; this draft does not claim that the submitter personally reran the automated validation.

Permit the validated GLM sparse-attention layout: CUDA SM100, TP4/DCP4, PCP1, block/interleave 64, FP8 MLA and indexer caches, FLASHINFER_MLA_SPARSE, and model runner V2. Keep other unvalidated block-interleaved layouts rejected. Derive the MTP capability from the live configuration because NIXL finalizes the interleave after model construction. Restrict speculation to MTP with three draft tokens; preserve the no-speculation case. No NIXL protocol, transfer mapping, or attention kernel changes are included. Extend existing DCP tests for the allowlist, late configuration finalization, block-boundary causal masks, local/global top-k mapping, physical pages, and empty-shard handling. Validation on main 252ed87: pre-commit hooks, mypy 3.10/3.12, compileall, and git diff --check pass. These three files match the GPU-validated patch byte-for-byte. Prior validation on nightly d9105ea: 179 tests passed; 44/44 NIXL correctness cases passed; full GSM8K scored 1201/1319 local DCP4 versus 1197/1319 P/D DCP4, with 5276 successful rank transfers and zero request or transfer failures. This is not logit parity, and a full GPU rerun on the refreshed main base remains outstanding. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local>

Add an attention-layer GPU test using identical inputs for an unsharded TP1/DCP1 reference and a real four-process TP4/DCP4 candidate with block64 and interleave64. Exercise production FP8 indexer/cache kernels, global sparse top-k selection, FlashInfer TRTLLM sparse attention, and DCP query gathering and A2A output combination. Cover plain decode and MTP3 causal rows, empty local shards, shuffled physical blocks, top-k boundaries, two seeds, and non-unit FP8 scales. Retain output tensors and numerical metrics, require exact selected-index sets, and compare both paths with FP32 attention on the quantized inputs. Validation: all applicable pre-commit hooks, Python syntax compilation, and git diff --check pass. GPU execution remains pending; this commit does not establish numerical parity or extend backend support. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

Use FlashInferMLASparseImpl.lse_base_on_e for the production DCP manager and normalize captured LSE to natural-log units for the FP32 oracle. The initial harness hardcoded natural-log handling despite this backend's base-2 declaration, introducing an artificial output mismatch. Use the existing sparse-backend FP8 elementwise tolerance for TP1 versus DCP4 outputs. Check independent FP32 attention and DCP error per query using relative L2, since FP8 softmax rounding and cancellation amplify individual coordinates even when TP1 and DCP4 produce identical values. Retain exact selected-index comparisons, tight LSE checks, and raw metrics. GPU validation on Whitefiber Rack1 ps3, four GB200 GPUs: all 102 cases in tests/v1/attention/test_indexer_dcp_localize.py pass in 42.44s. The eight real-backend parity cases cover 164 query rows: zero index-set mismatches, maximum absolute output error 0.03125, maximum per-query relative L2 1.9392%, and maximum natural-log LSE error 9.5367e-7. Output elementwise tolerance is rtol=0.065, atol=0.05; per-query relative L2 is below 0.065. This validates sparse decode/MTP3 within FP8 tolerance, not bitwise equality, TRTLLM_RAGGED prefill, FLASHMLA_SPARSE, or NIXL transfers. Runtime: repository Python source plus unchanged C/CUDA artifacts from nightly d9105ea, PyTorch 2.13.0+cu130, FlashInfer 0.6.18, CUTLASS DSL 4.6.2. Source hashes match the GPU runtime. All applicable pre-commit hooks and git diff --check pass. No production code was changed. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

Compare real TP1/DCP1 and TP4/DCP4 attention outputs with block/interleave64 for all three GLM attention implementations under investigation. Reuse the distributed sparse test for FlashMLA's DS-MLA FP8 cache and ag_rs collectives, including its separate TP1 decode path. Add TRTLLM ragged dense prefill with real cache gather/reorganization, KV projection, causal suffixes, context continuation chunks, and output merging. Whitefiber Rack1 ps3, four GB200 GPUs: 24 numerical parity cases passed (eight per implementation), plus 101 existing tests. Sparse index sets matched exactly. Maximum absolute TP1/DCP4 output differences: FlashInfer 0.03125, FlashMLA 0.0078125, TRTLLM ragged 0.00390625. Independent FP32 attention checks also passed. All applicable pre-commit hooks passed. The first dense-prefill attempt hit a harness routing-metadata error before the kernel; calling the shared dense-prefill implementation fixed the test. Production backend/configuration guards are unchanged. FlashMLA's numerical test intentionally runs below its interleave/MTP configuration restrictions. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

…llowlist Delete the indexer's DCP/interleave rejection entirely and remove the GLM-specific allowlist and MTP capability override. The net production change is the original nine-line guard deletion; separate backend compatibility checks remain unchanged. Build real indexer metadata in the numerical tests so the TP4/DCP4 interleave64 comparison fails if the indexer guard is reintroduced. Remove the obsolete GLM allowlist tests. Validation on four GB200 GPUs: 79 tests passed in one invocation, including 24 actual attention-output parity cases across FlashInfer sparse MLA, FlashMLA sparse MLA, and TRTLLM ragged MLA prefill. The sparse cases also check exact index sets, FP8 scaling, causal MTP3 bounds, and natural-log LSE against TP1 and an independent FP32 oracle. All applicable manual-stage pre-commit hooks passed for changed files. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

github-actions · 2026-09-08T03:30:58Z

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State that the attention-output regression covers sparse decode/MTP, not sparse prefill or fused model-level cache population. Clarify that the FP32 oracle checks attention given the indexer's selected tokens; it is not an independent top-k oracle. This documentation-only change leaves the numerical test behavior and the generic indexer guard deletion unchanged. All applicable changed-file manual-stage pre-commit hooks passed. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

claude

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

Exercise ordinary prefill metadata and real FP8 indexer/MLA kernels at interleave 64 against complete TP1 KV. Use an exact position-based score ordering to assert causal top-k independently without ambiguous cutoff ties. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <>

Add a dedicated attention job for the sparse and TRTLLM ragged DCP4 parity tests. Check the hardware allocation before pytest so an incorrect runner cannot turn the required coverage into skipped tests. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Summer Yang <girasoleyang@gmail.com>

GirasoleY

Thank you for helping us adding the E2E tests!

I opened one change to add a 4 node B200 test setup as the current test gets skipped with < 4GPU nodes. feel free to merge this in: https://github.com/stu-cao/vllm/pull/1/changes

From the test results we can relax the guard. However your description mentioned a gsm8k score drop, could you confirm if it is an actual regression or just red herring?

Once both are addressed we are good to go.

[CI] Run MLA DCP parity tests on four B200 GPUs

stu-cao · 2026-09-08T23:29:13Z

@GirasoleY The GSM8K discrepancy seems to be a re-herring. I ran the test 4 times. interleave 64 has higher score twice while interleave 1 has higher score twice.


Run | Interleave1 correct /1319 | Interleave64 correct /1319 | I64 minus I1
-- | -- | -- | --
Original pair | 1206 (91.43%) | 1195 (90.60%) | −11
Controlled repeat0 | 1194 (90.52%) | 1217 (92.27%) | +23
Controlled repeat1 | 1205 (91.36%) | 1208 (91.58%) | +3
Controlled repeat2 | 1214 (92.04%) | 1204 (91.28%) | −10

LucasWilkinson

LGTM assuming @GirasoleY is happy with it now

LucasWilkinson · 2026-09-12T03:20:12Z

/ci run

github-actions · 2026-09-12T03:20:30Z

✅ Triggered Buildkite CI #88462 for commit db51037cbd67.

…onfig DSpark under PCP+DCP dies before it loads a single weight: pydantic_core.ValidationError: 1 validation error for ParallelConfig Value error, tp_size=1 must be divisible by dcp_size=8. DFlashSpeculator.__init__ (which DSparkSpeculator subclasses) collapses the PCP axis for the draft but leaves decode_context_parallel_size untouched. ParallelConfig._verify_args admits dcp in (1, pcp, tp * pcp) while PCP is on, and otherwise requires tp % dcp == 0, so tp=1/pcp=8/dcp=8 is legal for the target and invalid for the draft the moment pcp is forced to 1. Removing the PCP axis removes a factor of pcp from the KV sharding, so the draft keeps dcp // pcp: 1 where DCP spanned the PCP axis, tp where it spanned TP x PCP. Both land on a value the validator accepts. This is a plain-main bug. None of vllm-project#56157 / vllm-project#54496 / vllm-project#55802, nor the MTP capability commit, touches this file. MTP is unaffected because it shares the target model instead of deriving a draft parallel config. Found by running DSpark7 at PCP8+DCP8+EP8 behind NIXL P/D on H200, 2026-09-12. Fixes only the config derivation; whether DSpark then clears _validate_dspark_dcp_support at cp_kv_cache_interleave_size=64 is a separate question this unblocks testing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

…lm53-dcp-nixl-main-20260907 # Conflicts: # tests/v1/attention/test_indexer_dcp_localize.py Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

mergify · 2026-09-12T23:56:10Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @stu-cao.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

LucasWilkinson · 2026-09-13T11:44:49Z

/ci run

github-actions · 2026-09-13T11:45:06Z

✅ Triggered Buildkite CI #88634 for commit caa32555ccc1.

Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

LucasWilkinson · 2026-09-13T17:43:59Z

/ci retry

github-actions · 2026-09-13T17:44:18Z

✅ Triggered Buildkite CI #88669 for commit 10afd0150e6d, running 4 failed step(s) from Buildkite CI #88634.

Leave only the sparse indexer interleave guard deletion in the PR.\n\nCo-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

LucasWilkinson · 2026-09-13T20:56:07Z

/ci run

github-actions · 2026-09-13T20:56:25Z

✅ Triggered Buildkite CI #88683 for commit 2a8349861c39.

…arity (vllm-project#55802) Signed-off-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Signed-off-by: <> Signed-off-by: Summer Yang <girasoleyang@gmail.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: Stu Cao <stucao@Stus-MacBook-Pro.local> Co-authored-by: OpenAI Codex <codex@openai.com> Co-authored-by: Summer Yang <girasoleyang@gmail.com> Co-authored-by: Lucas Wilkinson <lwilkins@redhat.com> Co-authored-by: OpenAI Codex <noreply@openai.com>

Stu Cao and others added 4 commits September 7, 2026 15:28

mergify Bot added glm nvidia labels Sep 8, 2026

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stu-cao changed the title ~~[Attention] Add DCP interleave64 parity tests and scoped GLM enablement~~ [Attention] Remove DCP indexer interleave guard and test TP1 output parity Sep 8, 2026

stu-cao marked this pull request as ready for review September 8, 2026 05:06

stu-cao requested a review from pavanimajety as a code owner September 8, 2026 05:06

claude Bot reviewed Sep 8, 2026

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stu-cao marked this pull request as draft September 8, 2026 07:06

GirasoleY mentioned this pull request Sep 8, 2026

[CI] Run MLA DCP parity tests on four B200 GPUs stu-cao/vllm#1

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GirasoleY reviewed Sep 8, 2026

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Merge pull request #1 from GirasoleY/ci/pr55802-four-gpu-parity

db51037

[CI] Run MLA DCP parity tests on four B200 GPUs

mergify Bot added the ci/build label Sep 8, 2026

ywang96 added the ready ONLY add when PR is ready to merge/full CI is needed label Sep 8, 2026

stu-cao marked this pull request as ready for review September 8, 2026 23:21

stu-cao requested review from Harry-Chen and khluu as code owners September 8, 2026 23:21

stu-cao requested a review from GirasoleY September 9, 2026 15:20

LucasWilkinson approved these changes Sep 12, 2026

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Merge remote-tracking branch 'origin/main' into t3code/pr-55802/fix/g…

caa3255

…lm53-dcp-nixl-main-20260907 # Conflicts: # tests/v1/attention/test_indexer_dcp_localize.py Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

mergify Bot added needs-rebase and removed needs-rebase labels Sep 12, 2026

Fix sparse DCP test setup after HiSparse merge

10afd01

Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

This was referenced Sep 13, 2026

[Bugfix][PCP][DCP] Respect interleave in indexer KV gather mapping #56715

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[NIXL][PCP][DCP] Expose PCP producer KV shards as transfer ranks #56645

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[Test] Remove MLA DCP attention parity coverage

2a83498

Leave only the sparse indexer interleave guard deletion in the PR.\n\nCo-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

LucasWilkinson enabled auto-merge (squash) September 13, 2026 20:56

mgoin approved these changes Sep 13, 2026

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vllm-bot merged commit b234330 into vllm-project:main Sep 13, 2026
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github-project-automation Bot moved this from Ready to Done in NVIDIA Sep 13, 2026

LucasWilkinson mentioned this pull request Sep 13, 2026

[PCP][DCP] Declare FlashMLASparse MTP support at CP interleave > 1 #56722

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stu-cao commented Sep 8, 2026 •

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