[Perf][Attention] Reduce GLM sparse MLA preparation overhead by GirasoleY · Pull Request #57458 · vllm-project/vllm · GitHub
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[Perf][Attention] Reduce GLM sparse MLA preparation overhead - #57458

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GirasoleY:codex/glm53-sparse-mla-prep
Sep 22, 2026
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@GirasoleY

@GirasoleY GirasoleY commented Sep 18, 2026 •

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Purpose

Reduce query packing and sparse-index preparation overhead around FlashInfer NoPE MLA attention.

The three commits:

  1. Reuse a contiguous NoPE query when its rotary tail is empty, avoiding a redundant torch.cat before FP8 quantization. Preserve concatenation for nonempty rotary dimensions and strided queries.
  2. Use one padded remap program per query for counted sparse capacities up to 4096. Initialize padding and reduce valid counts inside the remap kernel, removing separate fills and atomic accumulation. Preserve larger tiled cases and reusable buffers. Name the device entry point sparse_mla_index_remap_kernel for trace attribution.
  3. Simplify empty-query handling: fuse dummy-entry preparation and safe-length calculation while preserving raw counts; skip preparation and masking for resident TP; mask only LSE for DCP and let its existing merge suppress zero-weight outputs, including NaNs. Preserve PyTorch output masking for HiSparse without LSE. There is no custom output-zeroing kernel.

Duplicate-work checks found no matching open PR. Related PRs #54258 (separate ROCm remap kernel), #38354 (DeepSeek pre-attention GEMM/RoPE fusion), and #57161 (GLM kpool/indexer scheduling) address different kernels or stages. No existing issue is being claimed or closed.

Correctness

Prior TP validation on the experiment stack used GLM-5.3-Flash, TP8+EP8, all custom ops enabled, eager execution, no prefix cache, and exactly 32768 input tokens with either 1 or 16 generated tokens. Across 12 repeatability requests, all 8976 sparse-layer calls across eight ranks had positive valid counts (minimum 1). A direct full-shape FP8 attention comparison at 32768 queries × 8 heads × 512 dimensions, capacity 2176, matched the previous implementation bit for bit.

Performance

Controlled end-to-end comparison on four GB300 GPUs, TP4+EP4, eager, custom_ops=["all"], no prefix cache, no speculative decoding, and FP8 E4M3 KV cache. The actual upstream PR model stayed loaded while an idle-worker extension switched only the three preparation changes to their PR-base implementations. All four ranks verified the implementation selection. Compatible prebuilt extensions were reused.

Both implementations were warmed, then measured in four alternating baseline/PR and PR/baseline rounds with five matched samples per workload per round (20 pairs each). Each request had exactly 32768 input tokens, with a 32768-token prefill scheduling limit. Prefill generated one token; decode generated 128 tokens. These are client-observed serving latencies.

Metric PR base PR Latency reduction
32K prefill TTFT, median 518.25 ms 507.61 ms 2.05%
Decode inter-token latency, median 122.85 ms/token 120.77 ms/token 1.70%

All 20 pairs improved for each workload. Per-round median reductions ranged from 1.78–2.27% for prefill and 1.21–1.98% for decode. These measurements apply to eager TP4 execution; CUDA-graph decode and other parallel configurations were not measured in this comparison.

Earlier isolated measurements on the same GB300 GPU, using CUDA graph replay and the median of five samples with 40 repetitions/sample:

Operation Before Optimized
Remap including initialization 531.90 µs 282.93 µs
Safe lengths and dummy entry 12.32 µs 4.12 µs

Shape: 32768 queries × 8 heads × 512 dimensions, capacity 2176. These are preparation microbenchmarks from the experiment stack. Resident TP now skips safe-length preparation entirely. Earlier tracing also confirmed removal of all 11 redundant query concatenations. The final upstream revision has not been reprofiled; the controlled end-to-end results above are separate from these older microbenchmarks. Measurements of the removed output-zeroing kernel are excluded.

@GirasoleY
GirasoleY force-pushed the codex/glm53-sparse-mla-prep branch 3 times, most recently from 1d499d7 to 2221877 Compare September 18, 2026 01:51
GirasoleY and others added 2 commits September 17, 2026 18:55
Reuse a contiguous query when its rotary tail is empty. Preserve the
concatenation fallback for nonempty rotary dimensions and strided queries.
Cover both custom-op dispatch modes and FP8 value preservation.

Co-authored-by: Codex <noreply@openai.com>
Signed-off-by: Summer Yang <girasoleyang@gmail.com>
Use one padded program per query for counted sparse capacities up to
4096, including GLM's 2176 entries. Initialize padding and store counts in
the remap kernel, removing two fills and atomic accumulation. Preserve the
larger multi-tile fallback. Name the device entrypoint
sparse_mla_index_remap_kernel for trace attribution.

Extend remap and DCP reference tests for 2176 and the 4224-entry fallback.

Co-authored-by: Codex <noreply@openai.com>
Signed-off-by: Summer Yang <girasoleyang@gmail.com>

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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.

indices_ptr,
counts_ptr,
safe_lengths_ptr,
NUM_TOKENS: tl.constexpr,

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This will trigger JIT recompilation

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@ZJY0516 ZJY0516 left a comment

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LGTM

@github-project-automation github-project-automation Bot moved this to Ready in NVIDIA Sep 18, 2026
@ZJY0516 ZJY0516 added the ready ONLY add when PR is ready to merge/full CI is needed label Sep 18, 2026
Fuse dummy-entry preparation and safe-length calculation while preserving
raw valid counts. Resident TP skips this work and all empty-query masking.
DCP masks only empty-query LSE to -inf; its existing merge suppresses
zero-weight outputs, including NaNs. Preserve PyTorch output masking for
HiSparse without LSE.

Validate native BF16/FP8 attention and DCP merge behavior, safe lengths,
strided storage, remapping, and query packing with focused regression cases.

Co-authored-by: Codex <noreply@openai.com>
Signed-off-by: Summer Yang <girasoleyang@gmail.com>
@GirasoleY
GirasoleY force-pushed the codex/glm53-sparse-mla-prep branch from 6728a18 to 736f29a Compare September 18, 2026 03:20
@GirasoleY

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/ci run

@ZJY0516

ZJY0516 commented Sep 21, 2026

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/ci run --allow-stale

@ZJY0516
ZJY0516 enabled auto-merge (squash) September 21, 2026 08:23
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@vllm-bot
vllm-bot merged commit a369a7b into vllm-project:main Sep 22, 2026
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@github-project-automation github-project-automation Bot moved this from Ready to Done in NVIDIA Sep 22, 2026
michael-gpu pushed a commit to practical-inference/vllm-ampere-extended that referenced this pull request Oct 20, 2026
…oject#57458)

Signed-off-by: Summer Yang <girasoleyang@gmail.com>
Co-authored-by: Codex <noreply@openai.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
Co-authored-by: Jiangyun Zhu <riverclouds.zhu@qq.com>
mgehre-amd pushed a commit to ROCm/vllm that referenced this pull request Oct 20, 2026
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* [docs] Fix legacy hf CLI references (vllm) (vllm-project#57958)

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glm nvidia ready ONLY add when PR is ready to merge/full CI is needed

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