[Qwen3.8-Flash-Next] Improve QSA sparse GQA for prefill and short-ctx decode by gau-nernst · Pull Request #54873 · vllm-project/vllm

gau-nernst · 2026-09-02T02:50:36Z

Purpose

QSA sparse attention read topk indices from [num_tokens, 2048+3] buffer, where each token can attend up to 2048+3 past KV tokens. This number can be smaller when a new token's position is less than 2048, hence the indices buffer might be padded with -1. This is true for fresh prefill (causal attention) requests and short-context decode.

In the current code, sparse GQA always attend to the full 2048+3 indices (with appropriate masking of course). This is wasteful when there is a lot of padding like mentioned above. To fix this issue, we insert a variable valid_counts, representing the total number of valid, non-padding indices, at the end of the logical_indices table. Why this design?

The valid counts always go together with the indices table. This can be seen as an encoding of variable length sequences with stable buffer size.
This also works nicely with topk indices reuse in MTP compact_topk_indices -> we don't need to carry another tensor and ensure it's in-sync with the indices table

The key change in the kernel is basically this (notice the loop's upper bound is tightened by valid_counts)

valid_count = tl.load(indices_ptr + row * stride_indices_row + TOPK)
tile_end = tl.minimum(NUM_TILES, tl.cdiv(tl.minimum(valid_count, TOPK), BLOCK_N))
for tile in range(split_id, tile_end, NUM_SPLITS):
   ...

Other features included in this PR

Add proper warmup logic
Add a tuned config for prefill case

Microbenchmark

Prefill (no prefix cache context)

shape	tp1 before	tp1 after	speedup	tp4 before	tp4 after	speedup
1x1024	459.1	144.1	3.19x	319.9	81.6	3.92x
1x4096	1725.7	1268.6	1.36x	1040.5	603.3	1.72x
2x8192	6716.9	5745.1	1.17x	3678.8	2563.7	1.43x
1x16384	6820.3	6132.7	1.11x	4012.9	2732.3	1.47x

The speedup is most significant for short context, where the first 2048 tokens can skip most of the work.

Decode/Spec-decode

tp1:

N	DQL	ctx=1024	ctx=2048	ctx=8192	ctx=51200
1	1	11.9→10.0	12.7→10.4	12.9→10.6	12.8→10.6
1	4	15.1→11.9	16.0→13.2	16.9→14.1	17.2→15.1
4	1	15.5→12.8	17.3→14.5	17.6→15.1	17.6→15.6
4	4	20.2→15.8	23.1→20.6	26.8→24.3	29.9→27.2
16	1	21.4→17.5	26.7→24.3	29.2→26.6	30.1→27.8
16	4	53.1→29.4	57.9→52.2	67.3→62.2	73.0→67.0
64	1	58.7→37.3	67.4→62.5	72.4→66.2	74.5→68.6
64	4	154.3→98.8	174.3→177.1	195.9→197.8	205.6→206.3
256	1	167.1→112.4	196.1→197.3	204.2→206.0	208.2→209.3
256	4	485.4→271.6	537.1→547.7	633.9→628.7	674.6→671.8

tp4:

N	DQL	ctx=1024	ctx=2048	ctx=8192	ctx=51200
1	1	9.5→8.5	10.8→8.7	10.9→8.9	11.1→9.0
1	4	11.4→9.7	12.0→10.0	12.9→10.7	13.2→11.3
4	1	11.7→10.1	12.8→10.7	13.0→10.9	13.3→11.4
4	4	19.7→13.8	20.0→17.1	20.8→17.9	21.4→18.5
16	1	20.1→13.6	20.7→17.5	21.3→18.7	21.4→18.6
16	4	31.1→19.0	35.8→29.9	42.0→37.6	46.3→41.8
64	1	32.1→22.6	42.1→37.4	45.4→41.0	46.9→42.4
64	4	82.0→50.5	92.4→90.7	104.7→106.1	111.1→113.2
256	1	90.6→61.1	104.7→106.3	110.7→112.1	113.4→114.7
256	4	312.2→155.7	338.3→309.4	375.3→354.5	392.7→374.1

Similarly, the speedup is most significant for short context.

E2E perf

TP4 GB300, BF16 ckpt, no MTP

conc	tok/s before → after	Δ	TTFT p50 before → after	Δ	TPOT p50 before → after	Δ
1	1901.7 → 1901.1	−0.0%	155.1 → 157.1 ms	+1.2%	4.58 → 4.58 ms	−0.1%
4	5388.9 → 5377.1	−0.2%	433.9 → 398.4 ms	−8.2%	6.26 → 6.27 ms	+0.1%
16	13213.6 → 13376.0	+1.2%	592.1 → 582.3 ms	−1.7%	10.31 → 10.16 ms	−1.4%
64	27246.9 → 27756.2	+1.9%	661.7 → 626.1 ms	−5.4%	20.39 → 19.97 ms	−2.0%

Good improvements in TTFT, except at concurrency 1 which is probably noisy.

Test Plan

TP4 GB300, BF16 ckpt, MTP3

GSM8K (no think): 0.9678
MMMU Pro (thinking): 0.7694

Test Result

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… extent The QSA sparse attention kernel now always runs each row's tile loop only up to that row's valid selection extent, derived in-kernel from the QSA side-cache metadata (logical_positions + seq_lens, the latter indexed via the row's token_to_req) as min(min((pos+1)//R, seq//R), BLOCK_TOPK)*R + (pos+1)%R — bit-identical to the expand kernel's expanded_count + tail_count. No clipped/unclipped kernel split: the bound is a no-op for full-budget rows, and its register-pressure cost is absorbed by re-tuning the config table under the always-on bound (the earlier forced-clip penalty at decode was a tuning artifact — retuned configs match or beat the old table at every decode cell). Splits now walk tiles strided ({s, s+S, ...}), which damps the wave-quantization cliff and is locality-neutral because selections are score-rank-ordered, not position-ordered. The bound only ever grows as positions/seq lens advance, so MTP skip_topk steps that reuse step-0 indices stay safe without any cross-step buffer pairing. The config table's top region (bp > 2048) splits on long_query = max_query_len > 1 + num_spec, which is capture-stable (max_query_len is the uniform decode/verify length at capture and replay alike), so long prefill/mixed chunks keep the narrow no-split config while huge uniform verify batches keep the wide one. All production-reachable split-K/merge specializations are pre-compiled at startup via warmup_qsa_sparse_paged_attention. Also widen the split-K partial-slot addressing to int64 in the splitk store and merge load: (split * num_rows + row) * NUM_QUERY_HEADS * HEAD_DIM overflowed int32 past ~2^31 partial elements (found by compute-sanitizer during config sweeps at 128 splits x 5120 rows; not reachable with the shipped table, which caps splits at 64 and splits>1 at <=2048 base programs). Measured on GB300 at Qwen3.8-Flash-Next TP1/TP2/TP4 shapes: prefill speedup 1.05-3.6x vs unclipped depending on shape (3.6x at 1x1k TP4). Retuned decode table beats the old one on average (-4.9% over a 65-cell B x query-length grid). Decode is no longer bitwise vs upstream (strided accumulation order); outputs match the fp32 reference at 2e-2. Co-authored-by: Kimi <noreply@moonshot.cn> Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

The bp>2048 region of the sparse-GQA config table splits on is_prefill: prefill/mixed batches take the narrow no-split profile, uniform decode/verify keeps the wide entry. Measured driver is the per-row work distribution (ragged causal extents vs uniform full budget); no single config serves both within ~3%, and no host scalar reproduces the split capture-safely, so the wrapper takes the plain is_prefill bool and the mechanism stays a comment next to the table. Capture-stable: at FULL-graph capture max_query_len is the uniform decode/verify length by construction, identical at replay. Co-authored-by: Kimi <noreply@moonshot.cn> Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

coderabbitai · 2026-09-04T00:42:08Z

📝 Summary

Summary by CodeRabbit

Performance
- Improved Qwen4 sparse attention performance for both prompt processing and token generation.
- Enhanced handling of varied context lengths, including short and full-context requests.
- Improved compatibility across supported cache page sizes and multi-head configurations.
Stability
- Expanded kernel warmup coverage for supported Qwen4 attention workloads.
- Improved sparse attention indexing reliability across different request shapes and context lengths.

Walkthrough

The QSA path now uses packed selection buffers with a trailing valid-entry count. Sparse attention consumes this count, selects prefill-aware configurations, and receives packed-width buffers from the QSA layer. Warmup and correctness tests cover the updated execution paths.

Changes

QSA packed sparse attention

Layer / File(s)	Summary
Packed selection-buffer contract `vllm/models/qwen4_exp/nvidia/indexer_qsa.py`, `vllm/models/qwen4_exp/nvidia/ops/qsa_indexer.py`, `tests/models/qwen4_exp/test_qsa_reference.py`	The selection buffer adds a trailing valid-entry count column. Allocation, validation, expansion, fallback handling, and tests use the packed width.
Packed sparse-kernel execution `vllm/models/qwen4_exp/nvidia/ops/qsa.py`, `vllm/models/qwen4_exp/nvidia/qsa.py`	Sparse attention reads per-row counts, limits tile processing, uses int64 row offsets, and selects configurations with `use_prefill_config`.
Prefill-aware QSA integration `vllm/models/qwen4_exp/nvidia/qsa.py`, `vllm/models/qwen4_exp/nvidia/indexer_qsa.py`, `tests/models/qwen4_exp/test_qsa_reference.py`	The QSA layer allocates packed buffers and passes the renamed configuration selector. Tests cover prefill selection, production page sizes, and mixed request contexts.
Sparse-kernel warmup wiring `vllm/model_executor/warmup/qwen4_exp_qsa_warmup.py`, `vllm/models/qwen4_exp/nvidia/ops/qsa.py`	Warmup resolves the QSA attention layer and cache block tables, then compiles sparse attention configurations for prefill and decode paths.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🟡 Moderate · up to 37d3e

The QSA performance changes may leave sparse attention unable to compile for affected prefill and decode workloads. Replace the runtime Python range with Triton's runtime iterator or establish compatibility before merging.

Suggested reviewers: peakcrosser7

Sequence Diagram(s)

sequenceDiagram
  participant Qwen4ExpQSAAttention
  participant QSAIndexer
  participant qsa_sparse_paged_attention
  participant KVCache
  Qwen4ExpQSAAttention->>QSAIndexer: request packed selection indices
  QSAIndexer->>Qwen4ExpQSAAttention: return entries and valid-entry counts
  Qwen4ExpQSAAttention->>qsa_sparse_paged_attention: pass packed indices and use_prefill_config
  qsa_sparse_paged_attention->>KVCache: read selected K/V blocks
  KVCache->>qsa_sparse_paged_attention: return K/V data
  qsa_sparse_paged_attention->>Qwen4ExpQSAAttention: return attention output

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 37.50% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 24 functions across 6 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly summarizes the main change: improving QSA sparse GQA for prefill and short-context decode. It is specific and concise.
Description check	✅ Passed	The description directly explains the valid-count optimization, warmup logic, prefill configuration, benchmarks, and test results for the changeset.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

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gau-nernst · 2026-09-04T08:35:28Z

/ci run

github-actions · 2026-09-04T08:35:46Z

✅ Triggered Buildkite CI #87223 for commit 09fa1f359762.

Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

gau-nernst · 2026-09-04T10:23:41Z

/ci run

github-actions · 2026-09-04T10:24:22Z

✅ Triggered Buildkite CI #87237 for commit 37d3e21fe316.

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⚠️ Outside diff range comments (1)

vllm/models/qwen4_exp/nvidia/ops/qsa.py (1)
85-85: 🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

Use tl.range for the runtime tile bounds.

split_id and tile_end are runtime Triton values. Python range cannot consume these values. Replace it with tl.range; otherwise Triton compilation can fail.
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In `@vllm/models/qwen4_exp/nvidia/ops/qsa.py` at line 85, Update the tile
iteration loop to use Triton’s tl.range instead of Python range, passing the
runtime split_id and tile_end bounds while preserving NUM_SPLITS as the step.
Source: MCP tools

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Outside diff comments:
In `@vllm/models/qwen4_exp/nvidia/ops/qsa.py`:
- Line 85: Update the tile iteration loop to use Triton’s tl.range instead of
Python range, passing the runtime split_id and tile_end bounds while preserving
NUM_SPLITS as the step.

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tests/models/qwen4_exp/test_qsa_reference.py
vllm/models/qwen4_exp/nvidia/indexer_qsa.py
vllm/models/qwen4_exp/nvidia/ops/qsa.py
vllm/models/qwen4_exp/nvidia/ops/qsa_indexer.py
vllm/models/qwen4_exp/nvidia/qsa.py

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Accept --kv-cache-dtype fp8/fp8_e4m3 for the QSA full-attention owner. The cache is allocated as uint8 and written by reshape_and_cache_flash with the layer's per-tensor scales (unchanged write path); the sparse split-K Triton kernel reinterprets the bytes as e4m3, dequantizes K/V to bf16 on load and folds the K and V scales into the logits and the (partial) output. The bf16 branch is compiled out unchanged (IS_FP8=False). Indexer and ring side caches stay bf16; their backend only accepts the fp8 strings so engine validation passes when the main cache is fp8. The impl hands the parent FlashAttention constructor "auto" because flash-attn's fp8 probe rejects sm120 while QSA never runs flash-attn over the cache. The warmup compiles the fp8 variants. Scope is deliberately fp8-only (see the scoping discussion on vllm-project#54846); nvfp4 can follow separately. Design follows the gist patch validated on the preview build in vllm-project#53896's thread, ported to the post-vllm-project#54873/vllm-project#54915 kernels. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Andrea Semeraro <andrea.semeraro@sezione1.it>

… decode (vllm-project#54873) Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg> Co-authored-by: Kimi <noreply@moonshot.cn> Signed-off-by: Jyotirmoy Roy <jyotirmoyroy649@gmail.com>

mergify Bot added the qwen Related to Qwen models label Sep 2, 2026

gau-nernst force-pushed the qwen38next-qsa-prefill-on-split branch 14 times, most recently from 0ad652b to c59494f Compare September 3, 2026 04:44

gau-nernst and others added 10 commits September 4, 2026 00:37

clean up

763517d

Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>

clean

67f39eb

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remove unnecessary canonicalize_singleton_dim_strides

1e13532

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prune

b199ce0

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prune

c196269

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update tests

42ad06d

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update test

11cf9f5

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encode valid_counts in the same topk buffer

1fd46f4

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gau-nernst changed the title ~~[Qwen3.8-Flash-Next] Tune QSA sparse GQA for prefill~~ [Qwen3.8-Flash-Next] Improve QSA sparse GQA for prefill and short-ctx decode Sep 4, 2026

update comment

5647125

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gau-nernst force-pushed the qwen38next-qsa-prefill-on-split branch from a0c3b36 to 5647125 Compare September 4, 2026 00:42

add do_not_specialize

835808d

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update tuning config

09fa1f3

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gau-nernst marked this pull request as ready for review September 4, 2026 08:34

gau-nernst requested review from AndreasKaratzas, DarkLight1337 and ywang96 as code owners September 4, 2026 08:34

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Comment thread vllm/models/qwen4_exp/nvidia/qsa.py Outdated

gau-nernst added 2 commits September 4, 2026 10:22

rename variable

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Merge branch 'main' into qwen38next-qsa-prefill-on-split

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vllm-bot merged commit 31a8a26 into vllm-project:main Sep 4, 2026
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gau-nernst deleted the qwen38next-qsa-prefill-on-split branch September 4, 2026 13:10

jschmied mentioned this pull request Sep 4, 2026

[RFC]: Tile-union QSA sparse attention for prefill (Qwen3.8-Flash-Next): share the K/V gather across neighbouring query rows, −4.7 % TTFT on GB10 #55394

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This was referenced Sep 4, 2026

[Bugfix][SM70] Make QSA page4 ordering independent of KV allocation Leonccaa/1Cat-vLLM#7

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[Bugfix][SM70] Stabilize QSA page4 order under KV relocation 1CatAI/1Cat-vLLM#494

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jschmied mentioned this pull request Sep 5, 2026

[Kernel][Qwen3.8-Flash-Next] Tile-union QSA sparse attention for prefill on SM121 #55430

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semerandre mentioned this pull request Sep 6, 2026

[Model] Qwen4Exp: fp8_e4m3 main KV cache on the QSA path #55557

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[Qwen3.8-Flash-Next] Improve QSA sparse GQA for prefill and short-ctx decode - #54873

[Qwen3.8-Flash-Next] Improve QSA sparse GQA for prefill and short-ctx decode#54873
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Purpose

Microbenchmark

Prefill (no prefix cache context)

Decode/Spec-decode

E2E perf

Test Plan

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