[TurboQuant] enable FA3/FA4 for prefill paths by huangzhilin-hzl · Pull Request #40092 · vllm-project/vllm

huangzhilin-hzl · 2026-04-17T03:59:41Z

Purpose

Resolves part of #40069 (Backend Coverage: extend flash_attn_varlen_func support to FA3/4).

Two issues fixed:

FA version passthrough: TurboQuant prefill paths call flash_attn_varlen_func without the fa_version kwarg, so on Hopper (SM90) the call defaults to FA2 instead of leveraging FA3, and on Blackwell (SM100) it misses FA4 entirely. The standard FlashAttention backend already detects and passes fa_version at init time; this PR aligns TurboQuant to the same pattern.
Mixed-backend assert fix: _get_sliding_window_configs() in flash_attn.py asserts all Attention layers are FlashAttentionImpl. When kv_cache_dtype_skip_layers routes some layers to a different backend (e.g. TurboQuant), this assert fails. Fixed by skipping non-FA layers, since they use their own metadata builders.

Test Plan

# 1. Unit tests
python -m pytest tests/quantization/test_turboquant.py -v

# 2. GSM8K correctness eval (all 4 TQ presets)
python -m pytest -s -v tests/evals/gsm8k/test_gsm8k_correctness.py \
    --config-list-file=tests/evals/gsm8k/configs/models-turboquant.txt

# 3. E2E inference with CUDAGraph (no enforce_eager, validates assert fix)
CUDA_VISIBLE_DEVICES=0 HF_HUB_OFFLINE=1 python -c "
from vllm import LLM, SamplingParams
for dtype in ['turboquant_k8v4', 'turboquant_3bit_nc']:
    llm = LLM(model='Qwen/Qwen3-4B', kv_cache_dtype=dtype,
              max_model_len=2048, gpu_memory_utilization=0.5)
    outputs = llm.generate(['What is 2+2?'], SamplingParams(max_tokens=32))
    print(f'{dtype}: {outputs[0].outputs[0].text[:80]}')
    del llm
"

Test Result

Hardware: NVIDIA H20 (SM90 / Hopper)

FA version detection

FA version for head_size=128: 3   (was: unspecified, defaulting to FA2)
FA version for head_size=256: 3

Unit tests

114 passed, 6 failed (pre-existing rotation matrix atol issues, unrelated)

Confirmed pre-existing: same 6 failures on unmodified code via git stash / re-run.

E2E inference with CUDAGraph (enforce_eager=False)

Validates both the FA3 passthrough and the assert fix (AOT schedule path is entered).

Preset	CUDAGraph Capture	Result
k8v4	51 piecewise + 51 full	PASSED
t3nc	51 piecewise + 51 full	PASSED

GSM8K correctness eval (Qwen3-4B, 1319 questions, 5-shot)

Preset	Accuracy	Threshold	Result
k8v4 (FP8 key + 4-bit value)	-	>= 0.80	PASSED
t4nc (4-bit MSE + NC)	-	>= 0.80	PASSED
k3v4nc (3-bit key + 4-bit value + NC)	-	>= 0.78	PASSED
t3nc (3-bit all + NC)	0.7574	>= 0.75	PASSED

Note: t3nc failed in batch run due to GPU memory from zombie processes, passed when run alone.

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

github-actions · 2026-04-17T03:59:51Z

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Code Review

This pull request removes the --enforce-eager flag from several GSM8K evaluation configurations and updates the FlashAttention backend to skip non-FlashAttention layers during sliding window configuration retrieval. It also introduces FlashAttention version detection within the TurboQuant backend to support different prefill paths. Feedback was provided to include the requires_alibi argument in the version detection logic to ensure proper fallback behavior when ALiBi slopes are present.

gemini-code-assist · 2026-04-17T04:00:57Z

@@ -271,6 +272,9 @@ def __init__(
        self._val_data_bytes = math.ceil(head_size * cfg.effective_value_quant_bits / 8)
        self._n_centroids = cfg.n_centroids if not cfg.key_fp8 else 1

+        # Detect flash-attn version (FA2/3/4) for prefill paths.
+        self.fa_version = get_flash_attn_version(head_size=head_size)


The call to get_flash_attn_version should include the requires_alibi argument. Passing requires_alibi=alibi_slopes is not None ensures that the backend correctly falls back to FlashAttention 2 if ALiBi slopes are present, as FA3 and FA4 do not currently support them. This maintains consistency with the version detection logic used in FlashAttentionImpl.

Suggested change

self.fa_version = get_flash_attn_version(head_size=head_size)

self.fa_version = get_flash_attn_version(

requires_alibi=alibi_slopes is not None, head_size=head_size)

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chatgpt-codex-connector · 2026-04-17T04:06:09Z

+        # Detect flash-attn version (FA2/3/4) for prefill paths.
+        self.fa_version = get_flash_attn_version(head_size=head_size)


Mirror SM90 head_dim>256 FA4 override in TurboQuant

This new FA-version selection path only calls get_flash_attn_version(head_size=...), but it does not apply the SM90 head_size > 256 upgrade to FA4 that FlashAttentionImpl already uses. On Hopper, get_flash_attn_version still defaults to FA3, so TurboQuant prefill can be routed into FA3 with unsupported large head dimensions and fail at runtime for those models. Please mirror the same SM90/head-size override logic before assigning self.fa_version.

Useful? React with 👍 / 👎.

Three fixes to let TurboQuant use FA3 on Hopper and FA4 on Blackwell: 1. Detect flash-attn version at init via get_flash_attn_version() and pass fa_version= to all three flash_attn_varlen_func call sites (batch prefill, per-request prefill, continuation prefill). 2. Relax _get_sliding_window_configs() assert so it skips non-FA layers (e.g. TurboQuant, MLA) instead of asserting all layers are FlashAttentionImpl. Other backends use their own metadata builders. 3. Remove --enforce-eager from TQ eval configs — no longer needed as a workaround now that FA3/CUDAGraph works with TQ. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

huangzhilin-hzl · 2026-04-20T13:02:09Z

@vibhavagarwal5 @mgoin
I also ran a focused FA3 retest on a single H20 by applying the equivalent of this change on top of the hybrid TurboQuant branch from #39931. Detailed benchmark commands can be found in this comment.

Workload	Config	FA2 Req/s	FA3 Req/s	Req/s Δ	FA2 TTFT (ms)	FA3 TTFT (ms)	TTFT Δ
prefill_heavy	turboquant_4bit_nc	1.712	2.939	+71.69%	7264.19	4070.06	-43.97%
prefill_heavy	turboquant_k8v4	1.468	2.781	+89.51%	9231.23	4242.39	-54.04%
prefill_heavy	turboquant_k3v4_nc	1.561	2.609	+67.15%	7817.76	4349.17	-44.37%
prefill_heavy	turboquant_3bit_nc	1.576	2.096	+32.99%	7799.79	6560.09	-15.89%
long_balanced	turboquant_4bit_nc	0.788	1.185	+50.48%	7519.04	2861.31	-61.95%
long_balanced	turboquant_k8v4	0.768	1.220	+58.79%	8340.68	3039.84	-63.55%
long_balanced	turboquant_k3v4_nc	0.704	1.052	+49.34%	7823.62	2843.31	-63.66%
long_balanced	turboquant_3bit_nc	0.723	1.058	+46.40%	7586.00	2839.00	-62.58%

Would appreciate a review when you have a chance.

vibhavagarwal5 · 2026-04-20T13:48:20Z

What about baseline FA3 @huangzhilin-hzl do add that as well in the same table. this is good

jhsmith409 · 2026-04-20T16:50:44Z

Hardware-support note from a Blackwell-consumer run

Tried this PR on RTX 5090 (sm_120, Blackwell consumer) stacked on top of JartX#10 (hybrid TurboQuant + #40074 overlay). Two findings worth flagging:

1. #39931's arg_utils.py still forces FA2.
While this PR fixes the assert in _get_sliding_window_configs — exactly the reason the override was added in #39931 — the override is unconditional and not removed. On a TurboQuant run today we still see:

WARNING [arg_utils.py:1968] TurboQuant is not yet compatible with FlashAttention >= 3.
        Overriding flash_attn_version to 2. To silence this warning,
        pass --attention-config.flash_attn_version=2

So turboquant_attn.py's new self.fa_version = get_flash_attn_version(head_size=head_size) resolves to 2 on any stack with #39931. The two PRs should probably land in coordination: once this one is merged, #39931's override in arg_utils.py (~lines 1962-1973) can be dropped.

2. Consumer Blackwell (sm_120) has no FA3/FA4 in the shipped flash-attn wheel.
Even with the override removed locally, the version probe stays at 2:

>>> from vllm.vllm_flash_attn.flash_attn_interface import is_fa_version_supported, fa_version_unsupported_reason
>>> for v in (2, 3, 4): print(v, is_fa_version_supported(v), fa_version_unsupported_reason(v))
2 True None
3 False FA3 is only supported on devices with compute capability 9.x
4 False FA4 is only supported on devices with compute capability 9.x, 10.x, or 11.x

get_flash_attn_version() also only picks FA4 when device_capability.major == 10. sm_120's major is 12, so RTX 50-series consumers fall through to the FA2 branch regardless. Not this PR's bug — just worth calling out that this PR's speedup applies to H100/H200 (sm_90) and datacenter Blackwell (sm_100, B200) but is a provable no-op on RTX 5090-class hardware in the current flash-attn build.

3. Bench, for the record.
4 k-token prompt, 8-token decode, RedHatAI/Qwen3.6-35B-A3B-NVFP4 + turboquant_k8v4, torch.compile + cudagraph, RTX 5090:

concurrency	prefill tok/s (with override, FA2)	prefill tok/s (override removed, still FA2)
1	22 943	22 506
2	26 263	26 448
4	25 967	29 989

Differences are within run-to-run noise at this concurrency; no regression from applying the PR. Applies cleanly on top of #39931 once the arg_utils override is relaxed.

(AI-assisted verification run; human submitter reviewed all edits and both A/B configurations.)

mergify · 2026-04-21T05:53:00Z

Hi @huangzhilin-hzl, the pre-commit checks have failed. Please run:

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mergify · 2026-04-21T07:07:00Z

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vibhavagarwal5 · 2026-04-22T07:50:28Z

@huangzhilin-hzl pls check why the CI is failing and fix it.

Co-authored-by: Codex <codex@openai.com> Signed-off-by: 墨楼 <huangzhilin.hzl@antgroup.com>

…upport

Signed-off-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Codex <codex@openai.com> Signed-off-by: Avinash Singh <avinashsingh.rcoem@gmail.com>

vLLM v0.20.0 (released 2026-04, two days before this commit) ships TurboQuant as a v1 attention backend via PRs: - vllm-project/vllm#38479 '[Attention Backend] TurboQuant: 2-bit KV cache compression with 4x capacity' (2963/3 LoC; merged) - vllm-project/vllm#40092 'FA3/FA4 prefill support for TurboQuant' Activated upstream via: pip install 'vllm>=0.20.0' vllm serve <model> --kv-cache-dtype turboquant_k8v4 # 2.6x, FP8K + 4-bit V vllm serve <model> --kv-cache-dtype turboquant_t4nc # 3.8x, 4-bit + NC vllm serve <model> --kv-cache-dtype turboquant_k3v4nc # 4.3x, 3-bit + NC vllm serve <model> --kv-cache-dtype turboquant_t3nc # 4.9x, 3/3-bit + NC This is the docs/plan-path-b.md §5 first-bullet 're-architect as a vLLM plugin / attention backend, not a monkey-patch' path — the path this repo explicitly didn't take. Investing further in this repo's monkey-patch surface is now a dead end. Why upstream's port works where this repo's hybrid mode didn't, in five upstream design decisions any of which our hybrid path lacks: 1. Walsh-Hadamard rotation (vs random-orthogonal here) 2. Norm correction (NC) — re-normalises centroid vectors before inverse rotation; ~0.8% PPL improvement at 4-bit. Not in this repo. 3. Boundary-layer protection — first/last N layers stay FP16 via kv_cache_dtype_skip_layers. We quantize all layers uniformly. 4. No QJL — explicitly omitted upstream per '5+ independent groups found it hurts attention quality by amplifying variance through softmax'. We use QJL. 5. No 2-bit-value preset shipped. Minimum upstream is 3-bit-value (turboquant_t3nc). Plan §2 default in this repo (3/2) is more aggressive than anything upstream ships — consistent with our §5 stop-loss finding that 2-bit value at 1B scale is not quality-viable. Documentation changes: README.md: - SUPERSEDED notice at top: migration path, design-decision diff against upstream, list of what this repo did contribute as a research record, what it is NOT. - Original ⚠️ notice + benchmark tables preserved verbatim below the SUPERSEDED block. docs/plan-path-b.md: - SUPERSEDED notice at top - Sprint 4 marked N/A as of 2026-04-30 with the actual S4.1 / S4.2 landing recorded honestly (S4.1 fixes free_kv_cache; S4.2 wrote bench script that never got run end-to-end) - Sprint 5 marked N/A — upstream's FA3/FA4 + Triton kernels are the target Sprint 5 contemplated, delivered at industrial scale - §4 F3 row updated to 'closed by upstream supersession' - §5 gains a fourth 'upstream supersession' stop-loss bullet - §5 first / second bullets get retrospective 2026-04-30 notes: bullet-1 vindicated (upstream took that path), bullet-2 engaged (Llama-1B numbers below 30% threshold across three bit budgets, consistent with upstream not shipping 2-bit-value) - Footer's 'Last updated' bumped with archive event docs/integration-state.md: - SUPERSEDED notice at top with pointers to the still-useful research artefacts: §F1bis (FULL CUDAGraph bypass diagnosis), §S1.3 (post-execute paged-cache reader recipe), §S3.1 - S3.3 follow-up (Llama-1B empirical numbers). Final tag follows: v0.2-final. Refs https://github.com/vllm-project/vllm/releases/tag/v0.20.0, vllm-project/vllm#38479, vllm-project/vllm#40092.

Signed-off-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Codex <codex@openai.com> Signed-off-by: Adrian <info@zzit.ch>

Signed-off-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: 墨楼 <huangzhilin.hzl@antgroup.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Codex <codex@openai.com> Co-authored-by: hongbolv <33214277+hongbolv@users.noreply.github.com>

huangzhilin-hzl requested review from LucasWilkinson, MatthewBonanni, mgoin and vadiklyutiy as code owners April 17, 2026 03:59

mergify Bot added the v1 label Apr 17, 2026

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huangzhilin-hzl force-pushed the a1-fa3-fa4-support branch from a2e5d10 to 839d499 Compare April 18, 2026 12:48

huangzhilin-hzl added 3 commits April 19, 2026 21:33

Merge branch 'main' into a1-fa3-fa4-support

7b3b90f

Merge branch 'main' into a1-fa3-fa4-support

13d9523

Merge branch 'main' into a1-fa3-fa4-support

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vibhavagarwal5 mentioned this pull request Apr 20, 2026

[Tracking issue]: TurboQuant/HIGGS Attention follow-ups #40069

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Merge branch 'main' into a1-fa3-fa4-support

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mgoin approved these changes Apr 21, 2026

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mgoin added ready ONLY add when PR is ready to merge/full CI is needed quantization labels Apr 21, 2026

Merge branch 'main' into a1-fa3-fa4-support

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huangzhilin-hzl and others added 2 commits April 23, 2026 10:21

[TurboQuant] Fix optional fa_version typing

dce084e

Co-authored-by: Codex <codex@openai.com> Signed-off-by: 墨楼 <huangzhilin.hzl@antgroup.com>

Merge remote-tracking branch 'origin/main' into worktree-a1-fa3-fa4-s…

4ea1078

…upport

mgoin merged commit fe9c3d6 into vllm-project:main Apr 23, 2026
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noonghunna mentioned this pull request Apr 24, 2026

[Bug]: TurboQuant KV + spec-decode + chunked-prefill crashes CUDA graph capture at query_start_loc.tolist() in continuation-prefill path (Qwen3-Next hybrid dense) #40807

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Sandermage mentioned this pull request Apr 26, 2026

[Bugfix][Spec-Decode] TurboQuant K+1 spec-verify routing (fixes #40880) #40914

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Defilan mentioned this pull request Apr 28, 2026

test + integrate vLLM v0.20.0 (TurboQuant 2-bit KV, DeepSeek V4, FA4 default) defilantech/LLMKube#354

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noonghunna mentioned this pull request May 1, 2026

Cliff 1 mech B leaks past PN12 sidecar on inductor-compiled FFN forward (real workloads, not synthetic verify-stress) noonghunna/club-3090#16

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[TurboQuant] enable FA3/FA4 for prefill paths#40092

[TurboQuant] enable FA3/FA4 for prefill paths#40092
mgoin merged 8 commits intovllm-project:mainfrom
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	self.fa_version = get_flash_attn_version(head_size=head_size)
	self.fa_version = get_flash_attn_version(
	requires_alibi=alibi_slopes is not None, head_size=head_size)

		# Detect flash-attn version (FA2/3/4) for prefill paths.
		self.fa_version = get_flash_attn_version(head_size=head_size)

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huangzhilin-hzl commented Apr 17, 2026

Purpose

Test Plan

Test Result

FA version detection

Unit tests

E2E inference with CUDAGraph (enforce_eager=False)

GSM8K correctness eval (Qwen3-4B, 1319 questions, 5-shot)

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Hardware-support note from a Blackwell-consumer run

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