[Model] Enable batch-invariant mixed decode and prefill for Qwen GDN by cm2435 · Pull Request #49827 · vllm-project/vllm

cm2435 · 2026-07-25T20:24:36Z

Fixes #48613. Related to #42960.

What

Enable VLLM_BATCH_INVARIANT=1 for the Qwen GDN execution path on CUDA SM90+,
while leaving the shared Kimi and Olmo GDN backend unsupported.

The implementation keeps cached Qwen decode rows on the packed recurrent decode
path when they share a model-runner step with fresh prefills.

Why

Qwen3.5 and related models interleave regular attention with Gated DeltaNet
layers. vLLM currently refuses to initialize these models in batch-invariant
mode because GDN_ATTN does not declare BIC support.

I first tested the smallest possible change: allowing the existing GDN backend
through the support check. That made Qwen/Qwen3.5-0.8B boot, but it still
failed batch-size invariance tests.

Tracing the first differing output led to an online continuous-batching case:

decode-only step:
  cached row -> packed recurrent decode

mixed step:
  cached row + fresh prefill rows
  cached row -> fused recurrent update
  prefill rows -> chunk prefill

The cached request therefore used different recurrent arithmetic depending on
whether an unrelated prefill arrived during that decode step.

How

The change has three parts:

Add a Qwen-specific GDN backend that declares BIC support on CUDA SM90+.
The shared GDN backend remains fail-closed because Kimi and Olmo have not
been validated.
Extract the existing packed recurrent decode call into a shared helper.
Use that helper for cached decode rows at the front of mixed decode-and-
prefill batches. Fresh prefill rows continue through the existing chunk
prefill path.

The route follows the existing packed recurrent decode setting rather than
checking VLLM_BATCH_INVARIANT directly. Normal and BIC serving therefore use
the same model arithmetic.

The additional registry, XPU, and KV-transfer edits preserve the existing
backend-specific behavior after introducing the Qwen backend enum.

Relationship to #45819

#45819 enables the shared GDN backend and processes prefill sequences
individually through the chunk kernel under BIC.

This change addresses a separate path: cached decode rows in mixed online steps.
It also limits the capability declaration to the Qwen implementation that was
tested, rather than enabling the shared GDN backend.

@yewentao256 invited this follow-up in #48613 after the support-only validation
showed batch-dependent outputs.

Test plan

.venv/bin/python -m pytest -v -s \
  tests/kernels/mamba/test_gdn_forward_core_split.py

.venv/bin/python -m pytest -v -s \
  tests/v1/kv_connector/unit/test_nixl_connector_hma.py \
  -k "compute_physical_blocks_per_logical or derive_mamba_conv_split"

VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B \
VLLM_NEEDLE_BATCH_SIZE=8 \
VLLM_NEEDLE_TRIALS=2 \
VLLM_MIN_PROMPT=64 \
VLLM_MAX_PROMPT=256 \
VLLM_NEEDLE_MAX_TOKENS=32 \
VLLM_MAX_MODEL_LEN=1024 \
.venv/bin/python -m pytest -v -s \
  "tests/v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]"

pre-commit run --from-ref upstream/main --to-ref HEAD

Results:

GDN route tests: 9 passed
NIXL/HMA compatibility tests: 11 passed
official Qwen3.5 BIC test: 2 passed
pre-commit: passed

The route regression runs the same decode rows and recurrent state both alone
and at the front of a mixed batch. The decode output and updated SSM state are
bitwise equal with atol=0, rtol=0.

Online serving validation

I also tested the complete OpenAI-compatible serving path on one H100:

Qwen/Qwen3.5-0.8B
TP 1
prefix caching disabled
chunked prefill enabled
concurrency up to 8
Poisson background arrivals
116, 321, and 1,059-token background prompt classes
16, 64, and 128-token target generations

The run completed 823 background requests and 69 deterministic target requests.
Scheduler traces confirmed that 36 target requests crossed 71 mixed
decode-and-prefill steps.

Results across all target requests:

request failures: 0
generated-token mismatches: 0
selected-token-logprob mismatches: 0
top-logprob-map mismatches: 0
maximum selected-token-logprob difference: 0.0

Model evaluation

GSM8K, 1,319 examples, five-shot:

Revision	Flexible extract	Strict match
upstream	0.3427	0.3381
patched	0.3457	0.3397

Performance

Median of three vllm bench serve repetitions on one H100:

Mode	Output tok/s	p50 TTFT	p50 ITL
upstream, normal	252.87	107.94 ms	29.56 ms
patched, normal	252.37	106.34 ms	29.65 ms
support-only BIC	193.35	134.42 ms	39.01 ms
patched BIC	206.39	127.29 ms	36.54 ms

Normal-serving output throughput changed by -0.20%. Against the support-only
BIC route, the patch improved output throughput by 6.74%.

Validated scope

Validated here:

Qwen GDN
CUDA SM90+
unquantized
TP 1
no speculative decoding
no prefix caching

Out of scope

This PR is limited to the single-GPU Qwen GDN mixed decode-and-prefill route.
It does not claim BIC support for:

tensor-parallel or expert-parallel execution;
recurrent state invariance across arbitrary chunked-prefill boundaries;
prefix caching;
speculative decoding;
quantized execution;
Kimi, Olmo, or other GDN implementations.

Work on TP/EP invariance and the remaining chunked-prefill/state-carry cases is
ongoing. Those changes will be submitted as separate PRs once each path has an
isolated regression test and public-model validation. They are deliberately
not included here so this PR retains one failure mode, one route-level fix, and
one reviewable correctness claim.

This contribution was developed with assistance from OpenAI Codex. I reviewed
the final diff and remain responsible for the implementation and results.

Keep cached Qwen GDN rows on packed recurrent decode when fresh prefills share the model-runner step, and opt only the validated Qwen CUDA backend into batch-invariant execution. Co-authored-by: OpenAI Codex <codex@openai.com> Signed-off-by: Charlie Masters <charlie.masters@hcompany.ai>

github-actions · 2026-07-25T20:24:46Z

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claude

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yewentao256

Thanks for the work!

Could you test on Blackwell as well?

yewentao256 · 2026-07-25T21:06:19Z

  source_file_dependencies:
    - vllm/v1/attention
    - vllm/model_executor/layers
+    - tests/kernels/mamba/test_gdn_forward_core_split.py


This shouldn't be included in batch invariance test as there are some normal tests

yewentao256 · 2026-07-25T21:06:48Z

    - pip install pytest-timeout pytest-forked
    - pytest -v -s v1/determinism/test_batch_invariance.py
    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
+    - pytest -v -s kernels/mamba/test_gdn_forward_core_split.py


yewentao256 · 2026-07-25T21:06:57Z

+    - pytest -v -s kernels/mamba/test_gdn_forward_core_split.py
    - VLLM_TEST_MODEL=deepseek-ai/DeepSeek-V2-Lite-Chat pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[TRITON_MLA]
    - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
+    - VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B VLLM_NEEDLE_BATCH_SIZE=8 VLLM_NEEDLE_TRIALS=2 VLLM_MIN_PROMPT=64 VLLM_MAX_PROMPT=256 VLLM_NEEDLE_MAX_TOKENS=32 VLLM_MAX_MODEL_LEN=1024 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]


How long will this run? Could you run all tests to see how much time in total?

mergify Bot added ci/build qwen Related to Qwen models intel-gpu Related to Intel GPU v1 kv-connector labels Jul 25, 2026

cm2435 marked this pull request as ready for review July 25, 2026 20:26

cm2435 requested review from AndreasKaratzas, ApostaC, Harry-Chen, LucasWilkinson, MatthewBonanni, NickLucche, WoosukKwon, ZJY0516, ivanium, jikunshang, khluu, mgoin, orozery, tdoublep, tlrmchlsmth, vadiklyutiy, xuechendi, yewentao256 and zyongye as code owners July 25, 2026 20:26

claude Bot reviewed Jul 25, 2026

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cm2435 mentioned this pull request Jul 25, 2026

[Feature]: [Batch-Invariant-Kernels] GDN_ATTN does not support batch-invariant mode for Qwen3.5/Qwen3.6 GDN models #48613

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[Model] Enable batch-invariant mixed decode and prefill for Qwen GDN#49827

[Model] Enable batch-invariant mixed decode and prefill for Qwen GDN#49827
cm2435 wants to merge 1 commit into
vllm-project:mainfrom
cm2435:qwen-gdn-bic-mixed-decode

cm2435 commented Jul 25, 2026 •

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cm2435 commented Jul 25, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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What

Why

How

Relationship to #45819

Test plan

Online serving validation

Model evaluation

Performance

Validated scope

Out of scope

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cm2435 commented Jul 25, 2026 •

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