[Perf][Qwen3.5] Avoid GDN decode gate copies

[feednetinfra]

Purpose

Qwen3.5 projects the GDN beta/alpha gates into one packed [b, a] buffer. Splitting that buffer produces valid row-strided views, but forward_cuda materializes both views with .contiguous() before entering the GDN custom op. This emits two copy kernels per GDN layer for multi-request standard decode.

This change keeps the gate projections as views through the existing packed non-spec decode path. The packed recurrent CUDA/Triton kernel already consumes tensor strides, so no new kernel is needed. Prefill, speculative decode, and fallback paths still materialize contiguous gates inside _forward_core, preserving their existing contracts.

The kernel test now covers independently strided QKV and gate inputs, including gate rows whose storage stride is larger than the logical packed [b, a] width (the replicated-BA/TP slicing shape).

Why this is not duplicate work

I searched open vLLM PRs for Qwen3.5 GDN decode projection, GDN gate contiguous decode, GDN strided gates, and GDN projection unpack Conv1D.

• [Perf][Qwen3.5] Fuse GDN linear_attn qkv|z|b|a into a single GEMM #42746 fuses the input projection GEMMs; this change removes downstream gate materialization and is composable with it.
• [Kernel][Perf] Add fused CUDA post-conv MTP decode kernel for Qwen3.5 GDN #51674 fuses the post-conv MTP/speculative decode path; this change targets ordinary packed non-spec decode before Conv1D.
• [Kernel] Optimize SM103 GDN causal-conv prefills #50226 optimizes causal-conv prefill, not decode projection views.
• [Kernel] Opt-in fused shared-expert gate + GDN in-proj fusion + prefill scan-output copy elision for Qwen3.5-122B-A10B-NVFP4 (Blackwell SM103) #45717 covers input-projection GEMM fusion and prefill scan-output copy elision, not these decode gate copies.

The optimization goal was inspired by SGLang #32919. I also prototyped its fused projection-unpack + Conv1D approach, but on current vLLM it duplicated work already handled efficiently by the existing Conv1D and packed recurrent kernels and was 6-8% slower. This PR therefore uses the smaller vLLM-native copy-elision approach.

Test Plan

Run the packed recurrent decode correctness matrix on CUDA:

.venv/bin/python -m pytest   tests/kernels/test_fused_recurrent_packed_decode.py -q

Run applicable changed-file checks:

.venv/bin/pre-commit run ruff-check --files   vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py   tests/kernels/test_fused_recurrent_packed_decode.py

.venv/bin/pre-commit run ruff-format --files   vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py   tests/kernels/test_fused_recurrent_packed_decode.py

.venv/bin/pre-commit run typos --files   vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py   tests/kernels/test_fused_recurrent_packed_decode.py

.venv/bin/pre-commit run mypy-3.12 --hook-stage manual --files   vllm/model_executor/layers/mamba/gdn/qwen_gdn_linear_attn.py   tests/kernels/test_fused_recurrent_packed_decode.py

Test Result

CUDA environment:

• NVIDIA RTX PRO 6000 Blackwell Server Edition (SM120)
• PyTorch 2.11.0+cu130
• Triton 3.6.0
• BF16 benchmark shape matching Qwen3.5 TP=4 local dimensions: H=4, HV=8, K=V=128, QKV width=2048, Conv1D width=4
• CUDA-event timing, 50 warmups + 500 iterations, full Conv1D + packed recurrent boundary

Correctness:

............                                                             [100%]
12 passed in 6.96s

The 12 cases cover FP16/BF16/FP32 and the cross product of contiguous/row-strided QKV and contiguous/arbitrary-row-strided gates. Outputs and recurrent-state updates are compared against the unpacked reference.

Performance:

Decode batch	Current copy path	Gate views	Speedup
1	0.0762 ms	0.0752 ms	1.01x
2	0.0956 ms	0.0765 ms	1.25x
8	0.0952 ms	0.0764 ms	1.25x

Static checks:

ruff check   Passed
ruff format  Passed
typos        Passed
mypy-3.12    Passed
git diff --check Passed

A full current-main Qwen3.5-35B-A3B-FP8 TP=4 serving smoke was attempted in the available vLLM v0.24 container. Model loading and torch.compile completed, but current-main warmup could not finish because the container's compiled _moe_C ABI predates the current topk_softmax/moe_sum signatures and its FlashInfer lacks set_autotune_process_group. This is an environment dependency mismatch, not a failure in the changed GDN path. The draft should be rerun in a current-main-matched image before it is marked ready.

The complete changed-file pre-commit invocation was also attempted, but its first-time Node environment bootstrap for markdownlint stalled. All applicable Python hooks are listed above and passed.

AI assistance

OpenAI Codex was used for implementation support, duplicate-work research, test automation, and benchmark analysis. This PR is intentionally a draft pending the human submitter's line-by-line review and rerun/understanding of the validation before marking it ready.

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