[roberteg16]

Summary

A Qwen MoE block gates its shared expert with sigmoid(shared_expert_gate(x)), where shared_expert_gate is a ReplicatedLinear(hidden_size, 1). During decode with a single token that is a 1x1xK GEMM, and hipBLASLt answers it with a 64x96x32 macro tile plus a SplitK post-pass in order to produce one scalar. Qwen3.5-35B-A3B has 40 such layers, so this fires 40 times per decode step.

This series replaces that shape with a dot product, then folds the surrounding work into the same kernel. On gfx1151 it is worth 3.2% of decode throughput end to end, with prefill and TTFT unchanged. Everything is gated on on_gfx1151(), the only architecture measured here.

What this touches

The dispatcher change fires for any unquantized bf16/fp16 Linear(K, 1) on a single token, which in this tree means the shared_expert_gate in qwen2_moe, qwen3_moe, qwen3_next, qwen3_5 and interns2_mobius. The only other 1-wide projection, qwen3_dspark's proj, is fp32 and therefore excluded.

The call-site change is narrower: it lives in Qwen2MoeMLP, so it reaches Qwen2-MoE, Qwen3-Next, Qwen3.5-MoE and InternS2 Mobius. qwen3_moe has its own Qwen3MoeMLP whose forward is line-for-line identical; I left it alone because I have not measured it, but folding the two would be the obvious follow-up.

Checkpoints without a shared expert are untouched, as is any deployment running with VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS, where expert_gate is None.

Commits

commit	what it does
Replace the 1x1xK BLAS call with a fused dot on gfx1151	Routes the m == n == 1 shape to (x*w).sum(dtype=x.dtype) instead of BLAS.
Serve the tiny scalar projection from a Triton kernel	Replaces the two-launch eager chain with a single-workgroup fp32-accumulated kernel, for bias=None and K within the register budget. _tiny_dot holds that choice so callers do not repeat it. Adds the correctness test and the A/B benchmark.
Fold the shared_expert_gate sigmoid into the tiny-dot kernel	APPLY_SIGMOID constexpr collapses mul + sum + sigmoid into one launch at the Qwen2MoeMLP call site, and the scalar output is allocated in the input dtype so the store does the rounding rather than a separate copy. Extends the benchmark with --sigmoid.

End-to-end results

Qwen3.5-35B-A3B-W4A16 on gfx1151 (Strix Halo), concurrency 1, 100-token prompts, 128 output tokens, 10 prompts, max_num_seqs=1. Three runs per arm, medians below.

metric	upstream/main	this branch	change
prefill	612 tok/s	627 tok/s	unchanged
TTFT	163 ms	159 ms	unchanged
decode	43.3 tok/s	44.7 tok/s	+3.2%
TPOT	23.11 ms	22.39 ms	−3.1%
end-to-end latency	3098 ms	3003 ms	−3.1%

Kernel microbenchmark

benchmarks/kernels/bench_tiny_dot.py, times in µs. Each cell is the median of five separate process invocations, because the BLAS arm is not repeatable enough for a single reading (see below).

K	dtype	BLAS	eager fused	Triton	Triton vs BLAS
1024	bf16	12.60	9.16	5.24	2.40x
1024	fp16	9.98	9.08	4.88	2.05x
2048	bf16	16.12	8.36	5.22	3.09x
2048	fp16	11.28	8.26	4.88	2.31x
4096	bf16	22.94	8.64	5.20	4.41x
4096	fp16	13.88	8.56	4.92	2.82x

The Triton kernel is flat in K at roughly 5 µs, being one launch over a single workgroup, while BLAS grows with K. Qwen3.5-35B-A3B uses K=2048.

With --sigmoid, which is how the gate is actually used, at K=2048 bf16:

	dot only	with sigmoid
BLAS	16.12 µs	18.92 µs
eager fused	8.36 µs	10.88 µs
Triton	5.22 µs	5.26 µs

Folding it into the kernel is what makes the sigmoid free; the other two arms pay another launch for it, so the Triton margin over the eager chain widens from 1.60x to 2.07x.

Accuracy

GSM8K, 5-shot, temperature 0, 400 questions per arm, no invalid responses on either side.

arm	accuracy
upstream/main	0.9225 (369/400)
this branch	0.9125 (365/400)

Testing

pytest tests/kernels/test_tiny_dot_triton.py          24 passed
pre-commit run --files <the four changed files>       clean (ruff, ruff-format, mypy, SPDX)

The new test compares the kernel against the eager reference across K = 32 / 1024 / 2048 / 4096 / 65536 / 131072, both dtypes, with and without the folded sigmoid. The two largest K exercise the multi-warp launches.

Software stack

Measured on ROCm 10.1.0a20260822 (torch.version.hip 7.16), PyTorch 2.13.0, Triton 3.8.0. This is newer than what docker/Dockerfile.rocm_base pins for the shipped image (ROCm 7.2.3, PyTorch 2.12, Triton 3.7). On older stack, improvement is even bigger.

Not a duplicate

No duplicated PRs identified.

AI assistance

This change was prepared with AI assistance (Claude). Every line was reviewed by the submitter, and the measurements above were run and checked by hand.

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[claude]

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[roberteg16]

Not needed any more after #53283.