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Motivation

Part of the fused MoE gate/topk kernel consolidation (#26771). SGLang has many per-model gate/topk kernels (grouped_topk.cuh, AOT topk_sigmoid/topk_softmax, the CUDA-JIT moe_fused_gate, flashinfer fused_topk_deepseek, …). #25835 introduced one Triton moe_fused_gate router and made it the default for the ungrouped sqrtsoftplus path.

This PR converges the remaining paths onto that single router, in three phases:

• Phase 1 — ungrouped: route the CUDA softmax/sigmoid topk and the ungrouped grouped_topk (num_expert_group==1) branch through the Triton router, and optimize it to beat the hand-tuned AOT kernels on B200 + H100 + H200.
• Phase 2 — cleanup: delete the kernel the consolidation makes dead.
• Phase 3 — grouped: add DeepSeek-V3 grouped routing (n_group/topk_group, noaux_tc) to the same router and wire an opt-in dispatch, validated bit-exact e2e and at parity with flashinfer.

Modifications

Triton router (jit_kernel/moe_fused_gate.py):

• Add softmax scoring (softmax-prob weight, bias kept, optional tanh softcap) alongside sigmoid/sqrtsoftplus.
• Accept fp32/fp16/bf16 scores (kernel upcasts on load) — bf16 router logits no longer need a host-side fp32 upcast.
• Phase 3: grouped routing (constexpr N_GROUP>1): per-group score = sum of the top-2 biased values, keep TOPK_GROUP groups (lowest group id wins ties), mask dropped groups, then the existing top-k runs. N_GROUP==1 compiles the block out (ungrouped path unchanged). More general than the AOT grouped kernel — no experts-per-group ≤ 32 cap.
• Perf: row-pack the launch (num_warps=1, BLOCK_M = max(1, min(4, 256//BLOCK_N))). The old single-warp-per-row grid=(M,) was occupancy-bound at small N on Hopper and lost ~25% to AOT; packing a few rows per program only when N is small fixes it (swept BLOCK_M × num_warps on H100/B200/H200 — this heuristic is the sweep-optimal point; larger tiles / more warps regress via register pressure).

Dispatch (srt/layers/moe/topk.py):

• Phase 1: route the CUDA fused_topk softmax/sigmoid branches and the biased_grouped_topk_gpu num_expert_group==1 branch through the Triton router (gated by SGLANG_OPT_USE_JIT_KERNEL_FUSED_TOPK, default on, _is_cuda). AOT stays the default on non-CUDA backends and the env-off fallback.
• Phase 3: new opt-in SGLANG_OPT_USE_JIT_KERNEL_GROUPED_TOPK (default off) routes the DeepSeek-V3 grouped path (num_expert_group>1) through the Triton router. Off by default so flashinfer fused_topk_deepseek stays the tuned production default; the flag is a consolidation escape hatch (validated bit-exact + at parity, below), not a perf flip.

Cleanup (Phase 2): delete the now-dead jit grouped_topk (kernel + wrapper + test) — its only dispatch site now routes to the Triton router.

Accuracy Tests

• test/registered/jit/test_moe_fused_gate.py: 1063 passed on GPU (sigmoid/sqrtsoftplus/softmax × experts × topk × shared-experts × renorm × scale, fp32+bf16), including:
  • softmax/sigmoid parity vs AOT topk_softmax/topk_sigmoid + a torch reference (Phase 1);
  • grouped parity vs the definitional biased_grouped_topk_impl for DeepSeek-V3 shapes (256/8/4/8, 128/8/4/6, 256/4/2/8), fp32+bf16, ≤1e-3 (Phase 3 kernel);
  • grouped dispatch parity: biased_grouped_topk_gpu with the flag on (Triton) vs off (flashinfer/AOT default), with and without a fused shared expert (Phase 3 wiring).
• Grouped kernel matches the AOT moe_fused_gate in fp32; bf16 differs only on borderline experts because AOT scores in bf16 while the reference + this router upcast to fp32.

Model accuracy — end-to-end (Phase 3)

Three levels of accuracy evidence for the grouped path:

1. Full-model downstream eval — GSM8K + MMLU on DeepSeek-V3-0324 (671B fp8, the flagship sigmoid-grouped model this dispatch targets; n_group=8, topk_group=4, 256 experts). Served with sglang.launch_server --tp 8 on 8×B200; the two runs use the same build, differing only in the SGLANG_OPT_USE_JIT_KERNEL_GROUPED_TOPK env var (flag verified live in the server process). sglang.test.run_eval:

eval	default (flashinfer fused_topk_deepseek)	Triton grouped (flag on)	Δ
GSM8K (1319, greedy)	95.74%	95.89%	+0.15% (2/1319)
MMLU (3000-example sample)	86.47%	86.47%	0.00%

MMLU is identical (2594/3000 both). GSM8K differs by 2 questions — the grouped Triton router and flashinfer diverge only on borderline-expert selection (fp32 vs bf16 scoring rounding), which occasionally flips a mid-CoT token; the effect is noise-level and here Triton scores marginally higher. No accuracy regression.

2. Kernel-level numerical precision (test_moe_fused_gate.py, on GPU): the grouped Triton router vs the definitional biased_grouped_topk_impl — routed-expert selection exact and weights max abs diff ≤ 1e-7 in fp32 (DeepSeek-V3 256/8/4/8, 128/8/4/6, 256/4/2/8). In bf16 selection is identical except on borderline experts, and only because the AOT kernel scores in bf16 while the reference + this router upcast to fp32. Grouped-dispatch parity (flag on vs off, ±fused shared expert) matches to ≤ 1e-3.

3. Bit-exact end-to-end output — DeepSeek-V3.2-5layer-bf16 (real DSA + grouped MoE: 256 experts, n_group=8, topk_group=4, sigmoid), greedy generation on fixed prompts, 1×H200:

dispatch	grouped-MoE invocations	output token hash
default (flashinfer fused_topk_deepseek)	—	5518026aa2a27596
SGLANG_OPT_USE_JIT_KERNEL_GROUPED_TOPK=1 (Triton)	52 (prefill+decode)	5518026aa2a27596

Every generated token is bit-identical across the two dispatches (a stderr marker confirmed the Triton grouped branch fired 52× across prefill + decode, ruling out a silent no-op).

Speed Tests and Profiling

CUDA-graph, kernel-only replay (production runs the gate under graphs).

Phase 1 — ungrouped, Triton vs AOT (Triton ≥ AOT on every shape on B200 + H100; H200 5/6, see note):

B200 (Blackwell):

scoring	M	N	topk	AOT µs	Triton µs	speedup
softmax	4096	128	8	10.21	6.24	1.64x
softmax	8192	256	8	19.05	12.38	1.54x
softmax	8192	512	8	24.44	18.53	1.32x
sigmoid	4096	128	8	10.35	6.23	1.66x
sigmoid	8192	256	8	18.74	12.36	1.52x
sigmoid	8192	512	8	141.82	18.51	7.66x

H100 (Hopper):

scoring	M	N	topk	AOT µs	Triton µs	speedup
softmax	4096	128	8	6.31	6.34	1.00x
softmax	8192	256	8	16.22	13.94	1.16x
softmax	8192	512	8	21.61	20.66	1.05x
sigmoid	4096	128	8	6.51	6.50	1.00x
sigmoid	8192	256	8	17.80	13.99	1.27x
sigmoid	8192	512	8	128.97	21.19	6.09x

H200 (Hopper):

scoring	M	N	topk	AOT µs	Triton µs	speedup
softmax	4096	128	8	6.19	6.41	0.96x*
softmax	8192	256	8	16.07	13.85	1.16x
softmax	8192	512	8	21.61	20.68	1.05x
sigmoid	4096	128	8	6.43	6.41	1.00x
sigmoid	8192	256	8	17.73	14.07	1.26x
sigmoid	8192	512	8	128.65	21.19	6.07x

* The single 0.96x is a bench artifact, not a kernel regression: the Triton wrapper allocates its two output tensors inside the captured graph while the AOT bench pre-allocates them. Kernel-only at that shape the Triton router is 1.13x (BLOCK_M=2, num_warps=1 — the heuristic's choice, confirmed sweep-optimal on H200). Production allocates identically on both paths.

Phase 3 — grouped, Triton vs the real defaults (DeepSeek-V3 256/8/4/8, H200; flashinfer fused_topk_deepseek is the current production default):

M	N	n_group	topk_group	topk	flashinfer µs	AOT µs	Triton µs	vs-flashinfer	vs-AOT
1024	256	8	4	8	5.86	12.21	5.93	0.99x	2.06x
4096	256	8	4	8	13.40	14.83	12.74	1.05x	1.16x
8192	256	8	4	8	23.03	20.88	22.36	1.03x	0.93x

Grouped Triton is at parity with flashinfer (0.99–1.05x) and generally beats AOT — i.e. no perf loss vs the actual production default. Combined with the bit-exact e2e result, this is why the grouped dispatch is wired as an opt-in flag rather than deferred to a follow-up.

Follow-ups (#26771, separate PRs)

• Default-enable the grouped Triton path once it has run through flagship (full DeepSeek-V3) e2e accuracy at scale.
• Retire the AOT .cu once XPU/HIP/MUSA/CPU migrate; MUSA decision.

Checklist

• pre-commit (ruff/isort/black/codespell + registered-test validator) pass.
• Unit tests (1063 on GPU: ungrouped + grouped kernel + grouped dispatch).
• End-to-end model accuracy (DeepSeek-V3-0324 GSM8K 95.74%→95.89%, MMLU 86.47%→86.47%; + DeepSeek-V3.2 bit-identical tokens, above).
• Docs.
• Accuracy + speed benchmarks (B200 + H100 + H200, above).
• Code style.

🤖 Generated with Claude Code

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CI States

Latest PR Test (Base): ❌ Run #28581806799
Latest PR Test (Extra): ❌ Run #28581806410

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Added a second commit folding the biased_grouped_topk_gpu ungrouped (num_expert_group==1/topk_group==1) sigmoid path onto the same Triton router (previously the jit grouped_topk.cuh kernel). Validated on B200: matches biased_grouped_topk_impl at fp32-epsilon across fp32+bf16, renorm + scaling. Now both ungrouped CUDA gate dispatch points go through the unified router.

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