[MoE/b12x] Accept W4A16 (kNvfp4Static, None) in FlashInferB12xExperts supports check

[ECMGit]

Purpose

FlashInferB12xExperts._supports_quant_scheme (introduced by PR #40082) currently requires the activation key to be kNvfp4Dynamic, which makes the dispatcher reject every W4A16 NVFP4 checkpoint (activation_key == None) — e.g. nvidia/Qwen3.6-35B-A3B-2.06GB-per-token. This forces such checkpoints onto Marlin, even though the b12x kernel itself is W4A16-compatible.

PR #42566 ("W4A16 NVFP4 fused MoE + mixed-precision dispatch") acknowledges this exact gap in its own commit message — "their _supports_quant_scheme requires (kNvfp4Static, kNvfp4Dynamic) exactly... only Marlin survives" — and deliberately routes W4A16 to Marlin as a workaround. This PR is the actual fix on the b12x side, complementary to #42566 and independent of its merge.

Per the b12x class docstring: "Input quantization (BF16→FP4) is performed inside the kernel so BF16 hidden states are passed directly." — i.e. the kernel already handles the BF16-activation case correctly. This PR only loosens the metadata gate; no kernel-side changes needed.

Change:

-        return (weight_key, activation_key) == (kNvfp4Static, kNvfp4Dynamic)
+        # b12x performs in-kernel BF16->FP4 activation quant, so W4A16
+        # NVFP4 checkpoints (activation_key=None, e.g. mixed-precision
+        # compressed-tensors layouts) are runtime-compatible.
+        return (weight_key, activation_key) in (
+            (kNvfp4Static, kNvfp4Dynamic),
+            (kNvfp4Static, None),
+        )

Failure mode without this PR:

ValueError: NvFp4 MoE backend 'FLASHINFER_B12X' does not support the deployment
configuration since kernel does not support quantization scheme
QuantKey(u8, scale(f8e4m3fn, static, GroupShape(row=1, col=16)),
         scale2(f32, static, per_tensor), symmetric) x None.

Risk: None — gate-only loosening. No kernel changes. No new dependencies. The existing (kNvfp4Static, kNvfp4Dynamic) path is preserved verbatim.

Test Plan

Hardware: DGX Spark (GB10, sm_121a)
Container: vllm/vllm-openai:nightly-aarch64 + this PR + the FP8-backend-env companion PR
Model: nvidia/Qwen3.6-35B-A3B-2.06GB-per-token (modelopt-native, mixed NVFP4 + FP8 experts)
Tooling: FlashInfer 0.6.11.post3, cutlass-dsl trio 4.5.1

Serve command:

export VLLM_NVFP4_GEMM_BACKEND=flashinfer-b12x
export VLLM_USE_FLASHINFER_MOE_FP4=1
export VLLM_FP8_MOE_BACKEND=flashinfer_cutlass     # companion PR
export FLASHINFER_DISABLE_VERSION_CHECK=1
export CUTE_DSL_ARCH=sm_121a

vllm serve nvidia/Qwen3.6-35B-A3B-2.06GB-per-token \
    --tensor-parallel-size 1 --trust-remote-code --dtype auto \
    --kv-cache-dtype fp8 --attention-backend FLASHINFER \
    --gpu-memory-utilization 0.85 --max-model-len 40960 \
    --max-num-seqs 4 --max-num-batched-tokens 8192 \
    --enable-chunked-prefill --async-scheduling --enable-prefix-caching \
    --moe-backend=flashinfer_b12x --quantization=modelopt \
    --compilation-config '{"pass_config":{"fuse_norm_quant":true,"fuse_act_quant":true,"fuse_attn_quant":false}}' \
    --speculative-config '{"method":"mtp","num_speculative_tokens":3,"rejection_sample_method":"synthetic","synthetic_acceptance_length":3.12,"moe_backend":"triton"}'

Benchmark (aiperf): K=3 AL=3.12, BS=1 / concurrency=1, ISL=2048 + 32K user-context (total ISL=34,831), OSL=1024, 10 warmup + 60 measured requests, --use-server-token-count --streaming.

Test Result

With this PR (b12x dispatcher accepts W4A16):

Metric	Value
Output Token Throughput	91.00 tok/s
Output Token Throughput / user	97.42 tok/s/user
TTFT	746.81 ms
ITL	10.27 ms
Request Latency	11,249.37 ms
MTP acceptance length	3.15 (target 3.12) ✓
Requests / errors	60 / 0

Without this PR: serve dies at engine init with the ValueError shown above before any throughput can be measured.

Reference on the same hardware/workload (sanity comparison):

Path	OTT (tok/s)	TTFT (ms)
Marlin (current W4A16 fallback in #42566)	92.26	798.72
b12x on dgx-fork (matched-cubin FlashInfer)	95.15	758.90
b12x on upstream (this PR)	91.00	746.81

With this PR, b12x runs cleanly on W4A16 NVFP4 checkpoints and reaches parity with Marlin on the public wheel stack (the small ~4% delta vs the dgx-fork b12x number is attributable to a known flashinfer-cubin 0.6.11.post2 / flashinfer 0.6.11.post3 version-skew in the public PyPI wheel — unrelated to this PR).

---

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[gemini-code-assist]

Code Review

This pull request updates the flashinfer_b12x_moe expert to support W4A16 NVFP4 checkpoints by allowing None as an activation quantization key in the _supports_quant_scheme method. While this enables the dispatcher to select the backend, feedback points out that the apply method contains a strict assertion requiring an activation scale (a2_gscale), which will likely cause an AssertionError for these models. It is recommended to update the apply method to provide a default scale tensor instead of asserting.

[ECMGit]

add @meena-at-work, @juhi10071998 as reviewer

[ECMGit]

Crossing reference with #43341 (@askliar) which also touches FlashInferB12xExperts for W4A16 NVFP4 — I am confirming the two PRs are complementary, not duplicate:

Quant scheme	Path	This PR (#43332)	#43341
(kNvfp4Static, kNvfp4Dynamic)	modelopt W4A4	✓ (preserved)	✓ (preserved)
(kNvfp4Static, None)	modelopt-native W4A16	✓ (new, this PR)	—
(uint8 + kNvfp4StaticGroupScale + kStaticTensorScale, None)	compressed-tensors nvfp4-pack-quantized W4A16	—	✓ (new, #43341)

So this PR covers the modelopt-native W4A16 family (e.g. nvidia/Qwen3.6-35B-A3B-2.06GB-per-token), while #43341 covers the compressed-tensors nvfp4-pack-quantized family (e.g. nvidia/Nemotron-H-3.5-*). #43341's own description acknowledges this split: "Both could land in any order; together they cover both W4A16 sources."

The only mechanical conflict is in the body of _supports_quant_scheme: whichever PR lands second needs a ~3-line rebase to combine both accepted forms.

One important note for landing order

The apply() a2_gscale=1.0 default in this PR's second commit (ac2187a72) is NOT in #43341. That commit replaces the strict assert self.a2_gscale is not None with a uniform-1.0 fallback per expert (symmetric to the existing if self.a2_gscale is not None: fill_(1.0) guard one method above in process_weights_after_loading).

This matters because compressed-tensors nvfp4-pack-quantized W4A16 (the case #43341 enables) has activation_key == None, so the FusedMoE machinery does not populate a2_gscale — and #43341 still uses self.a2_gscale directly in its _wrapper.run(...) call. Without the default-fallback fix, that path would hit AssertionError: a2_gscale must not be None at the first inference request.

So even if #43341 lands first, this PR's Change 2 is still needed end-to-end for the compressed-tensors W4A16 path to actually serve requests.

cc @askliar — happy to coordinate landing order, or if you'd prefer I can rebase Change 2 onto your apply() refactor so reviewers see a single coherent W4A16 picture.

[vadiklyutiy]

This is apply and hot path. I think it is better do any allocation somewhere early.

[ECMGit]

okay updated, route to early stage

[vadiklyutiy]

@claude review pls

[mergify]

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[ECMGit]

Hi @vadiklyutiy , the pre-commit has passed. let me know if further change is needed.

[xinli-sw]

LGTM!

[mgoin]

LGTM, thanks!