[Bugfix] Forward SwiGLU alpha/beta to the Marlin NVFP4 MoE quant config

[linjiapro]

FIX #34694 (for ModelOpt NVFP4 MoE / Marlin-fallback configurations of SwiGLU-OAI models)

Purpose

nvidia/MiniMax-M3-NVFP4 (and by construction any SwiGLU-OAI MoE model served via ModelOpt NVFP4 on the Marlin fallback, i.e. GPUs without native FP4) produces prompt-unrelated token noise. The root cause is not the Marlin kernel: the modelopt → NvFp4MoeBackend.MARLIN quant-config bridge forwards only gemm1_clamp_limit and drops swiglu_alpha / swiglu_beta, and MarlinExperts silently defaults them to alpha=1.0 / beta=0.0 — the wrong activation in every expert of every MoE layer, compounding across depth into noise.

All three values are present on the layer (RoutedExperts stores swiglu_alpha/beta/limit), and the neighboring MXFP8 site in modelopt.py already forwards them with the identical getattr pattern; the NVFP4 site never did. The fix threads them through the three-hop chain (modelopt.py → make_nvfp4_moe_quant_config → nvfp4_w4a16_moe_quant_config).

This also reattributes part of #34694 for MoE checkpoints: we initially reproduced that issue's symptom with this model and (like #47315) suspected the Marlin FP4 BF16 scale dequant. Direct kernel unit testing (below) shows the Marlin NVFP4 MoE kernel + scale-prep pair is numerically correct as-is (bf16 ≈0.8% rel err, fp16 ≈0.08% vs torch reference); only the activation parameterization was wrong. Detailed investigation notes in #47315 (comment) and follow-ups.

Changes

• nvfp4_w4a16_moe_quant_config: accept and forward gemm1_alpha / gemm1_beta.
• make_nvfp4_moe_quant_config: accept swiglu_alpha / swiglu_beta, forward on the MARLIN branch.
• modelopt.py (ModelOptNvFp4FusedMoE.get_fused_moe_quant_config): pass getattr(layer, "swiglu_alpha"/"swiglu_beta", None), mirroring the MXFP8 site.
• New tests/kernels/moe/test_marlin_nvfp4_moe.py:
  • a config-plumbing test that pins this regression (fails on the old code with a TypeError / missing params);
  • Marlin NVFP4 MoE kernel-vs-torch-reference coverage (bf16 + fp16, multiple shapes, incl. single-expert) — this path had no unit coverage at all (test_nvfp4_moe.py covers the CUTLASS experts only), which is how the bug shipped silently.

Note: the sibling FP8 MoE call site in modelopt.py (make_fp8_moe_quant_config) has the same omission and likely wants the same one-line treatment; left out here because it was not exercised by the model we validated with.

Test Plan

• New tests: 17/17 pass on 4× RTX PRO 6000 Blackwell (SM120), CUDA 13.0, torch 2.11.0+cu130. The kernel-correctness cases pass identically with and without the fix (the kernel was never the problem).
• End-to-end serving A/B on nvidia/MiniMax-M3-NVFP4 (TP=4, quantization=modelopt_mixed, Using 'MARLIN' NvFp4 MoE backend), only variable = this patch:

Before — output has no relationship to the prompt:

prompt: "What is 17 * 23? Reply with just the number."
output: '</\n\n</\n\n | &](\n\nX\n\n\n\n德里\n\nBid</\n\n</.\n\n\n\n",</</ …'

After — correct, with clean reasoning, including 2K/4.5K-token needle retrieval:

[math  ] '391'
[prose ] "The sky appears blue because Earth's atmosphere scatters shorter-wavelength blue light …"
[needle] 'BLUEBERRY'

(Separately from this fix, we observe an illegal-memory-access crash under highly concurrent load on this SM120 setup; single-stream and moderate-concurrency serving is coherent. That appears to be an orthogonal platform issue and is not addressed here.)

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[ehfd]

@mgoin @youkaichao @LucasWilkinson @MatthewBonanni

Critical fix for Marlin + NVFP4 on MiniMax-M3

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @linjiapro.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork