Support Llama 4 for fused_marlin_moe

[mgoin]

Purpose

Implements apply_router_weight_on_input for the fused_marlin_moe implementation in order to more performantly support Llama 4 INT4A16 and to correctly support NVFP4A16

Test Plan

Model evals

Test Result

NVFP4A16

# Before (ignoring check for apply_router_weight_on_input)
vllm (pretrained=Llama-4-Scout-17B-16E-NVFP4,max_model_len=4096,tensor_parallel_size=2,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |    0|±  |     0|
|     |       |strict-match    |     5|exact_match|↑  |    0|±  |     0|

# After
vllm (pretrained=Llama-4-Scout-17B-16E-NVFP4,max_model_len=4096,tensor_parallel_size=2,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8082|±  |0.0108|
|     |       |strict-match    |     5|exact_match|↑  |0.8074|±  |0.0109|

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[gemini-code-assist]

Summary of Changes

Hello @mgoin, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces support for the apply_router_weight_on_input parameter within the fused_marlin_moe implementation. This change is crucial for more performant and correct handling of Llama 4 INT4A16 and NVFP4A16 models, by enabling a specific weight application logic that was previously unsupported, as demonstrated by significant improvements in model evaluation results.

Highlights

• Core Functionality Enablement: The fused_marlin_moe function now accepts an apply_router_weight_on_input boolean parameter, which dynamically controls the mul_topk_weights argument in internal operations. This enables specific weight application logic required for certain model configurations.
• Removal of Restrictions: Previous NotImplementedError and assert statements that explicitly disallowed apply_router_weight_on_input for Marlin MoE across various quantization methods (AWQ, Compressed Tensors, FP8, GPTQ, ModelOpt) have been removed, unblocking this functionality.
• Integration Across Quantization Layers: The new apply_router_weight_on_input parameter is now correctly propagated and passed down to the fused_marlin_moe calls within the AWQMarlinMoE, CompressedTensorsMoE, FP8MoE, GPTQMarlinMoE, and ModelOptMoE quantization methods, ensuring consistent behavior.
• Marlin Utility Update: The check_moe_marlin_supports_layer utility function has been updated to no longer disallow apply_router_weight_on_input for Marlin MoE layers, reflecting the newly added support.

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[gemini-code-assist]

Code Review

This pull request introduces support for applying router weights on the input for fused_marlin_moe, a feature required for Llama 4 models. The implementation adds an apply_router_weight_on_input flag to fused_marlin_moe and its callers across various quantization methods.

The core logic correctly uses this flag to toggle where the router weights are applied—either in the first or second GEMM of the MoE layer—ensuring they are applied exactly once. This change is consistently propagated through the codebase, and backward compatibility is maintained by defaulting the new flag to False.

The code is clean, and the changes are well-contained. The provided test results, showing a significant improvement for NVFP4A16 models, strongly support the correctness of this implementation. Overall, this is a solid contribution.

[tlrmchlsmth]

Nice, thanks!