[Bugfix] Allow skipping MoE in NVFP4 (fix for MTP)

[benchislett]

Purpose

There is no fallback in ModelOptNvfp4Config.get_quant_method for when quant_config should skip an MoE layer.

This is a problem for nvidia/DeepSeek-R1-FP4 when running with MTP since the entire MTP layer is left unquantized, and should be skipped by quantization:

https://huggingface.co/nvidia/DeepSeek-R1-FP4/blob/main/hf_quant_config.json#L188
"exclude_modules": [
...
"model.layers.61*",
...
]

This PR includes some diff from #25953.

Testing

Evaluated in combination with #25984, see results there.

[gemini-code-assist]

Code Review

This pull request introduces a bugfix to allow skipping Mixture-of-Experts (MoE) layers during NVFP4 quantization, which is crucial for models like nvidia/DeepSeek-R1-FP4 when using Multi-Token Prediction (MTP).

The main changes are:

1. In vllm/model_executor/layers/quantization/modelopt.py, the ModelOptNvFp4Config.get_quant_method now checks if an MoE layer is in the exclusion list and returns None if so.
2. In vllm/model_executor/layers/fused_moe/layer.py, the FusedMoE layer's __init__ method is updated to handle the None return from get_quant_method by falling back to the unquantized method, effectively skipping quantization for that layer.
3. Several related changes in deepseek_v2.py, deepseek_mtp.py, and deepseek_eagle.py refactor how the model configuration is passed to DeepseekV2DecoderLayer to correctly support draft models in speculative decoding scenarios.

The changes are well-structured and correctly address the identified issue. The refactoring for config propagation is clean and necessary. The overall implementation looks solid.

[mgoin]

Looks reasonable to me, thanks for the fix

[mgoin]

@benchislett The basic model failure seems related

[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)   File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 1139, in <lambda>
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)     lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix,
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)   File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/deepseek_v2.py", line 997, in __init__
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)     config = config or vllm_config.model_config.hf_config
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)   File "/usr/local/lib/python3.12/dist-packages/torch/utils/_device.py", line 103, in __torch_function__
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)     return func(*args, **kwargs)
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151)            ^^^^^^^^^^^^^^^^^^^^^
[2025-10-01T00:28:07Z] (EngineCore_DP0 pid=151) RuntimeError: Boolean value of Tensor with more than one value is ambiguous
[2025-10-01T00:28:07Z] FAILED

[mgoin]

@benchislett please merge with main to fix the docker

[mergify]

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

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