[Misc][DP] Guard mxfp4 implementation selection

[varun-sundar-rabindranath]

Purpose

Guard MxFP4 implementation selection in the case of DP

Machine : H100
command : VLLM_USE_FLASHINFER_MOE_MXFP4_BF16=1 VLLM_ALL2ALL_BACKEND="deepep_high_throughput" vllm serve openai/gpt-oss-20b --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-caching --port 9010

Error on main:

EngineCore_DP0 pid=1017522)     assert quant_config.use_mxfp4_w4a16, "Supports only mxfp4_w4a16"
(EngineCore_DP0 pid=1017522) AssertionError: Supports only mxfp4_w4a16

Here, despite explicitly selecting SM90_FI_MXFP4_BF16 backend via VLLM_USE_FLASHINFER_MOE_MXFP4_BF16, we choose the OAITritonExperts (TRITON backend) for DP and we fail somewhere deep in the codebase.

Error on PR:

ERROR 10-24 17:09:17 [core.py:779] NotImplementedError: Incompatible Mxfp4 backend for EP batched experts format

Much more straightforward error.

Test Plan

VLLM_ALL2ALL_BACKEND="deepep_high_throughput"  vllm serve openai/gpt-oss-20b  --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-caching

and

VLLM_MXFP4_USE_MARLIN=1 VLLM_ALL2ALL_BACKEND="deepep_high_throughput"  vllm serve openai/gpt-oss-20b  --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-caching

Test Result

The commands work as expected. on H100, The first defaults to using Triton backend and the second uses the Marlin backend.

[varun-sundar-rabindranath]

cc @zyongye @mgoin PTAL! Thanks 🙌

[gemini-code-assist]

Code Review

This pull request introduces a guard to prevent incorrect MxFP4 implementation selection in data-parallel setups. The change adds an explicit error to be raised when an incompatible MxFP4 backend is selected for EP batched experts format, improving error handling and providing more informative messages. The review focuses on ensuring the correctness and clarity of the added error handling.

[mgoin]

Nice find!