[cr-gao]

Purpose

Qwen3MoeDecoderLayer builds its sparse MoE block with a hard-coded Qwen3MoeSparseMoeBlock(...), while the model level already lets callers inject decoder_layer_type. A downstream layer subclass that only needs a different MoE block forward today has to either re-implement the whole __init__ (as MellumDecoderLayer does for attention) or reassign self.mlp.__class__ after construction to keep the loaded experts.

This adds moe_block_type: type[nn.Module] = Qwen3MoeSparseMoeBlock to the layer constructor, mirroring decoder_layer_type. Default behaviour is unchanged.

Downstream use: vLLM-Omni's opt-in HF-numerics mode for the Qwen3-Omni thinker (true on-policy RL, verl-project/verl-omni#637), which subclasses the layer and the MoE block.

Test Plan

No behaviour change for existing callers: the default argument is the class that was hard-coded. pre-commit run --files vllm/model_executor/models/qwen3_moe.py.

Test Result

ruff / ruff format clean. Qwen3-MoE model construction and weight loading exercised downstream through the vLLM-Omni subclass on a 4-layer Qwen3-Omni-30B-A3B thinker.

AI assistance was used for this change. I reviewed every line.

[claude]

Claude Code Review

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[github-actions]

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[mergify]

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

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