[bugfix]fix multistream moe in torchair

[hust17yixuan]

What this PR does / why we need it?

the multistream moe in tochari only validate in decode, but can't be applied to chunked prefill, So add some judgments to isolate the scenario

Does this PR introduce any user-facing change?

No

How was this patch tested?

• vLLM version: v0.10.2
• vLLM main: vllm-project/vllm@f225ea7

[github-actions]

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[gemini-code-assist]

Code Review

This pull request aims to fix a bug in multistream Mixture of Experts (MoE) for the DeepseekV2 model. The changes ensure that a specific communication optimization (mla_moe_communication) is only active during the decode phase, which appears to be the correct behavior. The implementation is refactored to use a centralized forward context to determine the prefill/decode state, which improves consistency. However, this refactoring has introduced a dead parameter in one of the modified functions, which poses a maintainability risk. My review includes a comment to address this issue.

[gemini-code-assist]

Using forward_context.with_prefill makes the is_prefill parameter of this forward method unused. This is misleading and can lead to future bugs if other developers rely on this parameter, expecting it to have an effect.

To improve maintainability and prevent potential issues, the is_prefill parameter should be removed from the function signature of TorchairDeepseekV2RowParallelLinearReplaceAllreduce.forward. This will require updating the call site in vllm_ascend/attention/mla_v1.py to no longer pass this argument.