[Model] Fix bailing_moe accuracy problem

[zhaozx-cn]

Purpose

When using all2all-based MoE strategy in vllm-ascend, there is no need to do all-reduce after SharedFusedMoE forward. Applying maybe_all_reduce_tensor_model_parallel could fix accuracy problem, as it did for​ other MoE models like glm4_moe and deepseek_v2.

Test Plan

No need to add new test.

Test Result

all tests should pass

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
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• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

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[gemini-code-assist]

Code Review

This pull request addresses an accuracy issue in the bailing_moe model when using an all-to-all communication strategy for Mixture-of-Experts. The change replaces an unconditional tensor_model_parallel_all_reduce with a call to self.experts.maybe_all_reduce_tensor_model_parallel. This correctly delegates the decision of whether to perform an all-reduce to the MoE layer, which is aware of the communication strategy being used. This prevents an unnecessary and incorrect reduction when all-to-all is active, resolving the accuracy problem. The change is clean, targeted, and improves both correctness and design by centralizing the reduction logic. It also brings bailing_moe in line with other MoE models in the repository.

[heheda12345]

LGTM!