[Model] Apply SharedFusedMoE to glm4_moe.

[whx-sjtu]

Purpose

The class SharedFusedMoE was proposed by @bnellnm in PR #23273. The model glm4_moe has shared experts but we don't use SharedFusedMoE for glm4_moe, I'm not sure why, please cc @bnellnm.
Let glm4_moe uses SharedFusedMoE can solve two problems in vllm-ascend:

1. All-reduce related accuracy problem. In vllm-ascend we might use different MoE strategies in different situations according to num_tokens. For example, we might use all2all-based MoE strategy when num_tokens is small and use all-gather-based MoE strategy when num_tokens is large. This means in the same vllm service we sometimes perform all-reduce of shared experts together with router experts in maybe_all_reduce_tensor_model_parallel, and sometimes perform all-reduce of shared experts independently. However in currently glm4_moe modeling, whether perform all-reduce of shared experts independently is determined for sure in init by must_reduce_shared_expert_outputs, which conficts our implemention.
2. Same as PR [Kernels] Overlap shared experts with send/recv #23273, we also want to implement our conputition-communication overlap in Ascend backend, which can easily be done with SharedFusedMoE.

In conclusion, I think we should use SharedFusedMoE to glm4_moe. cc @bnellnm @robertgshaw2-redhat @wangxiyuan @LucasWilkinson @Yikun

Test Plan

No need to add new test.

Test ResultResult

all tests should pass

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[gemini-code-assist]

Code Review

This pull request refactors Glm4MoE to use SharedFusedMoE when shared experts are present, which is a good improvement for flexibility and performance optimization on Ascend hardware. However, I've found a few critical issues in the implementation that need to be addressed. There's a bug in the forward method that will cause a TypeError during runtime, and another issue in the __init__ method that leads to incorrect model behavior by ignoring the routed_scaling_factor. I've also suggested a refactoring to improve code clarity and maintainability by reducing duplication.

[bnellnm]

I only added SharedFusedMoE support for deepseek and llama4 since adding it everywhere would have been more disruptive. There's no reason it can't be applied to other models that use shared experts.

[bnellnm]

LGTM!

[Yikun]

Label ready to run all test