Summary of Changes

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This pull request enhances the fused Mixture-of-Experts (MoE) system to properly integrate and process shared experts, a critical feature for models like Qwen3.5. The changes ensure that the MoE layer can correctly handle the unique gating and activation requirements of shared expert configurations, particularly for the 310P Ascend environment. This improves the model's compatibility and performance with advanced MoE architectures.

Highlights

• Shared Experts Support: Added comprehensive support for shared experts within the fused Mixture-of-Experts (MoE) implementation, specifically tailored for Qwen3.5 and Qwen3-Next models on the 310P Ascend platform.
• Two-Part Shared Expert Processing: Introduced new internal methods, _shared_experts_part1 and _shared_experts_part2, to manage the two-stage processing of shared expert hidden states and their gating mechanisms.
• Qwen3.5 Specific Gating: Implemented a dedicated gate path for Qwen3.5/Qwen3-Next shared experts, incorporating torch.nn.functional.sigmoid for activation in the _shared_experts_part2 method.
• Runner Reinitialization: Modified the __init__ method to ensure the MoE runner is reinitialized after shared experts and gate configurations are set, facilitating correct custom operation dispatch for shared expert processing.
• Internal Router Property: Defined the is_internal_router property to return False, indicating that the 310P Ascend path expects router logits from the model's forward pass rather than an internal router.

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