【0.11.0-dev】optimization of kimi-k2 in cann8.3

[Levi-JQ]

What this PR does / why we need it?

In cann8.3， npu_moe_gating_top_k operator can support expert nums with 384, so kimi can use the operator to get better preformance.

Does this PR introduce any user-facing change?

How was this patch tested?

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces an optimization for the 'kimi' model on CANN 8.3 by enabling a fused MoE gating kernel. The changes are applied across several files related to MoE, including different quantization paths. My review includes a critical comment about an inconsistency in one of the quantization files that could lead to incorrect behavior, and a high-severity comment about code duplication that impacts maintainability. Addressing these points will improve the robustness and clarity of the implementation.

[gemini-code-assist]

There's an inconsistency in how the model type is determined here. This file checks global_num_experts directly, while other files in this PR (e.g., torchair_w8a8_dynamic.py) check against the effective number of experts (global_num_experts - global_redundant_expert_num). This could lead to incorrect kernel selection if global_redundant_expert_num is non-zero, which would be a bug. The logic should be consistent across all files. The original logic if global_num_experts == 256: was also likely incorrect for the same reason.

Suggested change

	if global_num_experts == 256 or (global_num_experts == 384 and
	torch.version.cann.startswith("8.3")):
	if (global_num_experts - global_redundant_expert_num == 256) or \
	((global_num_experts - global_redundant_expert_num == 384) and torch.version.cann.startswith("8.3")):

[gemini-code-assist]

The logic to identify deepseek_v3_r1 and kimi models using magic numbers (256, 384), and the check for CANN version 8.3, is duplicated across multiple files (experts_selector.py, torchair_fused_moe.py, torchair_w8a8_dynamic.py, and torchair_w4a8_dynamic.py). This makes the code harder to maintain and increases the risk of inconsistencies when adding support for new models or CANN versions. Consider centralizing this logic into a helper function or a configuration object for better maintainability and readability.