[Feat] Support native Kimi-K2-Thinking native W4A16 quantized experts weights

[zhoux77899]

What this PR does / why we need it?

Adds W4A16 quantization method for the Kimi-K2-Thinking model and updates relevant modules to support the new quantization method.

• Implements complete W4A16 quantization method including weight packing/unpacking, per-group quantization parameter generation, post-processing logic and MoE method application.
• Adds parameters use_int4_w4a16, w1_offset and w2_offset, adjusts with_quant conditional logic to support W4A16 matrix multiplication.
• Adds packed_modules_model_mapping for Kimi-K2-Thinking model and processing logic for weight_packed field.

Does this PR introduce any user-facing change?

None.

How was this patch tested?

• vLLM version: 86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24
• vLLM main: vllm-project/vllm@86e178f

[github-actions]

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[gemini-code-assist]

Code Review

This pull request adds support for W4A16 quantization for MoE layers, specifically for Kimi-K2 models. The changes include a new quantization method AscendW4A16FusedMoEMethod, modifications to the MoE MLP logic to handle the new format, and updates to configuration files. Additionally, a bug fix in the rotary embedding implementation is included, which prevents a potential crash. The implementation for W4A16 seems consistent with existing quantization methods for Ascend NPUs. The bug fix is a welcome improvement to robustness.

[gemini-code-assist]

This change correctly prevents a potential AttributeError. In the previous implementation, if _rope_forward_oot was called when is_first_layer was False in the calling AscendRotaryEmbedding.forward_oot on its first execution, self.cos and self.sin would not have been initialized, leading to a crash. The addition of hasattr checks ensures the attributes exist before they are accessed, making the code more robust.

[github-actions]

This pull request has conflicts, please resolve those before we can evaluate the pull request.