[quantization] Add w8a16 quantization support

[TmacAaron]

What this PR does / why we need it?

related to #4267

Does this PR introduce any user-facing change?

support w8a16 quantization now

How was this patch tested?

• vLLM version: v0.11.2
• vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces support for W8A16 quantization for dense models. The changes include a new quantization method AscendW8A16LinearMethod and its integration into the existing quantization framework. The implementation is straightforward and looks correct. I have one suggestion to refactor the apply method in vllm_ascend/quantization/w8a16.py to reduce code duplication, which will improve readability and maintainability.

[gemini-code-assist]

The if/else block contains duplicated calls to torch_npu.npu_weight_quant_batchmatmul. This can be refactored to improve readability and maintainability by determining the weight tensor first and then making a single call to the function.

        weight = layer.weight
        if is_310p():
            # On 300I Duo platform, we need transpose again if
            # using nz. This transpose can be skipped in torchair.
            weight = layer.weight.data.transpose(0, 1)

        output = torch_npu.npu_weight_quant_batchmatmul(
            x=x,
            weight=weight,
            antiquant_scale=layer.weight_scale,
            antiquant_offset=layer.weight_offset,
            bias=bias
        )
        return output

[realliujiaxu]

please fix DCO and lint, refer to https://docs.vllm.ai/projects/ascend/en/latest/developer_guide/contribution/index.html

[github-actions]

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