[Feat.]: support 310p w8a8

[Tflowers-0129]

What this PR does / why we need it?

Introduced 310P W8A8 Quantization Support: New modules and methods have been added to enable W8A8 static quantization specifically for the Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now dynamically loads the appropriate quantization configurations (AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization method for 310P is provided, handling the specifics of weight and activation quantization, including input parameter broadcasting and weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration class for 310P integrates the new W8A8 linear method for both standard linear layers and vocabulary parallel embeddings, ensuring proper quantization application.

Does this PR introduce any user-facing change?

How was this patch tested?

• vLLM version: v0.14.1
• vLLM main: vllm-project/vllm@dc917cc

[gemini-code-assist]

Summary of Changes

Hello @Tflowers-0129, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request integrates W8A8 static quantization support for the Ascend 310P platform within the vllm-ascend project. It introduces platform-specific quantization methods and configurations, ensuring that models can leverage efficient 8-bit weight and activation quantization when deployed on 310P devices. The changes include new modules for 310P quantization logic and a modification to the platform's initialization to conditionally load these specialized configurations.

Highlights

• Introduced 310P W8A8 Quantization Support: New modules and methods have been added to enable W8A8 static quantization specifically for the Ascend 310P platform.
• Platform-Specific Quantization Configuration Loading: The system now dynamically loads the appropriate quantization configurations (AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether the current hardware is an Ascend 310P device.
• Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization method for 310P is provided, handling the specifics of weight and activation quantization, including input parameter broadcasting and weight data manipulation.
• Extended AscendModelSlimConfig for 310P: A specialized configuration class for 310P integrates the new W8A8 linear method for both standard linear layers and vocabulary parallel embeddings, ensuring proper quantization application.

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[gemini-code-assist]

Code Review

This pull request introduces support for w8a8 quantization on 310p devices by adding specific implementations for quantization methods and configurations. The changes are mostly sound, but I've identified a critical issue in w8a8_static.py related to inefficient and incorrect parameter registration that should be addressed. Additionally, please note that the pull request title and description do not fully align with the repository's style guide. The summary sections are currently empty, and the title is missing the [Branch] and [Module] tags.