[Refactor]refactor 310p ops and add ut

[pu-zhe]

What this PR does / why we need it?

This pull request focuses on a significant refactoring effort within the vllm-ascend project, specifically targeting operations optimized for the Ascend 310P hardware. The changes aim to streamline the implementation of core components like quantization and multi-head attention, making the codebase more maintainable and robust. Concurrently, new unit tests have been introduced to ensure the correctness and reliability of these refactored modules.

Does this PR introduce any user-facing change?

No

How was this patch tested?

E2E test with qwen3-32b w8a8

• vLLM version: v0.15.0
• vLLM main: vllm-project/vllm@d7e17aa

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @pu-zhe, 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 focuses on a significant refactoring effort within the vllm-ascend project, specifically targeting operations optimized for the Ascend 310P hardware. The changes aim to streamline the implementation of core components like quantization and multi-head attention, making the codebase more maintainable and robust. Concurrently, new unit tests have been introduced to ensure the correctness and reliability of these refactored modules.

Highlights

• Refactoring of 310P Operations: Key operations specific to the Ascend 310P platform, including attention mechanisms and activation functions, have been refactored for improved efficiency and clarity.
• Enhanced Unit Test Coverage: New and comprehensive unit tests have been added for quantization methods and model configuration on the Ascend 310P, ensuring greater reliability and correctness.
• Codebase Standardization: Several classes and functions have been renamed to standardize naming conventions, contributing to better code consistency and maintainability.

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Changelog

• tests/ut/_310p/quantization/test_modelslim_config.py
  • Added comprehensive unit tests for AscendModelSlimConfig310, verifying its get_quant_method logic for both linear and fused MoE layers.
• tests/ut/_310p/quantization/test_w8a8.py
  • Introduced new unit tests for AscendW8A8LinearMethod310, covering its weight and parameter retrieval, and validating the apply method's behavior with various input types.
• vllm_ascend/_310p/ops/activation.py
  • Removed explicit prefetching and waiting calls within the AscendSiluAndMul310.forward method, simplifying the activation function's implementation.
• vllm_ascend/_310p/ops/mm_encoder_attention.py
  • Eliminated the einops dependency and refactored the forward_oot method to use direct tensor reshaping for query, key, and value, improving the clarity and potentially the performance of the attention mechanism.
• vllm_ascend/_310p/quantization/methods/w8a8_static.py
  • Renamed the quantization class from AscendW8A8LinearMethod310P to AscendW8A8LinearMethod310 for naming consistency.
• vllm_ascend/_310p/quantization/modelslim_config.py
  • Renamed the helper function create_scheme_for_layer_310p to create_scheme_for_layer and updated its references throughout the file.

Activity

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[gemini-code-assist]

Code Review

This pull request refactors several operations for the 310p platform and adds corresponding unit tests. The changes simplify the attention mechanism and activation functions by removing prefetching logic and unused dependencies. While the new unit tests for quantization methods are a good addition, they contain incorrect assertions regarding bias handling. More critically, a bug has been introduced in the refactored attention logic that will cause a runtime error. Please address these issues.