add mla_preprocess test script

[LinyuanLi0046]

• golden test for mla_preprocess
• include bf16 and fp16 dtype, with cachemode krope_ctkv, int8_nzcache and nzcache
• support two types of golden: unfused NPU small ops and Pytorch native

[gemini-code-assist]

Summary of Changes

Hello @LinyuanLi0046, 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 introduces a new, comprehensive test script for the mla_preprocess operation. The script is designed to validate the correctness of mla_preprocess across various configurations, including different data types (bfloat16, float16) and cache modes (krope_ctkv, int8_nzcache, nzcache). It employs two distinct golden reference implementations—one using NPU-specific operations and another using standard PyTorch—to ensure robust verification of the operator's behavior.

Highlights

• New Test Script: A new test script, test_mla_preprocess.py, has been added to provide comprehensive validation for the mla_preprocess operation.
• Comprehensive Data Type and Cache Mode Coverage: The tests cover bfloat16 and float16 data types, and include various cache modes such as krope_ctkv, int8_nzcache, and nzcache.
• Dual Golden Reference Implementations: The script supports two types of golden references: one based on unfused NPU small operations (golden1_torch_npu) and another on native PyTorch operations (golden2_pytorch), ensuring robust verification.

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive set of golden tests for the mla_preprocess operator, covering various data types and cache modes. The tests are well-structured. My review focuses on improving test correctness and code quality. I've identified a couple of high-severity issues: one where tests use print statements instead of assertions, which could lead to silent failures, and another where a test case uses an incorrect data type. I have also included several medium-severity suggestions to clean up unused code and improve overall quality.