mlapo fit different hidden state dim

[Todobe]

Modify the MLAPO op to be compatible with hidden state dimensions of different sizes.

Test Result：

DeepSeek（hidden state dim：7168）

LongCat（hidden state dim：6144）

[gemini-code-assist]

Summary of Changes

Hello @Todobe, 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 significantly improves the adaptability of the MLAPO operation by enabling it to work with neural network models that have varying hidden state dimensions. This change allows the operation to be more broadly applicable without requiring code modifications for different model architectures, making it more robust and reusable.

Highlights

• Dynamic Hidden State Dimension: The MLAPO operation has been modified to dynamically accept and utilize different hidden state dimensions, moving away from a hardcoded constant.
• Code Refactoring: The hardcoded HIDDEN_STRATE constant has been removed from various files and replaced with a hiddenStateDim parameter or member variable, enhancing flexibility.
• Memory Allocation Adjustment: Buffer allocation calculations within the kernel operations (mla_preprocess_mix_bf16.hpp and mla_preprocess_mix_fp16.hpp) now correctly use the dynamic hidden state dimension.

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[gemini-code-assist]

Code Review

This pull request successfully refactors the MLAPO op to support variable hidden state dimensions, removing the previously hardcoded value. The changes are well-implemented across the host and kernel code, passing the dynamic dimension down from the op's input tensor. My review includes a couple of suggestions to improve the readability and maintainability of buffer offset calculations within the kernel files, which currently use repetitive expressions.

[iforgetmyname]

turn this input var into a class var