feat(transformers): add ut for cache/modeling_utils

[wcrzlh]

What does this PR do?

Fixes # (issue)

Adds # (feature)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
  documentation guidelines
• Did you build and run the code without any errors?
• Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• Did you write any new necessary tests?

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[gemini-code-assist]

Summary of Changes

Hello @wcrzlh, 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 significant enhancements to the mindone.transformers library by refactoring and adding new functionalities for model state dictionary loading and key management, alongside comprehensive unit tests for both cache_utils and modeling_utils. The changes aim to make model loading more robust, flexible, and better handle various scenarios such as shared parameters and key renaming.

Highlights

• Enhanced Model Loading Logic: Introduced new helper functions and methods in modeling_utils.py to refine how model state dictionaries are loaded, including handling of shared tensors and key remapping.
• Improved Static Cache Initialization: Modified StaticCache in cache_utils.py to provide more flexible initialization by allowing batch_size or max_batch_size to be specified, enhancing its usability.
• Comprehensive Unit Testing: Added new test files (test_cache_utils.py and test_modeling_utils.py) with extensive unit tests covering DynamicCache and StaticCache functionalities, various from_pretrained scenarios (including low_cpu_mem_usage and safetensors), and attention mask utilities.

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[gemini-code-assist]

Code Review

This pull request introduces unit tests for cache_utils and modeling_utils, which is a great addition for ensuring code quality and correctness. The changes also include a significant and valuable refactoring of the model loading logic in modeling_utils.py, making it more robust and maintainable. The changes to cache_utils.py for backward compatibility are also well-implemented. I have one suggestion regarding a wildcard import to improve code clarity.

[gemini-code-assist]

Using wildcard imports (from ... import *) is generally discouraged as it can pollute the namespace and make it unclear which names are being imported. This can lead to maintenance issues and potential name clashes. It's better to explicitly import the required names from cache_utils.

from .cache_utils import (
    Cache,
    DynamicCache,
    EncoderDecoderCache,
    HybridCache,
    MambaCache,
    OffloadedStaticCache,
    SlidingWindowCache,
    StaticCache,
)