fix(transformers) seamless_m4t ut bugs

[Dong1017]

What does this PR do?

Fixes

The torch-version Seamless_m4t modules will raise "RuntimeError: mixed dtype (CPU): all inputs must share the same datatype", in the case when passing inputs with float datatype.
It can be directly avoided by changing hidden_states = self.layer_norm(hidden_states)
to hidden_states = self.layer_norm(hidden_states.to(self.layer_norm.weight.dtype))
in seamless_m4t/modeling_seamless_m4t.py.
But, instead of changing the HF codes, a mask is designed to enforce the type conversion (from float to int) in UTs.

Notes

Refer to #1293

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
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• 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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Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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[gemini-code-assist]

Summary of Changes

Hello @Dong1017, 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 addresses a RuntimeError encountered in seamless_m4t unit tests, which occurred due to mixed data types when passing different input types. The solution involves introducing a mechanism to explicitly enforce type conversion for specific input tensors within the test suite, preventing the error without altering the core hf module code. This ensures the robustness of the unit tests by standardizing input data types where necessary.

Highlights

• Test Parameterization Update: Modified the pytest.mark.parametrize decorator and the test_named_modules function signature to include a new inputs_type_idx parameter.
• Input Type Conversion Logic: Implemented conditional type conversion for input tensors within test_named_modules, ensuring that specific inputs are cast to long() (for PyTorch) or ms.int64 (for MindSpore) based on the inputs_type_idx list.
• Test Case Configuration: Updated various test case configurations within get_config to provide the appropriate inputs_type_idx lists, addressing the RuntimeError caused by mixed data types in seamless_m4t unit tests.

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[gemini-code-assist]

Code Review

This pull request addresses a mixed dtype error in the seamless_m4t unit tests by introducing a mechanism to selectively cast input tensors to integer types. This is achieved by adding an inputs_type_idx parameter to the test cases. While the overall approach is correct, I've found a few issues with the implementation. The lists of indices for type conversion are incorrect for several test cases, which would lead to tensors like labels and input_ids being improperly cast to float types. I've provided suggestions to correct these lists. Additionally, the type casting logic for PyTorch tensors is inefficient and can be simplified, for which I've also suggested a refactoring. Addressing these points will make the tests more robust and correct.