Dcu: format code

[fuheaven]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @fuheaven, 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 enhancing code consistency and readability across several Python files related to DCU (AMD GPU) device handling and Flash Attention implementation. The changes primarily involve adjusting whitespace by removing extraneous blank lines and consolidating a multi-line function call into a single line, aligning the codebase with better formatting practices.

Highlights

• Code Formatting: Standardized code formatting by removing unnecessary blank lines within docstrings and at the end of methods, and reformatting a multi-line function call for improved readability.

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[gemini-code-assist]

Code Review

This pull request focuses on code formatting. While most changes involve removing whitespace, which is generally fine, there are a couple of areas that could be improved.

• In lightx2v_platform/ops/attn/dcu/flash_attn.py, a multi-line function call has been collapsed into a single line that is excessively long, harming readability. I've suggested restoring the multi-line format.
• In lightx2v_platform/ops/attn/dcu/__init__.py, the trailing newline has been removed, which goes against the PEP 8 style guide.

Please see the specific comments for details.

[gemini-code-assist]

This change removes the trailing newline from the file. The PEP 8 style guide recommends that all source files end with a single newline character (see warning W292). Please add a newline at the end of the file to adhere to this convention.

[gemini-code-assist]

This line is excessively long (over 130 characters), which significantly harms code readability and maintainability. The PEP 8 style guide recommends limiting line length (typically to 79 or 99 characters). Please reformat this call to span multiple lines, similar to how it was before this change.

Suggested change

	out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=causal, dropout_p=dropout_p)
	out = torch.nn.functional.scaled_dot_product_attention(
	q, k, v, attn_mask=None, is_causal=causal, dropout_p=dropout_p
	)