Quick Fix by adding conditional import for flash_attn_varlen_func in flash_attn

[xuechendi]

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Purpose

Fix comments: #19560 (comment)

Test Plan

Test Result

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @xuechendi, 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 refactors the import logic for specific Flash Attention functions to make them conditionally available based on the underlying hardware platform. The primary goal is to prevent import-time errors on systems where these specialized functions are not supported, thereby improving the robustness and compatibility of the attention backend.

Highlights

• Conditional Imports: Implemented conditional imports for flash_attn_varlen_func and get_scheduler_metadata within vllm/v1/attention/backends/flash_attn.py. These functions are now only imported if the is_flash_attn_varlen_func_available() check passes, preventing import errors on unsupported platforms.
• Platform Availability Check: Introduced a new utility function, is_flash_attn_varlen_func_available(), in vllm/attention/utils/fa_utils.py. This function determines if the current platform is CUDA or XPU, serving as the condition for the aforementioned conditional imports.
• Import Refinement in Utilities: Adjusted vllm/attention/utils/fa_utils.py to ensure that flash_attn_varlen_func is only assigned from ops within the primary conditional block (likely for CUDA/XPU platforms). An else branch was added to handle cases where these specific ops might not be available, ensuring reshape_and_cache_flash is still imported from _custom_ops.

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[gemini-code-assist]

Code Review

This pull request introduces a conditional import for flash_attn_varlen_func and get_scheduler_metadata to prevent import errors on platforms where flash attention is not available. This is achieved by adding a new helper function is_flash_attn_varlen_func_available.

While the change in vllm/v1/attention/backends/flash_attn.py correctly uses this new function, the corresponding change in vllm/attention/utils/fa_utils.py introduces a potential issue. The new else block for non-CUDA/XPU platforms incorrectly uses CUDA-specific custom operations, which could lead to runtime errors. I've left a comment with a high severity to address this.