[V1] Use FlashInfer by default on Blackwell GPUs

[mgoin]

Purpose

FlashInfer has a specific backend for NVIDIA Blackwell so it is much more performant than FlashAttention2 default in V1 (FA3 is unsupported). If a user has it installed, I think we should choose it by default, which this PR achieves. See this comment for benchmarks #18095 (comment)

This PR also adds is_device_capability to the platform interface for exact cc checking since we only want this for SM 10.0

Test Plan

Test locally on a B200 that FlashInfer is enabled by default

Test Result

On B200 without flashinfer installed:

vllm serve facebook/opt-125m --enforce-eager
...
INFO 06-04 13:16:58 [cuda.py:234] FlashInfer failed to import for V1 engine on Blackwell (SM 10.0) GPUs; it is recommended to install FlashInfer for better performance.
INFO 06-04 13:16:58 [cuda.py:240] Using Flash Attention backend on V1 engine.

On B200 with flashinfer installed:

uv pip install https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl
vllm serve facebook/opt-125m --enforce-eager
...
INFO 06-04 13:15:34 [cuda.py:228] Using FlashInfer backend on V1 engine by default for Blackwell (SM 10.0) GPUs.

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[gemini-code-assist]

Hello @mgoin, 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!

Summary of Changes

As gemini-code-assist, I've reviewed this pull request. The primary goal is to enhance performance on NVIDIA Blackwell GPUs by making FlashInfer the default attention backend when it's available. The author notes that FlashInfer has a specific, more performant backend for Blackwell compared to FlashAttention2 (FA3 is unsupported on V1). This change modifies the backend selection logic within vllm/platforms/cuda.py to prioritize FlashInfer for Blackwell architectures (compute capability 10.x or 100), falling back to other available backends if FlashInfer is not installed.

Highlights

• Prioritize FlashInfer on Blackwell: FlashInfer is now the preferred attention backend for NVIDIA Blackwell GPUs (compute capability 10.x / 100) in both V1 and V2 engine paths.
• Conditional Selection: The FlashInfer backend is only selected if the flashinfer library is successfully imported. If the import fails, the system falls back to the existing backend selection logic (e.g., Flash Attention).
• Backend Selection Logic Update: The get_attn_backend_cls function in vllm/platforms/cuda.py is updated to check for Blackwell capability before other architectures like Ampere+.

Changelog

• vllm/platforms/cuda.py
  • Modified the get_attn_backend_cls function to add checks for NVIDIA Blackwell GPUs (DeviceCapability 10,0 for V1 engine and 100 for V2 engine).
  • Introduced logic to attempt importing the flashinfer library when a Blackwell GPU is detected.
  • If flashinfer is successfully imported on a Blackwell GPU, the FlashInferBackend is returned.
  • Included exception handling for the flashinfer import to ensure graceful fallback to other backends if FlashInfer is not installed.

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---

Blackwell's power shines,
FlashInfer speeds up the lines,
Fast attention flies.

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[gemini-code-assist]

Code Review

This pull request effectively enables FlashInfer by default for Blackwell GPUs, which is a good performance enhancement. The changes are well-targeted within vllm/platforms/cuda.py.

I have a couple of suggestions primarily around error handling and logging to improve debuggability, particularly if flashinfer fails to import or initialize.

Summary of Findings

• Logging for FlashInfer Import Failure (V1 Engine): The V1 engine path currently silently fails over if flashinfer import raises an exception. Adding a log message would improve debuggability.
• Exception Specificity for FlashInfer Import: Both V1 and V2 paths use a broad except Exception. Consider using except ImportError: if that's the primary expected failure, or adjusting log messages if other import-time exceptions from flashinfer itself are meant to be caught. This enhances clarity and helps in diagnosing issues.
• Consistency in GPU Capability Check: The V1 engine path uses cls.get_device_capability() == DeviceCapability(10, 0) while the V2 path uses cls.has_device_capability(100) to check for Blackwell GPUs. Both are functionally equivalent. Using DeviceCapability(10, 0) is arguably more explicit, but the current approach is also acceptable. This was not commented on directly due to review settings.

Merge Readiness

The pull request is well-focused and addresses the goal of enabling FlashInfer for Blackwell GPUs. However, there are a few medium-severity suggestions regarding logging and exception handling that would improve the robustness and maintainability of the code. I recommend addressing these points before merging. As an AI, I am not authorized to approve pull requests; please ensure further review and approval from the maintainers.

[youkaichao]

why a new function rather than using get_device_capability() == 100?

[mgoin]

@youkaichao get_device_capability() returns Optional[DeviceCapability] so the full check would have to check for None and construct the DeviceCapability tuple - I think we will need this utility in more places going forward due to capability breaks between Hopper and Blackwell