[Perf] Set Flashinfer sparse MLA as default backend for FP8 kv cache

[wzhao18]

Purpose

This PR sets Flashinfer sparse MLA as default backend for FP8 KV cache for better performance.

Test Plan

Test Result

Kernel microbenchmark results: #35807

E2E results with different TP (with EP enabled)

• Flashinfer shows significantly better performance across the pareto for TP=1, 4, 8.
• For TP=2, it shows slightly worse performance than flashMLA at higher concurrency. This is a known issue from kernel microbenchmark that the kernel has unusually bad performance at TP=2. For simplicity and the small E2E perf gap, we will set Flashinfer as the default backend for all TP sizes for FP8 KV types.

Command:

vllm serve \
    nvidia/DeepSeek-V3.2-NVFP4 \
    --trust-remote-code \
    --stream-interval 20 \
    --no-enable-prefix-caching \
    --kv-cache-dtype fp8 \
    --attention-backend {FLASHMLA_SPARSE, FLASHINFER_MLA_SPARSE} \
    --data-parallel-size {1,2,4,8} \
    --tensor-parallel-size {8,4,2,1} \
    --enable-expert-parallel \
    --gpu-memory-utilization 0.8

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[mergify]

Documentation preview: https://vllm--37252.org.readthedocs.build/en/37252/

[gemini-code-assist]

Code Review

This pull request modifies the attention backend selection logic to prioritize Flashinfer sparse MLA for FP8 KV cache on Blackwell GPUs. The change is implemented in vllm/platforms/cuda.py by updating _get_backend_priorities to consider the kv_cache_dtype. The corresponding documentation in docs/design/attention_backends.md has been updated to reflect the new backend priority. The pull request also refactors type hints in vllm/platforms/cuda.py by introducing from __future__ import annotations.

[wzhao18]

cc @MatthewBonanni

[MatthewBonanni]

LGTM, thanks! Can you update the documentation to discuss when one is preferred over the other? I should have done this when I conditioned it on num_heads and neglected to. You'll need to modify the generator script. It can be as simple as just adding an asterisk to each of those and a footnote somewhere

[wzhao18]

@MatthewBonanni Done. Thanks!