[Platform] Refactor Platform attention backend selection to avoid breakpoint for OOT platform

[Isotr0py]

Purpose

• Currently, there are too many arguments used by platform's get_attn_backend_cls, while not all platforms use all of them. And it will also easily break OOT platform when introduce attention feature with new argument like use_mla and use_sink.
• To avoid this kind of mess and breakage, this PR wrap platform's attention selection arguments into hashable AttentionSelectorConfig, so that platform can use these arguments on demand and no longer need to update interface for each feature update.

Test Plan

Test Result

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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.
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[gemini-code-assist]

Code Review

This pull request refactors the attention backend selection mechanism by introducing an AttentionSelectorConfig NamedTuple. This new configuration object encapsulates various attention parameters such as head size, data type, KV cache data type, block size, MLA usage, sink token presence, sparse attention usage, and attention type. The get_attn_backend and _cached_get_attn_backend functions in vllm/attention/selector.py are updated to create and pass this single config object instead of multiple individual arguments. This change propagates throughout the platform-specific attention backend selection logic in vllm/platforms/cpu.py, vllm/platforms/cuda.py, and vllm/platforms/interface.py, where relevant methods like get_attn_backend_cls and get_valid_backends are modified to accept and utilize the AttentionSelectorConfig object, simplifying their signatures and improving parameter management. Additionally, logging for attention configurations is updated to use the __repr__ method of the new config object.

[mergify]

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merged. Please rebase the PR, @Isotr0py.

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[Isotr0py]

/gemini review

[gemini-code-assist]

Code Review

This pull request refactors the attention backend selection by introducing AttentionSelectorConfig to encapsulate the configuration parameters. This is a great improvement as it simplifies the function signatures in platform-specific modules and makes the interface more stable for out-of-tree platforms. The changes are applied consistently across all relevant files. I've found one minor issue with an incomplete __repr__ implementation which could affect debugging.

[mergify]

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[DarkLight1337]

LGTM, thanks for cleaning this up! cc @tjtanaa

[Isotr0py]

Just found that block_size is set to None here to bypass attention backend validation for state space model, otherwise the validation will fail:

[2025-12-15T14:12:44Z] models/language/generation/test_hybrid.py::test_models[5-64-hmellor/tiny-random-BambaForCausalLM] The fast path for Bamba will be used when running the model on a GPU
...
[2025-12-15T14:13:30Z] (EngineCore_DP0 pid=2108) ERROR 12-15 06:13:30 [core.py:866] ValueError: No valid attention backend found for cuda with AttentionSelectorConfig(head_size=64, dtype=torch.bfloat16, kv_cache_dtype=auto, block_size=48, use_mla=False, has_sink=False, use_sparse=False, use_mm_prefix=False, attn_type=decoder). Reasons: {FLASH_ATTN: [block_size not supported], FLASHINFER: [block_size not supported], TRITON_ATTN: [block_size not supported], FLEX_ATTENTION: [block_size not supported]}.

Perhaps we need to update FlashAttention backend's get_supported_kernel_block_sizes for state space model? @tdoublep