[Spec Decode] Add FlashInfer metadata grouping for DFlash SWA

[gq112]

Purpose

This PR is based on #40898 and complements #43081.

• [Spec Decode] Add Sliding Window Attention support to DFlash drafter #40898 adds generic DFlash + SWA support.
• [SpecDecode] Support DFlash with FlashInfer  #43081 adds base DFlash + FlashInfer support.

However, the combination of FlashInfer + SWA still requires one extra step: FlashInfer metadata builders cannot be shared across layers with different attention parameters.

In Qwen3-style SWA models, sliding-window and full-attention layers have different window_left values. Without splitting FlashInfer metadata groups by these layer-specific parameters, the builder may group incompatible layers together and fail with:' Window left is not the same for all layers '. This PR adds a backend-owned metadata grouping key and applies it when creating attention groups for:

• the main GPU runner, and
• the DFlash draft proposer.

FlashInfer uses this key to separate layers by:

• window_left
• logits_soft_cap
• scale
• sinks

Note: No FP8 KV-cache config propagation changes are included here; that is handled separately by #42692.

Files Changed

• vllm/v1/attention/backend.py

  • Adds a generic get_metadata_group_key() hook for attention backends.

• vllm/v1/attention/backends/flashinfer.py

  • Implements FlashInfer-specific metadata grouping using window_left, logits_soft_cap, scale, and sinks.
  • Handles DFlash non-causal prefill metadata for FlashInfer.

• vllm/v1/worker/gpu/attn_utils.py

  • Uses the backend metadata grouping key when initializing attention groups.

• vllm/v1/worker/gpu_model_runner.py

  • Applies the same grouping key in the main GPU model runner attention group creation.

• vllm/v1/spec_decode/llm_base_proposer.py

  • Applies the same grouping key for DFlash draft attention groups.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @gq112.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[gemini-code-assist]

Code Review

This pull request introduces support for Sliding Window Attention (SWA) and multiple KV cache groups within the DFlash speculative decoding framework. Key enhancements include the implementation of DFlashAttention to manage specific KV allocation semantics, updates to the FlashInfer backend to support non-causal attention, and logic in the GPU model runner to handle shared raw tensors with different physical layouts. The scheduler was also updated to refine prefix caching behavior for Eagle-style models. Review feedback identified critical bugs in vllm/v1/spec_decode/dflash.py where CommonAttentionMetadata (a dataclass) was incorrectly accessed using a non-existent replace method; the reviewer provided actionable suggestions to use the replace function instead.

[gemini-code-assist]

The CommonAttentionMetadata class is a dataclass and does not have a replace method. Since you have imported the replace function from vllm.config (which is an alias for dataclasses.replace), you should use it as a function instead of a method call.

            group_cad = replace(
                cad,
                block_table_tensor=self._get_dflash_block_table(kv_cache_gid, cad),
                slot_mapping=self._slot_mapping_buffers_by_gid[kv_cache_gid][1][
                    : cad.num_actual_tokens
                ],
                causal=False,
            )

[gemini-code-assist]

As noted above, group_cad is a dataclass and does not have a replace method. Use the replace function instead.

Suggested change

	common_attn_metadata=group_cad.replace(causal=True),
	common_attn_metadata=replace(group_cad, causal=True),