[Bugfix][Attention] Enable FlashInfer mm-prefix attention

[lesj0610]

Purpose

Allow eligible multimodal PrefixLM / mm-prefix batches to stay on the native FlashInfer attention backend.

The mask itself now lives in FlashInfer, as a batch-prefill wrapper whose attention variant evaluates it per KV tile from compact per-query ranges. This PR is the vLLM adapter for that wrapper: it builds the ranges from request metadata, decides when the path is eligible, and calls the wrapper. An earlier revision of this PR carried the attention variant and its JIT ABI in-tree. Following review, both were removed, leaving only the vLLM adapter.

Dependency: FlashInfer upstream PR 5189, open at the time of writing. The FlashInfer release currently pinned by vLLM does not provide this wrapper, so supports_mm_prefix() returns False and the backend is demoted before selection. The adapter can become eligible once vLLM pins a release containing the wrapper; support is still advertised only after the module builds successfully for the requested configuration.

On SM100 the composite multimodal-prefix backend that landed in main is offered ahead of FlashInfer whenever a batch carries mm-prefix ranges. This PR puts FlashInfer first there: it serves the whole batch in one kernel when its own combination validates, and the composite stays the fallback for what it rejects. Selection validates all candidates and chooses the valid backend with the highest priority, so configurations FlashInfer rejects still fall through to the composite.

Changes

• Construct FlashInfer's mm-prefix batch-prefill wrapper instead of declaring an attention variant here. The in-tree CUDA declaration, its JIT URI and versioning, the additional tensor and scalar argument lists, and the direct module-generator call are all gone from this PR.
• Resolve the wrapper by name at call time rather than importing it at module scope. A FlashInfer without it has to demote this backend before selection; a module-scope import would instead raise ImportError for every user of this backend.
• Advertise supports_mm_prefix() only after the capability probe has built the module once through FlashInfer's public generator, and re-probe per layer group in supports_combination() with that group's own head size and resolved KV dtype (hybrid models such as Gemma4 select their backend per group). FLASHINFER_DISABLE_JIT, a missing toolchain, or a FlashInfer without the wrapper demote the backend before selection instead of crashing startup after it; the builder re-checks its exact combination at engine start as a fail-fast backstop.
• Hand the ranges to the wrapper as (N, 2) rows, which is its public contract, and slice the prefill portion as mm_ranges_gpu[num_decode_tokens:]. The wrapper rejects a non-contiguous range tensor rather than copying it, and a row slice of this row-major buffer is contiguous.
• Pass q_scale, k_scale and v_scale to run() instead of folding q_scale * k_scale into an sm_scale scalar by hand. This delegates scale handling to the wrapper's public API instead of reproducing it in vLLM: plan() carries sm_scale, the wrapper folds the query and key scales, and the value scale is applied to the output.
• Unquantized caches (auto/float16/bfloat16) are served. FP8, nvfp4_4over6, and nvfp4 on SM100 are rejected for the mm-prefix path. This PR does not claim to enable an end-to-end selectable mm-prefix NVFP4 configuration.
• Fail closed on the remaining unsupported combinations: attention sinks, DCP, logits_soft_cap > 0, explicitly forced trtllm-gen attention, and trtllm-only kernel page sizes (>= 128 rejected at selection time; large pages are no longer advertised for mm-prefix models).
• Keep the plain causal wrapper planned alongside the mm wrapper and dispatch between them at forward time on mm_prefix_query_range_tensor, honoring Gemma4's late clear of that field for its full-attention layer groups (the field name is shared with the FlashAttention backend for exactly that contract).
• Classify short chunked-prefill extends as prefills on mm batches (treat_short_extends_as_decodes=False) so a tail inside a bidirectional range keeps access to the future tokens of its range, and carry mm_req_doc_ranges and is_prefilling through DBO metadata slicing. Both fields are already on CommonAttentionMetadata; what this PR adds is re-keying the range dict to ubatch-local request indices and slicing is_prefilling per side, which the ubatch split did not do.
• Offer FLASHINFER before TRITON_FLASHINFER in the SM100 priority list when use_mm_prefix is set. The composite reaches these batches by routing the bidirectional spans to Triton and the causal remainder to FlashInfer; FlashInfer does both in one kernel when it validates, and the composite still answers for an fp8 cache, a trtllm-only page size, or a build whose module will not compile.
• Forward use_rswa through FlashInferBackend.validate_configuration(). This is a compatibility fix, not R-SWA support: the base method gained the argument when the composite landed, and this backend overrides that method, so without it every selection through FlashInfer raised TypeError.
• The two selector tests that enumerate the mm-prefix fallback order assumed FlashInfer could not serve these batches. Both now take that as a parameter and assert the order either way: FlashInfer when its combination validates, the composite or the Triton fallback when it does not.
• Build the per-query range metadata only when fill_mm_prefix_query_ranges() reports covered tokens (> 0) — a {request: []} text-only dict or a decode-only batch keeps the plain causal path and never touches the mm wrapper.

Design

No mask is materialized. An earlier revision of this PR built a dense boolean mask and handed it to FlashInfer's packing path -- O(qo_len * kv_len) per step -- 16384 scheduled tokens against a 262144-token context is a 4 GiB mask. Nothing here builds one. The wrapper's attention variant evaluates the whole expression in-kernel, from the compact per-query [start, end] rows that fill_mm_prefix_query_ranges() already produces:

(causal AND sliding_window) OR mm_prefix

Those ranges are 128 KiB at 16384 scheduled tokens and do not grow with context. vLLM's side of that contract is one int32 (N, 2) buffer sized by scheduled tokens, never by context length.

Test Plan

The adapter tests need a FlashInfer that provides the wrapper, so point at a checkout of FlashInfer upstream PR 5189. Against the pinned release the wrapper-dependent tests skip instead.

FLASHINFER_PR_CHECKOUT=/path/to/flashinfer-checkout

CUDA_VISIBLE_DEVICES=0 \
PYTHONPATH="$FLASHINFER_PR_CHECKOUT" \
pytest \
  tests/v1/attention/test_mm_prefix.py \
  tests/v1/attention/test_attention_splitting.py \
  -q -rs

The selector and override checks do not need it:

pytest \
  tests/kernels/attention/test_attention_selector.py::test_hopper_mm_prefix_selects_triton_flash_attn \
  tests/kernels/attention/test_attention_selector.py::test_mm_prefix_priority_without_changing_causal_default \
  tests/v1/attention/test_mm_prefix.py::test_flashinfer_mm_prefix_validate_configuration_rejects_dcp \
  -q -rs

Covers, for the FlashInfer path specifically: end-to-end builder → impl → wrapper against a dense float32 reference on paged KV with a sliding window narrower than the widest range (so the clamp-off and clamp-on formulas genuinely differ and both are checked), the (N, 2) shape and contiguity of the prefill range slice, the cleared-metadata fallback to the causal wrapper, capability rejections (NVFP4/FP8/sinks/DCP/page-size/wrapper-unavailable), per-group probe arguments for hybrid head sizes, kernel block-size advertising for mm and non-mm models, builder fail-fast on soft-cap and forced trtllm, text-only and decode-only gating, short-extend reclassification, and DBO slicing (field preservation plus a split request whose per-side fills must reproduce the unsplit assignment, inclusive end included).

The kernel-level tests — the mask against a dense reference, window semantics, partial query tiles, packed NVFP4 — moved to FlashInfer with the variant.

Test Result

On one SM80 device, with the FlashInfer branch that carries the wrapper:

pytest tests/v1/attention/test_mm_prefix.py tests/v1/attention/test_attention_splitting.py -q -rs
45 passed, 22 skipped

All 22 skips are hardware: 10 pre-existing FA4-only cases, and 12 composite cases that need Blackwell or Hopper. No FlashInfer adapter test skips.

The targeted selector and override checks completed with 21 passed. They cover native FlashInfer availability in both directions, the Hopper and SM100 priority order, and use_rswa forwarding through the FlashInfer override.

On a FlashInfer without the wrapper the capability probe returns False and supports_combination() returns the rejection that backend selection consumes, so this backend is demoted before it is chosen. That path is covered by the selector tests rather than by a serving run; no fallback serving was executed for this revision.

ruff check / ruff format --check passed; commit hooks passed, including mypy for Python 3.10.

Historical serving measurements from the earlier in-tree variant

These results are retained as historical performance context only and are not validation of the current wrapper-based adapter. They were measured with the attention variant declared in-tree, before it moved to FlashInfer, and have not been repeated against this revision.

Run on 2 SM80 devices, -tp 2, BF16 weights and KV, a 31B multimodal model, --max-model-len 8192, --max-num-batched-tokens 8192, --max-num-seqs 128, --gpu-memory-utilization 0.85. FlashInfer carried the mm-prefix batches end to end; a single 896x896 image request completed successfully, and no workspace or KV-cache assertion fired.

These runs were made on a tree that also carried persistent attention-workspace profiling, which they needed: the mask-owning wrapper is created on the first batch that carries bidirectional ranges, which is after the arena is locked, so it has to be reserved while profiling runs. main has no such reservation machinery today, so this PR alone does not reach that path.

Same-host end-to-end comparison against the TRITON_ATTN baseline, using the image workload definition and seeds from the composite PR's repro block: an 896x896 image plus a configured 1024-token text input and 1024-token output; multimodal expansion produced 1295-1297 measured input tokens, --temperature 0 --ignore-eos, prefix and mm caches reset between runs. The server configurations differ in attention backend, block size, and available KV capacity, so this compares complete serving configurations rather than isolating the mm-prefix kernel. This is not a reproduction of that PR's numbers — different architecture and parallelism, so only the within-host results are meaningful.

Values are TRITON_ATTN / FLASHINFER, bold is better (higher tok/s, lower latency):

Conc	Total tok/s	Output tok/s	TTFT (ms)	TPOT (ms)	E2EL (ms)
1	14.2 / 58.5	6.3 / 25.8	3884 / 2167	155.4 / 36.6	162860 / 39652
32	437.8 / 426.7	193.3 / 188.4	31441 / 30559	134.9 / 140.1	169446 / 173846
128	416.2 / 438.0	183.7 / 193.4	280114 / 275765	186.1 / 190.2	470526 / 470314

Caveats, so the table is not read for more than it says:

• The concurrency-32 FlashInfer figure is the mean of three seeds (10032/10033/10034: 426.6 / 427.0 / 426.7, spread 0.09%). The TRITON_ATTN figure on that row is a single seed, so its 2.6% lead carries an unmeasured seed-variance error bar and should not be read as a real difference. Concurrency 1 and 128 are single-seed on both sides.
• At concurrency 32, the measured inputs are 1295-1297 tokens with 1024 output tokens, putting the maximum full-request footprint at approximately 74K tokens, above either KV budget; concurrency 128 is more constrained. Scheduling and cache pressure can therefore affect these rows, and they do not isolate attention-kernel cost.
• The two arms do not get identical KV budgets: FlashInfer reserves its attention workspace during profiling and lands at 49,593 tokens against the baseline's 51,515. The FlashInfer configuration is therefore measured with approximately 3.7% less KV capacity.

Notes

• When the native FlashInfer mm-prefix backend is eligible, its execution path uses native fa2 prefill; trtllm-gen and cascade attention are not selected for those batches.
• mm-prefix batches pay full-rectangle KV traversal on the prefill (the same compute shape the dense custom mask had) — the win of this rework is memory, not FLOPs: no O(qo_len * kv_len) mask is ever materialized.
• The DBO/ubatch metadata slicing gap noted in the earlier revision is fixed here (mm_req_doc_ranges re-keyed, is_prefilling sliced) and covered by tests.

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Summary by CodeRabbit

• New Features

  • Added FlashInfer support for multimodal prefix attention, including optimized prefilling, sliding-window handling, and validated fallback behavior.
  • Preserved multimodal metadata and prefill classification when requests are split into smaller batches.

• Bug Fixes

  • Improved consistency between split and unsplit multimodal processing, including correct range boundaries and optional metadata handling.

• Tests

  • Added comprehensive coverage for supported configurations, fallbacks, validation, cache execution, and multimodal prefix ranges.

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📝 Walkthrough

Walkthrough

FlashInfer adds multimodal prefix attention with FA2 JIT masking, configuration checks, range metadata staging, runtime dispatch, and paged-KV execution. Ubatch slicing preserves multimodal ranges and prefilling state. Tests cover supported configurations, fallbacks, and split execution.

Changes

Multimodal prefix attention

Layer / File(s)	Summary
JIT capability and configuration validation vllm/v1/attention/backends/flashinfer.py, tests/v1/attention/test_mm_prefix.py	FlashInfer adds a cached multimodal-prefix FA2 JIT variant. Backend checks reject unsupported dtypes, cache settings, DCP, sinks, large pages, TRTLLM, and unavailable JIT variants.
Metadata construction and runtime dispatch vllm/v1/attention/backends/flashinfer.py, tests/v1/attention/test_mm_prefix.py	The backend stages query ranges, plans a dedicated paged-KV prefill wrapper, preserves short prefills, and selects multimodal-prefix or causal execution at runtime.
Ubatch metadata preservation vllm/v1/worker/ubatch_utils.py, tests/v1/attention/test_attention_splitting.py	Ubatch metadata re-keys multimodal document ranges to local request indices and preserves absolute bounds, optional fields, and is_prefilling.

Estimated code review effort: 4 (Complex) | ~45 minutes

Sequence Diagram(s)

sequenceDiagram
  participant RequestBatch
  participant UbatchUtils
  participant FlashInferBackend
  participant FIPrefill
  participant VllmMMPrefixAttention
  RequestBatch->>UbatchUtils: slice requests into ubatches
  UbatchUtils->>FlashInferBackend: provide local multimodal ranges and prefilling state
  FlashInferBackend->>FIPrefill: stage ranges and plan prefill
  FIPrefill->>VllmMMPrefixAttention: execute custom masked paged-KV attention
  VllmMMPrefixAttention-->>FlashInferBackend: return attention output

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Suggested reviewers: lucaswilkinson, mgoin

Merge Risk: 🔵 Low · up to b3280

This change enables multimodal prefix attention without materializing a dense mask and disables the feature when the required runtime support is unavailable. It is mergeable with owner follow-up on one localized lint correction before or after merge.

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Title check	✅ Passed	The title clearly and concisely summarizes the main change: enabling FlashInfer mm-prefix attention support and presenting it as an attention bugfix.

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[lesj0610]

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In `@tests/v1/attention/test_mm_prefix.py`:
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