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[None][chore] Update flashinfer-python from 0.6.18 to 0.7.2 - #20911
Pin flashinfer-python 0.6.18 -> 0.7.2 in requirements.txt, security_scanning/pyproject.toml and ATTRIBUTIONS-Python.md; nvidia-cudnn-frontend moves to 1.31 to match (see Open).
Why. Main's vendored tensorrt_llm/_torch/attention/backends/prims_ts tree (yuxianq's branch at aa709061, promoted in #21006) launches the QToken-KvBlock-Sparse attention through a metadata kernel that lives in the FlashInfer wheel (flashinfer.jit.gen_prims_ts_q_token_kv_block_sparse_metadata_module). 0.6.18 does not ship that module, so today the QSA sparse core in #20714 cannot run and falls back to Triton. 0.7.2 is the first release that ships it.
Compat hunks. The vendored Python tree already contains FlashInfer PR flashinfer-ai/flashinfer#5351 (the optimized attention kernel), but the metadata kernel in the 0.7.2 wheel predates it. The vendor patch therefore adds three hunks to q_token_kv_block_sparse_metadata.py so the new Python tree drives the old metadata kernel: the 13-argument launch (no locator_head_multiplier), allow_head_aware_locators=False, and query_major_memberships=False. They go away when the pin moves to a release that ships flashinfer-ai/flashinfer#5351's csrc.
cuDNN ragged prefill.flashinfer-ai/flashinfer#5133 (in 0.7.2) made the cudnn prefill backend take token-unit qo_indptr/kv_indptr like every other backend and reject element-unit offsets. The no-KV-cache ragged path in backends/flashinfer.py (Qwen3VL vision encoder) no longer scales them by heads x head_dim; this was the L0 failure in test_qwen3_vision_cuda_graph_replay_matches_eager[*-FLASHINFER].
top-k on beam search. 0.7.2 added CUB's batched top-k to flashinfer's topk JIT module; a cold build takes ~10 min (584 s on B300). Built lazily on the first beam step it tripped the 300 s hang detector (BART/T5 beam-2 tests); built synchronously in warmup it pushed startup past the disaggregated launchers' 300 s ready timeout. Now warmup loads the module when it is already on disk and _beam_topk uses flashinfer's radix kernel; on a cold cache it builds on a daemon thread and that process stays on torch.topk (~50-100 us more per call at a 152k vocab), so the next start picks up the cached kernels.
The vendored kernel carries the attention gain; only the metadata kernel (~17-20 µs per prefill call) waits for a release with flashinfer-ai/flashinfer#5351's csrc.
Open
nvidia-cudnn-frontend moved >=1.27.0,<1.28 -> >=1.31.0,<1.32: 0.7.2 requires >=1.31.0 and pip resolution failed in L0 with the old cap. Its only user is the visual-gen cuDNN attention backend; test_attention_cudnn.py gives identical results on 1.27.0 and 1.31.0.
The supplied change summary reports an upgrade to flashinfer-python 0.7.2 and nvidia-cudnn-frontend>=1.31.0,<1.32. It also reports changes to PrimTS cache and sparse MLA behavior, QToken-KvBlock-Sparse metadata compatibility, and cuDNN ragged no-KV offset handling.
The decoder warmup now prebuilds FlashInfer top-k when beam width exceeds one. The warmup is best-effort and logs a warning if prebuilding fails. The PR context reports higher prefill metadata latency with the 0.7.2 wheel than with the nightly wheel. It also reports three failed L0 pipelines but provides no failure details. The security scanning lockfile was not regenerated. Current review-finding counts are unavailable. The shell result contained no diff, so the reported implementation details could not be independently verified.
QA Engineer Review
No test changes.
Per-File QA Perspective
3rdparty/vendor_patches/flashinfer-prims-ts.patch: Verify cache-probe and precompile behavior, placeholder handling, sparse MLA cache eligibility and invalidation, and metadata compatibility with the installed FlashInfer kernels.
3rdparty/vendor_sources.lock.yaml: Verify that the updated digests match the vendored source and patch.
ATTRIBUTIONS-Python.md: Verify that the recorded FlashInfer version matches the dependency.
requirements.txt: Verify dependency resolution and compatibility with the FlashInfer and cuDNN frontend constraints.
security_scanning/pyproject.toml: Verify security-scanning dependency resolution with FlashInfer 0.7.2.
tensorrt_llm/_torch/attention/backends/flashinfer.py: Verify that the cuDNN ragged no-KV path uses token-unit offsets and that other backends remain unaffected.
tensorrt_llm/_torch/pyexecutor/engine/runners/decoder/warmup.py: Verify that scheduled warmup invokes top-k prebuilding only when max_beam_width > 1.
tensorrt_llm/_torch/pyexecutor/sampler/ops/flashinfer.py: Verify the guarded import, best-effort top-k prebuild, and lazy-build fallback when prebuilding fails.
farazkh80
changed the title
[None][chore] Update flashinfer-python from 0.6.18 to 0.7.2.dev20261022
[None][chore] Update flashinfer-python to 0.7.2 and move prims-ts vendor to aa709061
Oct 27, 2026
The vendored prims_ts tree (aa709061, promoted in NVIDIA#21006) calls the
QToken-KvBlock-Sparse metadata JIT module, which 0.6.18 does not ship;
0.7.2 is the first release that does. Its csrc predates FlashInfer NVIDIA#5351,
so the vendor patch gains three compatibility hunks: a 13-argument
metadata launch, head-aware locators disabled, and page-byte membership
masks for every profile. Drop them once the pin moves past NVIDIA#5351.
Signed-off-by: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com>
farazkh80
changed the title
[None][chore] Update flashinfer-python to 0.7.2 and move prims-ts vendor to aa709061
[None][chore] Update flashinfer-python from 0.6.18 to 0.7.2
Oct 27, 2026
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The changes update FlashInfer and cuDNN dependency requirements, adjust cuDNN indptr handling and PrimTS metadata settings, and add scheduled top-k module warmup. Vendor source digests and the FlashInfer attribution version also change.
The flashinfer-python requirements and attribution change from version 0.6.18 to 0.7.2. The nvidia-cudnn-frontend requirement changes from >=1.27.0,<1.28 to >=1.31.0,<1.32.
Adjust attention backend and PrimTS metadata tensorrt_llm/_torch/attention/backends/flashinfer.py, 3rdparty/vendor_patches/flashinfer-prims-ts.patch, 3rdparty/vendor_sources.lock.yaml
The ragged no-KV path passes token-offset indptrs to cuDNN without multiplying them by head counts and head dimension. PrimTS metadata calls omit locator_head_multiplier; query-major memberships and head-aware locators are disabled. The vendor patch index hash and source digests change.
When max_beam_width > 1, scheduled decoder warmup calls warmup_topk_module(). The function returns when FlashInfer is unavailable and logs a warning if module prebuilding fails.
sequenceDiagram
participant DecoderWarmup
participant FlashInferWarmup
participant FlashInferTopK
DecoderWarmup->>FlashInferWarmup: call warmup_topk_module when max_beam_width > 1
FlashInferWarmup->>FlashInferTopK: build top-k module when FlashInfer is available
Loading
Suggested reviewers: juney-nvidia, lori-ren
Merge Risk: Low · previous review c0a4c
The dependency update is resolvable, but repair the warmup test and add focused top-k fallback coverage before merging.
Pre-merge checks: 5
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Inline comments:
Review comments at @requirements.txt:
- Line 71: Resolve the dependency conflict between flashinfer-python==0.7.2 and
the nvidia-cudnn-frontend constraint by updating the compatible TRT-LLM
constraint and lock data, or pinning a FlashInfer release compatible with the
existing constraint. Ensure the resulting dependency set can be resolved by pip.
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Reviewing files that changed from the base of the PR and between df95eda and 45b63cf.
Files ignored due to path filters: 1
tensorrt_llm/_torch/attention/backends/prims_ts/q_token_kv_block_sparse_metadata.py is excluded by !tensorrt_llm/_torch/attention/backends/prims_ts/**
Files selected for processing: 5
3rdparty/vendor_patches/flashinfer-prims-ts.patch
3rdparty/vendor_sources.lock.yaml
ATTRIBUTIONS-Python.md
requirements.txt
security_scanning/pyproject.toml
Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review.
The bot keeps the first eligible attention reviewer who started reviewing. Current target-branch CODEOWNERS includes attention-owned changes in this PR.
@yuxianq, this PR is already assigned to you as trt-llm-torch-attention-devs based on your earlier review. Could you help with the renewed review request? Thanks~
5 points — The visible behavioral changes fix compatibility with the released metadata module. Review requires tracing launch signatures, workspace encoding, and attention-consumer agreement across packed/unpacked and grouped paths. Mirrored vendor hunks and digest updates add little independent effort.
Importance
Important — Changing metadata launch arguments and disabling optimized membership and locator formats affects the sparse metadata producer/consumer contract, with correctness and FP8 performance implications.
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Affinity and workload are snapshots at selection, not live counters. Existing reservations on this PR are counted once.
…n 0.7.2
flashinfer-python 0.7.2 requires nvidia-cudnn-frontend>=1.31.0, which the
<1.28 cap from NVIDIA#18075 rejected, so pip resolution failed in CI. The cuDNN
visual-gen attention tests give the same results on 1.27.0 and 1.31.0.
Signed-off-by: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com>
…dule is built
The synchronous topk prewarm moved the ~10 minute cold build from the
first beam step into warmup, which pushed startup past the disaggregated
launchers' 300 s ready timeout in L0 (test_disaggregated_beam_search_v2).
Decide once per process whether flashinfer's topk module is already on
disk: if so load it in warmup and let _beam_topk use the radix kernel; if
not, build it on a daemon thread and keep this process on torch.topk, so
the next start picks up the cached kernels. torch.topk costs ~50-100 us
more per call at a 152k vocabulary (B300); the two kernels differ only in
the order of equal values, which torch.topk leaves unspecified anyway.
Signed-off-by: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com>
…en wrapper
NVIDIA#20911 moved the vendored prims_ts tree to aa709061, where FlashInfer
NVIDIA#5351 renamed the QToken wrapper's route width from seq_len_q to
group_size and accepts it for fixed decode layouts as well.
Signed-off-by: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com>
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Summary
Pin
flashinfer-python0.6.18 -> 0.7.2 inrequirements.txt,security_scanning/pyproject.tomlandATTRIBUTIONS-Python.md;nvidia-cudnn-frontendmoves to 1.31 to match (see Open).Why. Main's vendored
tensorrt_llm/_torch/attention/backends/prims_tstree (yuxianq's branch ataa709061, promoted in #21006) launches the QToken-KvBlock-Sparse attention through a metadata kernel that lives in the FlashInfer wheel (flashinfer.jit.gen_prims_ts_q_token_kv_block_sparse_metadata_module). 0.6.18 does not ship that module, so today the QSA sparse core in #20714 cannot run and falls back to Triton. 0.7.2 is the first release that ships it.Compat hunks. The vendored Python tree already contains FlashInfer PR flashinfer-ai/flashinfer#5351 (the optimized attention kernel), but the metadata kernel in the 0.7.2 wheel predates it. The vendor patch therefore adds three hunks to
q_token_kv_block_sparse_metadata.pyso the new Python tree drives the old metadata kernel: the 13-argument launch (nolocator_head_multiplier),allow_head_aware_locators=False, andquery_major_memberships=False. They go away when the pin moves to a release that ships flashinfer-ai/flashinfer#5351's csrc.cuDNN ragged prefill. flashinfer-ai/flashinfer#5133 (in 0.7.2) made the cudnn prefill backend take token-unit
qo_indptr/kv_indptrlike every other backend and reject element-unit offsets. The no-KV-cache ragged path inbackends/flashinfer.py(Qwen3VL vision encoder) no longer scales them by heads x head_dim; this was the L0 failure intest_qwen3_vision_cuda_graph_replay_matches_eager[*-FLASHINFER].top-k on beam search. 0.7.2 added CUB's batched top-k to flashinfer's
topkJIT module; a cold build takes ~10 min (584 s on B300). Built lazily on the first beam step it tripped the 300 s hang detector (BART/T5 beam-2 tests); built synchronously in warmup it pushed startup past the disaggregated launchers' 300 s ready timeout. Nowwarmuploads the module when it is already on disk and_beam_topkuses flashinfer's radix kernel; on a cold cache it builds on a daemon thread and that process stays on torch.topk (~50-100 us more per call at a 152k vocab), so the next start picks up the cached kernels.Cost of the 0.7.2 metadata csrc
Per-kernel GPU µs, TP2 32K,
prims_ts_sparse_gqafrom #20714. "main" = nightly wheel with flashinfer-ai/flashinfer#5351's csrc.The vendored kernel carries the attention gain; only the metadata kernel (~17-20 µs per prefill call) waits for a release with flashinfer-ai/flashinfer#5351's csrc.
Open
nvidia-cudnn-frontendmoved>=1.27.0,<1.28->>=1.31.0,<1.32: 0.7.2 requires>=1.31.0and pip resolution failed in L0 with the old cap. Its only user is the visual-gen cuDNN attention backend;test_attention_cudnn.pygives identical results on 1.27.0 and 1.31.0.security_scanning/poetry.locknot regenerated.Test plan (0.7.2 + this branch's vendored tree)
test_flashinfer_attention.py23 (cuDNN no-KV ragged plan + forward) and Qwen3VL vision unit tests with the token-unitqo_indptrfix🤖 Generated with Claude Code
Dev Engineer Review
The supplied change summary reports an upgrade to
flashinfer-python0.7.2 andnvidia-cudnn-frontend>=1.31.0,<1.32. It also reports changes to PrimTS cache and sparse MLA behavior, QToken-KvBlock-Sparse metadata compatibility, and cuDNN ragged no-KV offset handling.The decoder warmup now prebuilds FlashInfer top-k when beam width exceeds one. The warmup is best-effort and logs a warning if prebuilding fails. The PR context reports higher prefill metadata latency with the 0.7.2 wheel than with the nightly wheel. It also reports three failed L0 pipelines but provides no failure details. The security scanning lockfile was not regenerated. Current review-finding counts are unavailable. The shell result contained no diff, so the reported implementation details could not be independently verified.
QA Engineer Review
No test changes.
Per-File QA Perspective
3rdparty/vendor_patches/flashinfer-prims-ts.patch: Verify cache-probe and precompile behavior, placeholder handling, sparse MLA cache eligibility and invalidation, and metadata compatibility with the installed FlashInfer kernels.3rdparty/vendor_sources.lock.yaml: Verify that the updated digests match the vendored source and patch.ATTRIBUTIONS-Python.md: Verify that the recorded FlashInfer version matches the dependency.requirements.txt: Verify dependency resolution and compatibility with the FlashInfer and cuDNN frontend constraints.security_scanning/pyproject.toml: Verify security-scanning dependency resolution with FlashInfer 0.7.2.tensorrt_llm/_torch/attention/backends/flashinfer.py: Verify that the cuDNN ragged no-KV path uses token-unit offsets and that other backends remain unaffected.tensorrt_llm/_torch/pyexecutor/engine/runners/decoder/warmup.py: Verify that scheduled warmup invokes top-k prebuilding only whenmax_beam_width > 1.tensorrt_llm/_torch/pyexecutor/sampler/ops/flashinfer.py: Verify the guarded import, best-effort top-k prebuild, and lazy-build fallback when prebuilding fails.