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Integrate explicit prefill backend selection, fused additive KDA normalization, versioned offsets and model-owned state reuse. Preserve borrowed state and existing decode selection. Include current-stack validation and pending full-serving measurements; remove the obsolete experimental benchmark. Runtime and regression tests match measured commit 2bc6cd9. AI assistance was used. Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Publish the audited six-launch comparison with TTFT, TPOT, output throughput, every repeat and all accuracy differences. Keep failed and pending model comparisons explicit; no production code changes. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Retain the verified CUDA error, failed phase, exact runtime checks and incomplete comparison. Existing completed measurements are unchanged. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Preserve the second baseline score failure and leave full GLM metrics unavailable. Retain completed comparisons and all accepted partial receipts. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Add validated GB200 full-serving comparisons and the separate Kimi GB300 prefill comparison. Retain every repeat, accuracy difference and earlier failure; production sources are unchanged. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Co-authored-by: Codex <codex@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Remove the obsolete FlashInfer epsilon argument and cover normalization across KDA providers. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
Keep the prefill integration, tests, and user documentation. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
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Documentation preview: https://vllm--61250.org.readthedocs.build/en/61250/ |
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Match the public FlashInfer dispatcher choices and adapt state dtype and packed metadata handling for explicit native backends. Document shape, dependency and graph-capture limits. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Anerudhan Gopal <agopal@nvidia.com>
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<!-- .github/pull_request_template.md --> ## 📌 Description KDA prefill can raise `RuntimeError: Inference tensors do not track version counter` when `cu_seqlens` is created under `torch.inference_mode()`. Both the packed-offset metadata cache and affine state-index carrier read `Tensor._version` without checking for inference tensors. The affected helpers are identical in FlashInfer 0.7.1 and main at `c0e62fa7`. This change skips version reads and eager cache reuse for inference tensors. Normal tensors retain version-based cache invalidation. CUDA graph capture still reuses warmed, fixed offset metadata and records state-index copies for replay. Add regression tests for mutation, cold capture rejection, and graph replay. ## 🔍 Related Issues - Reported in [vLLM CI build 93957](https://buildkite.com/vllm/ci/builds/93957#01a123d4-00df-4a2d-8d81-dda11be7d084), from vllm-project/vllm#60948. - vllm-project/vllm#61250 already copies inference offsets into contiguous, versioned int64 tensors. Its guard avoids this crash; this fix needs no change to that PR. ## 🚀 Pull Request Checklist Thank you for contributing to FlashInfer! Before we review your pull request, please make sure the following items are complete. ### ✅ Pre-commit Checks - [x] I have installed `pre-commit` by running `pip install pre-commit` (or used your preferred method). - [ ] I have installed the hooks with `pre-commit install`. - [ ] I have run the hooks manually with `pre-commit run --all-files` and fixed any reported issues. > If you are unsure about how to set up `pre-commit`, see [the pre-commit documentation](https://pre-commit.com/). Changed-file hooks passed, including mypy and Ruff: ```sh pre-commit run --files flashinfer/kda_prefill.py tests/kda/test_kda_prefill_inference_metadata.py ``` ## 🧪 Tests - [x] Tests have been added or updated as needed. - [x] All tests are passing (`unittest`, etc.). Unmodified FlashInfer 0.7.1 reproduced all three reported failures on GB300 (Torch 2.13.0+cu132, CUDA 13.2, CuTe DSL 4.7.1). The isolated harness uses the unchanged CI adapter and three test bodies from vLLM `da3cf3ad`, with real CUDA kernels and no backend override. Post-fix GB300 validation passed with only this patch applied to the same 0.7.1 release wheel: | Test scope | Before | After | | --- | --- | --- | | Three unchanged vLLM CI test bodies: near-collinear keys, BF16 correctness, cross-stream breakable CUDA graph | 3 failed with the version-counter error | 3 passed (33.82 s) | | Added metadata mutation and CUDA graph tests | — | 8 passed (4.83 s) | There were no post-fix test failures, collection errors or skips. The metadata cases cover both regular and inference tensors, including mutation, invalid offsets, cold capture rejection and state-index updates on graph replay. The executed helper bodies match this PR's commit `46a44472`. CuTe DSL deprecation warnings remain. These are scoped tests; the full FlashInfer/vLLM suites and serving benchmarks were not run. ## 🔬 Experimental Track <!-- Only for PRs submitted under the experimental policy (CONTRIBUTING.md → "Experimental APIs and Backends"). Leave this section untouched for normal PRs. --> - [ ] This PR is **experimental**: it adds or changes code under `flashinfer/experimental/` and/or an `@flashinfer_experimental_api`. Tracking issue: # - [ ] The tracking issue names an owner, the reason for the experimental path, and a graduation plan with a target release. - [ ] Core changes are limited to a thin entry point (signature, shared validation, feature-gate check, backend selection, handoff). - [ ] Tests live in `tests/experimental/` and were validated on the intended hardware; a runnable example is included. - [ ] Nothing is registered in `flashinfer/aot.py`, and no experimental backend is reachable from `backend="auto"` without `FLASHINFER_ALLOW_EXPERIMENTAL_AUTO_BACKENDS=1`. (Calling an `@flashinfer_experimental_api` or naming a backend explicitly is itself the opt-in and needs no environment variable.) - [ ] **Test scope declared below.** The experimental CI lane runs exactly these targets, so keep them as narrow as the change allows. <!-- Required for experimental PRs. Replace the commented lines below with your targets. Do not delete the fence or change its `experimental-tests` tag — the experimental-track watcher reads it verbatim to decide which targets to ask CI for. --> ```experimental-tests # One target per line: a directory or a file. (A pytest ::selector is not # supported -- the sharding runner cannot consume one.) Must be under # tests/experimental/ and must exist. Delete these comment lines and add yours, e.g. # # tests/experimental/test_my_backend.py # tests/experimental/my_backend/ # # Declaring the whole tree (tests/experimental/) is allowed but means every # experimental PR pays for every other feature's tests, in every matrix cell. ``` ## Reviewer Notes Inference offsets have no mutation counter, so eager calls must reread them instead of accepting an identity-only cache hit. This may add host synchronization for these previously failing calls. Capture requires offsets to retain their warmed values through replay; mutable state indices still refresh through the captured copy. No public API or backend selection changes. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Corrected metadata handling for inference-mode tensors. Offset changes are now picked up on eager calls, while capture and replay use warmed metadata. * Improved handling of generated state indices when tensor version information is unavailable. * **Documentation** * Clarified that packed offsets must retain their warmed values during capture and replay, and that eager calls reread inference-mode offsets. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
Contributor
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This is a huge PR and I'm not sure why vLLM should be aware of FlashInfer backend. Should this be folded to FlashInfer auto backend selection? Or perhaps FlashInfer can expose an env var to override the backend. |
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This pull request has merge conflicts that must be resolved before it can be |
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Overview
Add FlashInfer prefill selection for Qwen GDN (
auto/cudnn) and all eight public KDA backends for bounded GLM/Kimi KDA, with shared state handling and versioned KDA metadata. This carries the integration from Anerudhan/vllm#1; benchmark result files are excluded.Claims
cute-dsl,cute-dsl-persistent,tirx,ptx,cake, andsmall-bhfor KDA.New arguments
--flashinfer-gdn-backendauto,cudnn--gdn-prefill-backend flashinfer--flashinfer-kda-backendauto,cute-dsl,cute-dsl-persistent,tirx,ptx,cake,small-bh,cudnn--kda-prefill-backend flashinferBoth resolve to
autowhen omitted.autodelegates to FlashInfer's dispatcher; explicit choices retain FlashInfer's input checks. The existing provider argument must select FlashInfer to use these paths.KDA state requirements:
auto,cake, andsmall-bhuse BF16;cute-dslandcudnnaccept BF16 or FP32;cute-dsl-persistent,tirx, andptxrequire FP32. Set the existing--mamba-ssm-cache-dtypeargument to match. Extra shape, GPU and dependency limits still apply. In particular, PTX requires SM103a and H64/H96. Persistent CuTe DSL, TIRx and PTX require an explicit warmed workspace for prefill graph capture; this adapter does not manage it, so use eager prefill for those choices.These examples show backend options; add the model's normal deployment flags.
Validation
Current branch: changed-file pre-commit, including Ruff, mypy and test wiring, passed.
git diff --checkpassed. An isolated CPU harness ran 164 checks for backend forwarding, state dtypes, inference/strided offsets, sequence-order metadata, CLI choices and existing GDN selection contracts. The accepted KDA values match FlashInfer main atc0e62fa7..venv/bin/python -m pytest --noconftest \ ../artifacts/vllm-kda-backends-20261012/test_kda_backends_cpu.py -q # 164 passed; isolated source-level harness outside the PR.AWS runtime passed 74 KDA GPU tests and 44 metadata tests for the original auto/cuDNN paths. Results below retain their measured revisions and are not measurements of this backend-list update.
Latest microbenchmark results — GB200
Lyris job 3275022. B1 × 8,192-token prefill; cells show active CPU / GPU kernel interval union, µs/call (lower is better), averaged over four fresh-process medians.
For Kimi and GLM, vLLM auto uses FlashKDA. Both Qwen auto columns use the same native FlashInfer provider.
Kimi FlashInfer auto selects cuDNN for this shape. Against the previous FlashInfer baseline, GPU time falls 199.566 → 115.296 µs (42.23%); CPU time rises 173.512 → 213.388 µs (22.98%). GLM auto stays native. These runs establish no GDN auto GPU gain.
Measured FlashInfer baseline
461037fb→ candidate669a153d(#6259); unchanged vLLM caller29ad8bda. Frontend 1.31.0, cuDNN 9.20, Torch 2.13.0+cu132; BF16 KDA state and FP32 GDN state. 555 scoped tests passed; 2,880 GPU traces passed audit.CPU and GPU were measured separately and must not be added. These are synthetic operator timings, not serving TTFT; small CPU shifts also occur in unchanged controls. Full B1/B8/B32 prefill/decode results, repeats and raw evidence (NVIDIA access required).
Kimi K3 inference — AWS B300, C1 and C8
8× B300; one 3,600-second sending phase per point. AWS FlashKDA and FlashInfer auto use the same runtime and node, with BF16 recurrent state, TP8/DCP8, FP4 weights, FP8 KV, DSpark K7 synthetic acceptance 3.84, prefix cache and Mooncake enabled.
FlashInfer auto change versus the AWS FlashKDA BF16 control:
Total throughput includes cached input plus output. Median user rate is 1 / median TPOT. Rates use the exact raw request window, including completed requests during drain; warmups are excluded. All four AWS points passed validation with zero request errors or cancellations. Eight Mooncake lookup errors occurred at startup per point; none occurred during warmup or profiling. JIT, event-loop and cleanup warnings remain in the evidence.
AWS measured vLLM
156420052, FlashInfera7103ae2(0.7.2), frontend 1.31.0, cuDNN 9.20.0.48, Torch 2.13.0+cu130. These measurements predate the current rebase and use a different FlashInfer revision from the microbenchmark; they do not establish that AWS auto selected cuDNN.Comparison limits: FlashKDA supports one intermediate prefill checkpoint block; FlashInfer auto supports zero. Cache settings stayed enabled, but this capability differs. Published InferenceX values are external reference points with runtime, state-dtype and platform differences. Each AWS point is one run; these results do not isolate a kernel-only gain or establish statistical significance.
Earlier model evaluation and limits
The earlier GB200 serving study used vLLM
7c3c1a3f, FlashInfer73b63f88, and frontend7f85a1f7. In baseline/auto/cuDNN/cuDNN/auto/baseline order, full GSM8K correct counts were Kimi-K3 1259/1254/1251/1247/1246/1248 of 1319 and Qwen3-Next 1142/1149/1136/1141/1149/1142 of 1319. Invalid-answer counts were 1/1/1/1/2/1 and 1/0/0/0/1/0, respectively. Model accuracy equivalence is not established.The GLM auto C128 run failed with an RPC timeout and a CUDA error during cleanup. Qwen3.5 cuDNN missed its GSM64 gate (55/64 versus 56 required); those comparisons are incomplete. Full scores, repeats and failures remain on the source branch. The AWS workload uses synthetic speculative acceptance and does not measure model accuracy.
Details
The shared adapter calls FlashInfer's public dispatchers. The cuDNN KDA path uses fused additive Q/K normalization and supports bounded gates with
-5 <= lower_bound < 0. Seedocs/configuration/optimization.mdfor usage and limits.Duplicate-work checks found no matching prefill-selection PR. Related #60470 changes KDA decode; #60050 adds GDN checkpoints; #60105 optimizes native GDN gate conversion and offsets. This PR adds explicit prefill implementation selection and shared KDA integration. It does not include those separate optimizations. AI assistance was used.
Pull Request Checklist
I used vLLM's
/pr-checklistskill. (Mandatory for agents, optional for humans).AI assistance was used during the creation of this PR.
Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.
Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.
Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.
Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.