[Perf] Refine FlashInfer SM120 sparse-MLA cpb per capture bucket in warmup - #55264
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…armup After the mixed-batch warmup triggers FlashInfer's constants/crossover calibration, run one uniform-decode dummy run per CUDA-graph capture size <= 64 inside the same tuning context (leader) so flashinfer's refine_cpb measures the model's cpb pick against the exact warmed shape and persists the override; non-leaders replay the same buckets to keep the collectives aligned. Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> Co-authored-by: Kimi Code <noreply@moonshot.cn>
Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> Co-authored-by: Kimi Code <noreply@moonshot.cn>
The FlashInfer calibration and per-bucket cpb refinement measure the local device and persist under its own device key, so a non-leader that stays outside the tuning context keeps heuristic picks (and may never calibrate at all, depending on whether the general autotune pass happens to produce a decode-routed shape). Run the mixed warmup and the bucket loop inside the tuning context on every rank; set the autotune process group so any tunable op hit during these runs stays consistent across ranks, matching the general autotune pass. Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> Co-authored-by: Kimi Code <noreply@moonshot.cn>
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WalkthroughThe sparse-MLA warmup path derives valid CUDA-graph capture buckets, tunes on all ranks within a shared FlashInfer autotune context, and runs uniform-decode refinement for each bucket. Tests cover filtering, sorting, deduplication, and empty inputs. ChangesSparse MLA autotune refinement
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟡 Moderate · up to Sparse-MLA warmup now refines decode buckets across ranks, but multi-node startup may hang in the new process-group path and the added CPU bucket-selection test currently fails because its worker fixture lacks model_runner. Resolve these before merge. Sequence Diagram(s)sequenceDiagram
participant DecodeAutotune as _run_flashinfer_sparse_mla_decode_autotune
participant ProcessGroup as set_autotune_process_group
participant AutotuneContext as flashinfer_autotune
participant DummyRun as _dummy_run
DecodeAutotune->>ProcessGroup: Set world CPU process group
DecodeAutotune->>AutotuneContext: Enter shared autotune context on all ranks
AutotuneContext->>DummyRun: Run mixed warmup
DecodeAutotune->>DummyRun: Run uniform decode for each refine bucket
DecodeAutotune->>ProcessGroup: Restore process group to None
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Inline comments:
In `@vllm/model_executor/warmup/flashinfer_sparse_mla_warmup.py`:
- Line 166: Update the synchronized autotune setup around
set_autotune_process_group to avoid passing world.cpu_group when the deployment
uses unsupported host-staged NCCL without GPUDirect RDMA; provide a
configuration-controlled opt-out or per-rank fallback while preserving
synchronized profiling for supported transports.
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A uniform-decode refine run at bucket s schedules s requests, so a capture bucket past max_num_reqs (e.g. max_num_seqs=32) fails in metadata build. Clamp the refine set by the runner's max_num_reqs. Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> Co-authored-by: Kimi Code <noreply@moonshot.cn>
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Inline comments:
In `@vllm/model_executor/warmup/flashinfer_sparse_mla_warmup.py`:
- Line 60: Update the worker fixture in test_flashinfer_sparse_mla_warmup.py to
define model_runner.max_num_reqs with a positive value such as 64, while
preserving its existing vllm_config setup, so the warmup code can access the
attribute and return the expected buckets.
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vllm/model_executor/warmup/flashinfer_sparse_mla_warmup.py
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| ) | ||
| # A uniform-decode dummy run at bucket s schedules s requests; buckets past | ||
| # max_num_reqs cannot be formed. | ||
| max_reqs = getattr(worker.model_runner, "max_num_reqs", 0) or 0 |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Add model_runner.max_num_reqs to the test fixture.
worker.model_runner is accessed before getattr can apply its default. The fixture in tests/model_executor/test_flashinfer_sparse_mla_warmup.py defines only vllm_config, so this test raises AttributeError instead of returning the expected buckets. Add an explicit positive limit, such as max_num_reqs=64, to the fixture.
Proposed test fix
worker = SimpleNamespace(
vllm_config=SimpleNamespace(
compilation_config=SimpleNamespace(cudagraph_capture_sizes=capture_sizes)
- )
+ ),
+ model_runner=SimpleNamespace(max_num_reqs=64),
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In `@vllm/model_executor/warmup/flashinfer_sparse_mla_warmup.py` at line 60,
Update the worker fixture in test_flashinfer_sparse_mla_warmup.py to define
model_runner.max_num_reqs with a positive value such as 64, while preserving its
existing vllm_config setup, so the warmup code can access the attribute and
return the expected buckets.
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review. Visit https://docs.coderabbit.ai/cli.
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Purpose
FlashInfer's SM120 sparse-MLA decode path (flashinfer-ai/flashinfer#4802) picks
chunks_per_blockfrom a calibrated analytical model and refines the pick for the exact shape being warmed whenever a tuning-mode call reaches it (refine_cpb, persisted in the FlashInfer autotune cache). vLLM's sparse-MLA warmup previously exercised only the mixed batch (a T=1 decode plus one short prefill chunk), so the CUDA-graph capture buckets that production actually replays were never measured. Offline sweeps (DRAM-cold protocol) show the unrefined model pick costs up to 1.07x kernel time on dsv4 T=24 and 1.35x on dots3_swa T=32 — both real capture sizes.This PR runs one uniform-decode dummy run per capture bucket <= 64 inside the tuning context on every rank: the FlashInfer calibration and refinement measure the local device and persist under its own device key, so a rank that skips tuning keeps heuristic picks (and, depending on whether the general autotune pass happens to produce a decode-routed shape, may never calibrate at all). The autotune process group is set so any tunable op hit during these runs stays consistent across ranks, matching the general autotune pass. Wall clock is unchanged — the per-device measurements run in parallel on each GPU.
Dependency
No new FlashInfer API is called. With a FlashInfer build predating #4802 the extra runs are plain warmup; measured picks require a FlashInfer release containing #4802.
Relationship to #53605
Same root theme — the sparse-MLA warmup under-covers shapes that production reaches — on different axes and against different FlashInfer mechanisms:
extra_topkwidth axis (C128A 256/384) for the per-shape AutoTuner tactic cache (SparseMlaDecodeV3Runner tactic=-1fallbacks). That mechanism exists in FlashInfer <= 0.6.18. Refactor Sparse MLA SM120 flashinfer-ai/flashinfer#4802 removes it on the SM120 path: topk/extra_topk are runtime kernel arguments and cpb comes from the calibrated model, so width-keyed tactic misses no longer exist there. This PR covers the capture-bucket T axis for the new refinement mechanism; on FlashInfer <= 0.6.18 the bucket runs additionally pre-warm the legacy per-shape tactic entries at each bucket.skip_opsinto this warmup's tuning contexts — orthogonal and composes: asparse_mla_sm120skip also gates this refinement loop, per the FlashInfer-side autotuner contract.Test evidence
pytest tests/model_executor/test_flashinfer_sparse_mla_warmup.py(bucket selection: cap at 64, dedup/sort, empty/None).FLASHINFER_MLA_SPARSE_DSV4+ b12x, FlashInfer with [Performance]: Deepseek-v2 support #4802: 8K/1K serving sweep C1-C64 within +/-2% of the pre-change baseline (256/256 completed, 0 failed); GSM8K strict-match 0.9560. On this model the refined picks coincide with the model's, so e2e is unchanged by design; the gain lands on shapes with real pick error (above).Why not tune on the leader and share: the measurements are per-GPU-parallel anyway, so sharing saves no wall clock; the tuning cache is device-keyed by design (same-name GPUs can still differ), and a share path would add a silent-fallback failure mode. Per-rank tuning reuses the trigger path both ranks already exercise today.
This change was developed with AI assistance; I reviewed every changed line and ran the tests above.