[Bugfix][Spec Decode] Profile adaptive verification tail on a schedulable batch shape - #54065
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…able batch shape Adaptive verification seeds its cost tables from dummy runs at every captured size plus a doubling tail up to max_num_batched_tokens. The tail sizes were handed to _dummy_run as a bare token count, which splits them evenly over min(num_tokens, max_num_seqs) requests. With max_num_seqs=128 and num_speculative_tokens=7 the 16384-token tail was therefore timed as 128 requests of 128 queries each, while a fully occupied decode step is 128 * (1 + 7) = 1024 tokens. Every tail sample above 1024 measured a shape the scheduler cannot produce, and those samples are what the verify curve is fitted and extrapolated from. Shape the tail as the mixed step it has to be: the surplus over the decode ceiling can only be prefill, so it is cut into chunks sized by VLLM_ADAPTIVE_VERIFICATION_PROFILE_CONTEXT_LEN, the freed request slots stay decode requests of exactly 1 + num_speculative_tokens queries, and the split is passed down to InputBatch.make_dummy. Captured sizes keep the even split, since those runs must match the descriptor their graph was captured with, and make_dummy only honours an explicit split when cudagraph dispatch did not pad or reshape the batch. Signed-off-by: kakiuwang-ui <kakiuwang@gmail.com>
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cc @yewentao256 — this touches the adaptive-verification profiler under TL;DR: MERGEABLE, with tests in |
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Thanks for the work! For spec decoding, I think @benchislett would be a better person to review this PR
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✅ @kakiuwang-ui, CI is now available for this PR.
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This pull request has merge conflicts that must be resolved before it can be |
…cation-profile-shape-54046 Signed-off-by: kakiuwang-ui <kakiuwang@gmail.com> # Conflicts: # vllm/v1/worker/gpu/spec_decode/adaptive_verification.py
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Thanks @yewentao256 — cc @benchislett for the spec-decode side, and thanks for the Rebased onto current Short version of what this fixes, for whoever picks up the review:
The fix shapes the tail as the mixed step it has to be — surplus over the decode ceiling becomes prefill chunks sized by Re-validated on the merge commit (RTX A6000): 72 passed across |
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/ci run |
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✅ Triggered Buildkite CI #89211 for commit |
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This pull request has merge conflicts that must be resolved before it can be |
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Purpose
Fixes #54046.
AdaptiveVerificationManager.batches_to_profileseeds the cost tables from dummy runs at every captured size plus a doubling tail up tomax_num_batched_tokens. The tail sizes are handed to_dummy_runas a bare token count, which splits them evenly overmin(num_tokens, max_num_seqs)requests, andInputBatch.make_dummysplits evenly again.With
max_num_seqs=128andnum_speculative_tokens=7that times the 16384-token tail as 128 requests of 128 queries each, all carryingVLLM_ADAPTIVE_VERIFICATION_PROFILE_CONTEXT_LENof context. A fully occupied decode step is128 * (1 + 7) = 1024tokens, so every tail sample above 1024 measures a shape the scheduler cannot produce -- and those samples are exactly whatset_initial_cost_curvesfits the verify curve to and extrapolates beyond.The fix shapes the tail as the mixed step it has to be:
VLLM_ADAPTIVE_VERIFICATION_PROFILE_CONTEXT_LEN(already the profiler's stand-in for a sequence length).1 + num_speculative_tokensqueries, and their freed tokens go back to the chunks._dummy_run->execute_model->InputBatch.make_dummy.For the example in the issue (
max_num_batched_tokens=16384, ctx=8192, max_num_seqs=128, k=7) the profiled 16384-token step becomes 126 decode requests of 8 queries plus 2 prefill chunks of 7688, instead of 128 x 128.Scope is deliberately narrow:
make_dummyonly honours an explicit split when cudagraph dispatch did not pad or reshape the batch; otherwise the shape belongs to the descriptor and the even split is used, exactly as today._dummy_run's even split andmake_dummy's are identical, so passing the caller's split through is a no-op for every existing dummy-run caller.get_uniform_decode_token_countalready returnsNonefor a non-uniform batch, so a mixed profiling batch cannot be dispatched into a uniform decode graph.Why this is not duplicating an existing PR
gh pr list --repo vllm-project/vllm --state open --search "54046 in:body"-- no results.adaptive verification profileand forbatches_to_profile. The only nearby open PR is [Spec Decode] Cache adaptive verification profiles #52233, which caches the calibrated curves across boots; it does not change which shapes get profiled, so the two are orthogonal (they touch the same two files and may need a trivial rebase).Test Plan
Plus an on-GPU probe that drives a mixed decode/prefill dummy batch through the V2 runner (
facebook/opt-125m,max_num_seqs=8,enforce_eager), recording whatInputBatch.make_dummyactually built.Test Result
Hardware: NVIDIA RTX A6000, Ubuntu, CUDA 13.
Unit / regression:
55 passedacross the six files above, including the three new tests.The new tests are discriminating: reverting only the
batches_to_profilewiring (leaving the tests untouched) failstest_profiled_tail_batches_are_shapes_the_scheduler_can_producewithassert None is not None.On-GPU shape probe (identical
num_tokens, only the split differs):make_dummybuiltnum_reqs=8, num_tokens=256, split=[32, 32, 32, 32, 32, 32, 32, 32]num_reqs=8, num_tokens=256, split=[2, 2, 2, 2, 2, 2, 122, 122]The mixed batch forwards without error and ordinary generation is unaffected afterwards
(
llm.generate("The capital of France is")->' on par-and-par with Venezuela').Model evaluation: not applicable -- this changes only the batch shapes timed during startup calibration. It does not touch sampling, the verification kernels, or any token the model emits; the generation check above confirms the serving path is unchanged. The behavioural effect is on the seeded verify cost curve, and hence on the draft budgets adaptive verification chooses.
tests/v1/cudagraph/test_cudagraph_mode.pyhas 14 failures on this machine, but they reproduce identically on unmodifiedmain(verified by swapping the four changed files back and re-running), so they are environmental and unrelated.Lint:
ruff checkandruff format --check(v0.14.0, the pinned pre-commit revision) clean on all four files.mypyover the three changed source files reports 6 errors, all of which reproduce on unmodifiedmain(7 there); the one that disappears isinput_batch.py: Need type annotation for "num_scheduled_tokens", now that the parameter carries an explicit annotation. No new type errors.AI assistance was used in preparing this change. Every line was reviewed and the tests above were run and inspected by me.