Workload-conditioned operator profiling on patched vLLM 0.24.0 + Qwen3-30B-A3B/H20. H1b PASS (irregular patterns carry +23-45pp R64 raggedness, 8-45% token-efficiency loss vs rectangular controls); mechanism decomposition kills the padding narrative and finds the arrival-uniformization artifact (-12.9%); cross-version churn surface shows TP2/MNS64 -29.4% across vLLM 0.20->0.24 while the argmax held. Raw Layer-1 JSONL streams (507 MB) stay on disk, git-ignored; footer sidecars and metrics are tracked. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
5.4 KiB
vLLM 0.24.0 OpProf patch series
Goal
Apply the accepted OpProf Layer-1 instrumentation to exactly vLLM v0.24.0
at base commit ee0da84ab9e04ac7610e28580af62c365e898389. The series adds one
scheduler-owned composition record per step without installing new runtime
dependencies or changing GPU kernels.
Contents
0001-Add-lightweight-per-step-OpProf-telemetry.patch: adds the environment switch, import-light JSONL recorder/writer, scheduler hooks, and reuse of the existing CUDA-graph stat. Writer failures are exposed without blocking producers or shutdown, and request histograms are accumulated in-place.0002-Add-standalone-OpProf-telemetry-tests.patch: adds CPU-only tests that load the recorder directly without importing or installing vLLM or torch, including ENOSPC, golden-record, and zero-token regressions.0003-Log-the-OpProf-output-path-at-startup.patch: logs the resolved JSONL output path and covers it in the standalone shutdown test.0004-Exclude-OpProf-output-path-from-compile-cache-key.patch: prevents the per-run telemetry destination from invalidating vLLM's torch.compile/AOT cache and adds an import-light regression test.0005-Keep-compile-factor-regression-import-light.patch: isolates the new regression from torch in full vLLM test environments.0006-Checkpoint-OpProf-accounting-across-hard-kills.patch: atomically checkpoints balanced writer counters beside each JSONL stream once per flush interval, with clean-close and hard-kill regressions.0007-Recreate-scheduled-torch-profiler-between-windows.patch: discards a stopped scheduled torch-profiler wrapper so each subsequent official profile endpoint call receives a fresh 2+8 schedule and emits its own trace.apply.sh: verifies the exact base, refuses dirty/wrong revisions, applies all numbered patches withgit am, and exits successfully only when the exact series is already applied directly on the required base.pytest-evidence.txt: exact isolated test command, dependency versions, and all-pass summary.
The source branch tip used to generate the patches is
23450fb21ac255b0cf710f4ee965ee694921975d (opprof).
Apply
Prerequisite: a clean checkout whose HEAD is the exact base commit.
./patches/vllm-0.24.0-opprof/apply.sh /path/to/vllm-v0.24.0
Running the command again is a no-op only when the five matching patch commits
are rooted directly at the required base. A partially applied series, dirty
tree, unrelated commit, or any other HEAD is rejected instead of being
guessed around.
Enable and output
Set an absolute output directory before starting vLLM:
export VLLM_OPPROF_DIR=/absolute/path/to/run/opprof
Unset or empty disables the feature before recorder construction. Combining it
with --disable-log-stats fails fast, as approved.
Each EngineCore/DP scheduler writes one file named approximately
opprof-v1-dp0-pid1234-<start_ns>.jsonl. Records contain schema/engine/step and
timestamps; scheduled prefill/decode composition; first/middle/final/unsplit
prefill chunks; 12-bin context and 9-bin chunk-size histograms; preemptions;
running/waiting/deferred queues; KV blocks/usage; local/external prefix deltas;
CUDA-graph hit/mode/bucket/padding; explicit null Layer-1 MoE load; and drop-gap
accounting. A clean close writes a final writer-count footer in the stream.
Every JSONL flush also atomically replaces
<stream>.footer.json through a same-directory temporary file. The sidecar
contains the encoded, written, and dropped counts through that durable flush,
the last written step index, a wall-clock timestamp, the one-second flush
interval, and whether it is final. Queue entries carry their submission
ordinal and cumulative drops, so a periodic checkpoint always satisfies
encoded = written + dropped without decoding records in the writer thread.
On clean close the in-stream footer is authoritative and the final sidecar must
agree with all three counters. If a hard kill prevents the in-stream footer,
the latest sidecar is authoritative: the decoded data-line count must equal
its written_records, its final data-line step must equal
last_step_index, and its counters must balance. Data after that checkpoint
may be lost, bounded by at most the configured one-second flush interval.
The bounded queue holds 8192 encoded records. Producers never wait for disk; full queues or a failed writer drop the new record and report the gap on the next successful record. A writer I/O failure is exposed through recorder state, logged once, and cannot make shutdown wait indefinitely. The writer flushes at 1 MiB, one second, or shutdown.
Test
Only pytest and msgspec are required. --confcutdir prevents vLLM's global
test configuration from importing its full dependency stack.
cd /path/to/vllm-v0.24.0
uv run --no-project --with pytest --with msgspec \
pytest --confcutdir=tests/v1/core tests/v1/core/test_opprof.py -q
Expected summary:
18 passed in 1.09s
Caveats
- Layer 1 intentionally records no expert-load arrays. Exact routed experts remain a separate Layer-2 run.
PIECEWISEmeans graph-wrapped compiled regions, not full-step graph replay.- Phase 2 must measure the always-on overhead; acceptance requires the upper bound of the 95% confidence interval to remain below 3% for every primary serving metric.
- Primary campaign topology is TP1 on community BF16 Qwen3-30B-A3B, with TP2 and TP4 counterpoints. Record the selected MoE backend log every run.