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[Benchmark] Add per-case kernel profiles to vllm bench forward (#61692)

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Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: vllm-agent <claw@inferact.ai>
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Woosuk Kwon
2026-10-17 08:27:11 -07:00
committed by GitHub
co-authored by Claude Opus 5.5 vllm-agent
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commit c0b6f74bee
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@@ -136,7 +136,7 @@ vllm bench {forward, latency, serve, throughput}
### forward
Benchmark the model forward of exact batches: each case lists its requests as `[N*]Q[@K][c][:D]` (query length Q, KV length K including the query, D draft tokens). A prefill's prefix is a prefix-cache hit unless `c` marks a chunked prefill; they differ only on models with SWA bounded replay, which recompute the last window of a hit. Each request's prefix length varies by ±50% by default (`--kv-len-jitter`), identically on every DP rank. Each forward is captured as a one-shot CUDA graph; sampling and CPU overhead aren't measured. Use `torchrun`, one process per GPU, for DP/TP/EP. CUDA only for now.
Benchmark the model forward of exact batches: each case lists its requests as `[N*]Q[@K][c][:D]` (query length Q, KV length K including the query, D draft tokens). A prefill's prefix is a prefix-cache hit unless `c` marks a chunked prefill; they differ only on models with SWA bounded replay, which recompute the last window of a hit. Each request's prefix length varies by ±50% by default (`--kv-len-jitter`), identically on every DP rank. Each forward is captured as a one-shot CUDA graph; sampling and CPU overhead aren't measured. Use `torchrun`, one process per GPU, for DP/TP/EP. CUDA only for now. Add `--profile-dir DIR` to write a Perfetto trace and a per-kernel GPU time summary of each case; `python -m vllm.benchmarks.forward.profile A.kernels.json [B.kernels.json]` prints a summary or compares two. Under Nsight Systems, `nsys profile --capture-range=cudaProfilerApi --capture-range-end=repeat --cuda-graph-trace=node` records just those replays, one report per case.
```bash
vllm bench forward \