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[LLM] add metric observations for vLLM and SGLang - #1068
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Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughThe change adds five serving metrics to the metric catalog and documents them for vLLM and SGLang. Both backends now produce metric observations from benchmark results and optional semantic accuracy through a shared helper. Tests cover observations, filtering, and updated report fixtures. ChangesLLM Serving Metrics
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Feature Merge Risk: 🔵 Low · up to Out-of-range evaluator output can appear as a valid structured accuracy ratio. The impact is bounded to reported metrics, but the existing concern remains unresolved. 🚥 Pre-merge checks | ✅ 4✅ Passed checks (4 passed)
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
podkidyshev
changed the title
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/build |
Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
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Actionable comments posted: 1
- 🪄 Fix CodeRabbit comments on this PR
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Review comments at @src/cloudai/workloads/common/llm_serving.py:
- Around line 252-253: Update the accuracy check before appending a
MetricObservation to accept only finite values within the documented 0–1 range;
omit out-of-range values from structured observations while leaving the existing
scalar accuracy result unchanged.
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doc/workloads/_llm_serving_metrics.incdoc/workloads/sglang.rstdoc/workloads/vllm.rstsrc/cloudai/metrics.pysrc/cloudai/workloads/common/llm_serving.pysrc/cloudai/workloads/sglang/sglang.pysrc/cloudai/workloads/vllm/report_generation_strategy.pysrc/cloudai/workloads/vllm/vllm.pytests/workloads/common/test_llm_serving.py
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/build |
Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
Signed-off-by: Ivan Podkidyshev <ipodkidyshev@nvidia.com>
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/build |
podkidyshev
marked this pull request as ready for review
October 6, 2026 15:40
podkidyshev
requested review from
jj10306,
rutayan-nv and
srivatsankrishnan
as code owners
October 6, 2026 15:40
| ) -> list[cloudai.metrics.MetricObservation]: | ||
| """Use statistics to distinguish latency results; scalar results have no dimensions.""" | ||
| observations: list[cloudai.metrics.MetricObservation] = [] | ||
| if accuracy is not None and math.isfinite(accuracy): |
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I saw the code rabbit comments. Is it ever possible that it can actually produce NaN/Inf here?
Example: For vLLM: what happens if you have mean([]) --> NaN?
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Summary
metric_observationsto vLLM and SGLang for request throughput (requests/s), output-token throughput (tokens/s), TTFT/TPOT (ms), and optional semantic accuracy.statistic(mean,median,p99); throughput and accuracy have empty dimensions.Test Plan
metric_observationsdirectly on historical vLLM/SGLang runs. The output values correspond to workload artifacts 1:1Additional Notes
N/A