chore: sync 5737 from nmp - #7
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binaryaaron
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May 27, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
binaryaaron
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May 27, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
binaryaaron
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Jun 4, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
binaryaaron
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Jun 9, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
binaryaaron
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Jun 15, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
binaryaaron
added a commit
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this pull request
Jul 10, 2026
Adds ``vllm_benchmark_analysis.py`` — the post-hoc analyzer that partitions cells by cluster signal, computes per-condition + per-cluster aggregates, and emits Welch-CI effect sizes. Combined #7+#8 from the original brief because they're tightly coupled (effect size lives on the same condition aggregate as the cluster stats). ## Pipeline ``analyze(output_dir, cluster_signal='auto', min_cells_per_condition=6) -> AnalysisReport``: 1. Load all BenchmarkOutput JSONs in dir, flatten candidate cells. 2. Auto-pick cluster signal (or use the operator's choice). 3. Silhouette-scored ``k`` in range [2, 4]; ``random_state=42``. 4. Remap labels so cluster 0 has the lowest signal mean (stable ordering). 5. Per-condition: pooled mean/stddev/CoV on effective_tok_s + acceptance_rate. 6. Per-(condition × cluster): mean/stddev/CoV within cluster. 7. Effect size: Welch's-t Δ ± 95% CI vs the 'baseline' condition, both pooled and per-cluster. 8. Refuse aggregates for conditions with <6 cells (brief mandate). ## Schema Pydantic models: ``AnalysisReport``, ``ConditionAggregate``, ``ConditionClusterAggregate``, ``ClusterStats``, ``ClusterAssignment``, ``EffectSize``. Each ``extra='forbid'``. ``AnalysisReport.to_markdown_summary()`` renders a human-readable table-formatted summary. ## CLI ``tools/vllm_benchmark.py analyze <output_dir> [--cluster-signal auto|wall_seconds|acceptance_rate] [--min-cells-per-condition N] [--json-out PATH]`` — runs the pipeline + prints markdown + optionally writes the full report JSON. ## Welch CI math ``_welch_ttest_ci(cand, base, alpha=0.05) -> (mean_diff, ci_low, ci_high, welch_df) | None``. Welch–Satterthwaite degrees of freedom computed explicitly. Returns None for underdetermined (n<2 either side or both stddevs zero). Validated synthetically: clear-difference inputs (cand mean ~1708, base mean ~1502, n=6 each) → Δ=+205, CI=[+193, +216], df=7.3, CI excludes 0; identical inputs → Δ=0, CI brackets 0 symmetrically. Signed-off-by: Aaron Gonzales <aagonzales@nvidia.com>
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