chore: sync from 5168 - #8
Merged
Merged
Conversation
binaryaaron
force-pushed
the
aagonzales/sync-5169-from-nmp
branch
from
February 6, 2026 16:57
e0d209c to
be16b13
Compare
Contributor
|
All contributors have signed the DCO ✍️ ✅ |
Collaborator
Author
|
I have read the DCO document and I hereby sign the DCO. |
Collaborator
Author
|
recheck |
binaryaaron
added a commit
that referenced
this pull request
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
added a commit
that referenced
this pull request
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
added a commit
that referenced
this pull request
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
added a commit
that referenced
this pull request
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
added a commit
that referenced
this pull request
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
that referenced
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>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
Type of Change
Testing
Related Issues
Closes #7