Gahow Wang
d5b276180d
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>
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import numpy as np
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import analyze_phase5 as a
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def main() -> None:
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adjusted = a.holm({"A1": 0.001, "A2": 0.02, "A3": 0.04, "A4": 0.5})
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assert adjusted == {"A1": 0.004, "A2": 0.06, "A3": 0.08, "A4": 0.5}
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assert a.ci(np.arange(100, dtype=np.float64)) == [2.475, 96.52499999999999]
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runs = [
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{"blocks": np.asarray([[10.0, 2.0]] * 48)},
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{"blocks": np.asarray([[20.0, 4.0]] * 48)},
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{"blocks": np.asarray([[30.0, 6.0]] * 48)},
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]
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draws = a.hierarchical_draws(runs, np.random.default_rng(a.SEED))
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assert draws.shape == (a.RESAMPLES,)
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assert np.allclose(draws, 5.0)
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assert a.point_efficiency(runs) == 5.0
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idle_blocks = np.asarray([[0.0, 0.0]] + [[10.0, 2.0]] * 47)
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idle_draws = a.hierarchical_draws(
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[{"blocks": idle_blocks}], np.random.default_rng(a.SEED)
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)
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assert np.all(np.isfinite(idle_draws))
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assert np.allclose(idle_draws, 5.0)
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assert a.point_efficiency([{"blocks": idle_blocks}]) == 5.0
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assert a.two_sided_p(np.asarray([-1.0, 1.0])) == 1.0
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print("phase5 analysis: PASS")
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if __name__ == "__main__":
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main()
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