Gahow Wang
d975e57bb5
At replay_time_scale=0.2 the 600s arrival window compresses to 120s, so the inherited 900s wall-clock elapsed cap let overloaded TP1 probes burn ~15min each (the tractability hazard the brief flagged). Scale the caps proportionately to the time axis: early_stop_max_elapsed_s 900->180, early_stop_max_lag_s 120->30. Feasible probes (~120s arrival + drain) finish well inside 180s; overloaded probes die in ~3min. Both configs still differ only in use_harness + study_id. Adds the ablation doc skeleton and a read-only trajectory-extraction helper. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""Extract a per-iteration trajectory table from an ablation study store.
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Usage: python3 ablation_trajectory.py <study_store_dir>
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Prints iter, proposal source/name, config_patch summary, per_gpu, status,
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and the running incumbent per_gpu. Read-only.
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"""
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import json
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import sys
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from pathlib import Path
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def topo(patch):
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fp = (patch or {}).get("flag_patch", {}) or {}
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ep = (patch or {}).get("env_patch", {}) or {}
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parts = []
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for k, label in (
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("tensor-parallel-size", "TP"),
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("data-parallel-size", "DP"),
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("expert-parallel-size", "EP"),
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):
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if k in fp:
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parts.append(f"{label}{fp[k]}")
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runtime = {
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k: v
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for k, v in fp.items()
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if k not in ("tensor-parallel-size", "data-parallel-size", "expert-parallel-size")
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}
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runtime.update({f"env:{k}": v for k, v in ep.items()})
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base = "+".join(parts) if parts else "baseline-topo"
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if runtime:
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base += " | " + ", ".join(f"{k}={v}" for k, v in runtime.items())
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return base
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def main():
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store = Path(sys.argv[1])
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state = json.load(open(store / "state.json"))
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print(f"study_id: {state.get('study_id')}")
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print(f"best_trial: {state.get('best_trial_id')} best_per_gpu: {state.get('best_request_rate_per_gpu')}")
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print(f"stop_reason: {state.get('tuning_stop_reason')!r}")
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print(f"stop_diagnosis: {state.get('tuning_stop_diagnosis')!r}")
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print(f"stop_details: {json.dumps(state.get('tuning_stop_details'), ensure_ascii=False)}")
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print()
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incumbent = None
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hdr = f"{'iter':<5}{'trial':<11}{'status':<14}{'per_gpu':<10}{'incumbent':<11}config"
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print(hdr)
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print("-" * len(hdr))
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for i, t in enumerate(state.get("trials", []), 1):
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pg = t.get("best_request_rate_per_gpu")
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if pg is not None and (incumbent is None or pg > incumbent):
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incumbent = pg
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pgs = f"{pg:.4f}" if isinstance(pg, (int, float)) else str(pg)
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incs = f"{incumbent:.4f}" if isinstance(incumbent, (int, float)) else str(incumbent)
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print(
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f"{i:<5}{t.get('trial_id',''):<11}{str(t.get('status','')):<14}{pgs:<10}{incs:<11}{topo(t.get('config_patch'))}"
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)
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# also dump proposals dir to see what was *proposed* (incl. vetoed/failed)
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pdir = store / "proposals"
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if pdir.exists():
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print("\n-- proposal files (chronological) --")
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for p in sorted(pdir.glob("*.json")):
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try:
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pr = json.load(open(p))
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except Exception:
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continue
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print(f" {p.stem}: should_stop={pr.get('should_stop')} | {topo(pr.get('config_patch'))}")
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if __name__ == "__main__":
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main()
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