| |
| """Bakeoff orchestrator: train + eval every BIO arm with identical settings. |
| |
| One arm = one subprocess = one variable. Arms never share state. Any arm that |
| crashes is recorded as failed and the run continues, so one bad candidate |
| cannot abort the bakeoff. |
| |
| Usage: run_bakeoff.py [--only tag1,tag2] [--skip-eval] |
| """ |
|
|
| import argparse |
| import json |
| import os |
| import subprocess |
| import sys |
| import time |
|
|
| SCRIPTS = "/opt/vox/sandbox/scripts" |
| PY = "/opt/vox/venv/bin/python" |
| CKPT = "/opt/vox/temp/ckpt" |
| BENCH = "/opt/vox/sandbox/benchmarks" |
|
|
| |
| ARMS = [ |
| ("distilbert-cased", "distilbert/distilbert-base-cased"), |
| ("bert-base-cased", "google-bert/bert-base-cased"), |
| ("roberta-base", "FacebookAI/roberta-base"), |
| ("deberta-v3-base", "microsoft/deberta-v3-base"), |
| ("modernbert-base", "answerdotai/ModernBERT-base"), |
| ("electra-base-disc", "google/electra-base-discriminator"), |
| ] |
|
|
| COMMON = "--seed 42 --epochs 8 --batch-size 16 --lr-encoder 3e-5 --lr-head 1e-3 --max-len 128" |
|
|
|
|
| def sh(cmd, log): |
| with open(log, "a") as f: |
| f.write(f"\n$ {' '.join(cmd)}\n") |
| f.flush() |
| p = subprocess.run(cmd, stdout=f, stderr=subprocess.STDOUT) |
| return p.returncode |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--only", default="") |
| ap.add_argument("--skip-eval", action="store_true") |
| args = ap.parse_args() |
|
|
| only = {x for x in args.only.split(",") if x} |
| arms = [a for a in ARMS if not only or a[0] in only] |
| os.makedirs(CKPT, exist_ok=True) |
| os.makedirs(BENCH, exist_ok=True) |
| os.makedirs("/opt/vox/temp/logs", exist_ok=True) |
|
|
| summary = [] |
| for tag, hf in arms: |
| out_dir = os.path.join(CKPT, tag) |
| log = f"/opt/vox/temp/logs/arm_{tag}.log" |
| t0 = time.time() |
| print(f"=== {tag} ({hf})", flush=True) |
|
|
| if not os.path.exists(os.path.join(out_dir, "train_meta.json")): |
| rc = sh([PY, "-u", f"{SCRIPTS}/train_span.py", "--model", hf, |
| "--out", out_dir] + COMMON.split(), log) |
| if rc != 0: |
| print(f" TRAIN FAILED rc={rc} -> see {log}", flush=True) |
| summary.append({"tag": tag, "model": hf, "status": "train_failed"}) |
| continue |
| else: |
| print(" checkpoint exists, skipping train", flush=True) |
|
|
| meta = json.load(open(os.path.join(out_dir, "train_meta.json"))) |
| print(f" trained {meta['n_train']} ex, {meta['params_m']}M, " |
| f"loss {meta['train_log'][0]['train_loss']}->{meta['train_log'][-1]['train_loss']}, " |
| f"{meta['wall_s']}s", flush=True) |
|
|
| if args.skip_eval: |
| continue |
|
|
| ej = os.path.join(BENCH, f"ft_{tag}.json") |
| rc = sh([PY, "-u", f"{SCRIPTS}/eval_span.py", "--model", out_dir, "--tag", f"ft_{tag}", |
| "--out", ej, "--cpu-latency", "--export-onnx", "--onnx-int8"], log) |
| if rc != 0 or not os.path.exists(ej): |
| print(f" EVAL FAILED rc={rc} -> see {log}", flush=True) |
| summary.append({"tag": tag, "model": hf, "status": "eval_failed"}) |
| continue |
|
|
| r = json.load(open(ej)) |
| b = r["best"] |
| summary.append({ |
| "tag": tag, "model": hf, "params_m": r["params_m"], |
| "exact_match": b["exact_match"], "span_word_f1": b["span_word"]["f1"], |
| "precision": b["span_word"]["precision"], "recall": b["span_word"]["recall"], |
| "over_deletion_rate": b["over_deletion_rate"], |
| "harmful_span_rate": b["harmful_span_rate"], |
| "thr": b["threshold"], "passes_gate": b["passes_gate"], |
| "clean_exact_match": b["per_category"].get("CLEAN", {}).get("exact_match"), |
| "dis_exact_match": b["per_category"].get("DISFLUENCY", {}).get("exact_match"), |
| "cpu_p95_ms": r.get("cpu_latency", {}).get("p95_ms"), |
| "onnx_parity_rate": r.get("onnx", {}).get("parity_rate"), |
| "int8_compression": r.get("onnx", {}).get("int8_compression"), |
| }) |
| print(f" EM={b['exact_match']} f1={b['span_word']['f1']} " |
| f"overdel={b['over_deletion_rate']}% harmful={b['harmful_span_rate']}% " |
| f"gate={b['passes_gate']} cpu_p95={r.get('cpu_latency',{}).get('p95_ms')}ms " |
| f"parity={r.get('onnx',{}).get('parity_rate')}%", flush=True) |
|
|
| with open("/opt/vox/temp/logs/bakeoff_summary.json", "w") as f: |
| json.dump(summary, f, indent=2) |
| print("\n=== BAKEOFF SUMMARY ===", flush=True) |
| for s in sorted([x for x in summary if "exact_match" in x], |
| key=lambda x: -x["exact_match"]): |
| print(f" {s['tag']:20} EM={s['exact_match']:6} f1={s['span_word_f1']:.3f} " |
| f"P={s['precision']:.3f} R={s['recall']:.3f} overdel={s['over_deletion_rate']}% " |
| f"harm={s['harmful_span_rate']}% gate={s['passes_gate']} " |
| f"cpu_p95={s['cpu_p95_ms']}ms", flush=True) |
| print("artifacts: /opt/vox/sandbox/benchmarks/ft_<tag>.json", flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
