File size: 7,388 Bytes
3c5f059 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python
"""Train on seen MMP rule families and evaluate on disjoint held-out families."""
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
from polyedit import policy, realenv
from polyedit.rule_ood import build_rule_graphs
from load_polyedit_bundle import load_verifier
def split(canon):
return "eval" if int(hashlib.sha1(canon.encode()).hexdigest(), 16) % 5 == 0 else "train"
def evaluate(reqs, rollout, oracle):
rows = []
for req in reqs:
terminal = rollout(req)
start = req.target.distance(req.source, oracle)
final = min(req.target.distance(terminal, oracle), 1e3)
rows.append({"source": req.source, "terminal": terminal,
"success": realenv.hit(terminal, req), "regret": final,
"direction": float(final < start)})
return {"n": len(rows), "success": float(np.mean([r["success"] for r in rows])),
"regret": float(np.mean([r["regret"] for r in rows])),
"direction": float(np.mean([r["direction"] for r in rows])), "tasks": rows}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--frozen", type=Path, default=Path("data/real/frozen.json"))
ap.add_argument("--verifier_bundle", type=Path, required=True)
ap.add_argument("--seed", type=int, required=True)
ap.add_argument("--device", default="cuda")
ap.add_argument("--epochs", type=int, default=60)
ap.add_argument("--out", type=Path, required=True)
ap.add_argument("--ckpt", type=Path, required=True)
ap.add_argument("--wandb", action="store_true")
args = ap.parse_args()
records = json.loads(args.frozen.read_text())["polymers"]
graphs, values, rule_meta = build_rule_graphs(
records, ("Egc",), holdout_mod=2, holdout_bucket=0, min_support=3)
if set(rule_meta["train_families"]) & set(rule_meta["test_families"]):
raise SystemExit("rule-family leakage")
train_polymers = [p for p in values if split(p) == "train"]
eval_polymers = [p for p in values if split(p) == "eval"]
common = dict(budget=3, rel_width=0.03, directional=True, min_delta=1.0)
train_requests = (
realenv.build_requests(graphs["train"], values, ("Egc",), min_steps=1,
sources=train_polymers, seed=0, max_requests=250, **common)
+ realenv.build_requests(graphs["train"], values, ("Egc",), min_steps=2,
sources=train_polymers, seed=5, max_requests=125, **common))
tracks = {
"iid_single": realenv.build_requests(graphs["train"], values, ("Egc",), min_steps=1,
sources=eval_polymers, seed=1, max_requests=120, **common),
"iid_multi": realenv.build_requests(graphs["train"], values, ("Egc",), min_steps=2,
sources=eval_polymers, seed=2, max_requests=120, **common),
"ood_single": realenv.build_requests(graphs["test"], values, ("Egc",), min_steps=1,
sources=eval_polymers, seed=1, max_requests=120, **common),
"ood_multi": realenv.build_requests(graphs["test"], values, ("Egc",), min_steps=2,
sources=eval_polymers, seed=2, max_requests=120, **common),
}
if min(len(tracks["ood_single"]), len(tracks["ood_multi"])) < 30:
raise SystemExit("fewer than 30 OOD tasks")
run = None
split_hash = hashlib.sha256("\n".join(rule_meta["test_families"]).encode()).hexdigest()
if args.wandb:
import wandb
config = {"seed": args.seed, "epochs": args.epochs, "rule_split_hash": split_hash,
"min_rule_support": 3, "holdout_fraction": 0.5,
"train_families": len(rule_meta["train_families"]),
"test_families": len(rule_meta["test_families"]),
"tasks": {k: len(v) for k, v in tracks.items()}}
run = wandb.init(entity="promotion-kim", project="polyedit",
name=f"polyedit-real-rule-ood-s{args.seed}", config=config)
log = (lambda row: run.log(row)) if run else None
verifier, _ = load_verifier(args.verifier_bundle, args.device)
polymers = (set(graphs["train"]) | set(graphs["test"]) |
{p for graph in graphs.values() for row in graph.values() for p in row})
predictions = dict(zip(sorted(polymers), verifier.predict_many(sorted(polymers))))
verifiers = {"Egc": policy.CachedPredictor(verifier, predictions)}
embeddings = policy.encode_polymers(sorted(polymers), device=args.device)
steps = realenv.build_sft_steps(train_requests, graphs["train"])
features, masks = policy.step_features(steps, train_requests, embeddings, verifiers)
import torch
torch.manual_seed(args.seed)
model = policy.make_policy(len(next(iter(embeddings.values()))) + 1)
model = policy.train_sft(model, features, masks, epochs=args.epochs, device=args.device,
seed=args.seed, log=log)
oracle = realenv.real_oracle(values)
voracle = lambda p, prop: verifiers[prop].predict(p)
results = {}
for track, reqs in tracks.items():
graph = graphs["test"] if track.startswith("ood") else graphs["train"]
results[track] = {
"sft": evaluate(reqs, lambda r, g=graph: realenv.policy_rollout(
model, r, g, embeddings, verifiers, args.device), oracle),
"random": evaluate(reqs, lambda r, g=graph: realenv.random_rollout(
r.source, g, r.target, oracle, budget=r.budget, seed=args.seed), oracle),
"greedy_verifier": evaluate(reqs, lambda r, g=graph: realenv.greedy_rollout(
r.source, g, r.target, voracle, budget=r.budget), oracle),
"greedy_oracle": evaluate(reqs, lambda r, g=graph: realenv.greedy_rollout(
r.source, g, r.target, oracle, budget=r.budget), oracle),
}
if run:
run.log({f"rule_ood/{track}/{method}/{metric}": row[metric]
for method, row in results[track].items()
for metric in ("success", "regret", "direction")})
output = {"seed": args.seed, "rule_split_hash": split_hash, "rule_meta": rule_meta,
"n_train_requests": len(train_requests), "n_steps": len(steps),
"results": results, "wandb_run": run.url if run else None}
args.out.parent.mkdir(parents=True, exist_ok=True)
args.out.write_text(json.dumps(output, indent=2) + "\n")
args.ckpt.mkdir(parents=True, exist_ok=True)
torch.save({"format_version": 1, "state_dict": {k: v.detach().cpu() for k, v in model.state_dict().items()},
"mu": torch.from_numpy(model.mu_), "sigma": torch.from_numpy(model.sigma_),
"feat_dim": len(next(iter(embeddings.values()))) + 1,
"polybert": policy.POLYBERT, "rule_split_hash": split_hash, "seed": args.seed},
args.ckpt / "sft_policy_bundle.pt")
(args.ckpt / "config.json").write_text(json.dumps(
{"seed": args.seed, "rule_split_hash": split_hash,
"tasks": {k: len(v) for k, v in tracks.items()}}, indent=2) + "\n")
if run:
run.finish()
print(f"seed={args.seed} tasks={ {k: len(v) for k, v in tracks.items()} }", flush=True)
if __name__ == "__main__":
main()
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