File size: 7,637 Bytes
53a1159 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 | """Run separately frozen direction tuning and complete editing trajectories."""
import argparse
import copy
import itertools
import json
import os
import platform
import sys
from datetime import datetime, timezone
from pathlib import Path
import torch
from llm_memory_editability.bios_data import write_json
from llm_memory_editability.bios_direction import (
ARMS,
ROOT,
candidate_configs,
digest,
edit_batch,
load_parent,
minimal_task,
)
OUTPUT = ROOT / "results/bios-direction-v1"
ARTIFACT = ROOT / "docs/development-artifacts/direction-v1"
CONFIG = ROOT / "configs/bios-direction-v1.json"
def freeze():
config = json.loads(CONFIG.read_text())
files = [
CONFIG,
ROOT / "src/llm_memory_editability/bios_direction.py",
Path(__file__),
ROOT / "src/llm_memory_editability/bios_model.py",
ROOT / "src/llm_memory_editability/bios_data.py",
]
files += [ROOT / f"results/bios-path-minimal-v1/seed-{seed}/parent.pt" for seed in (0, 1)]
manifest = dict(
created=datetime.now(timezone.utc).isoformat(),
config=config,
files={str(p.relative_to(ROOT)): digest(p) for p in files},
python=sys.version,
executable=sys.executable,
torch=torch.__version__,
platform=platform.platform(),
arms={arm: candidate_configs(arm) for arm in ARMS},
)
if (ARTIFACT / "lock.json").exists():
assert json.loads((ARTIFACT / "lock.json").read_text())["files"] == manifest["files"], (
"Frozen sources changed"
)
return
write_json(ARTIFACT / "lock.json", manifest)
(ARTIFACT / "preregistration.md").write_text(
(ROOT / "docs/experimental-protocol.md").read_text()
)
for p in files:
if p.suffix in (".py", ".json"):
target = ARTIFACT / "source" / p.relative_to(ROOT)
target.parent.mkdir(parents=True, exist_ok=True)
target.write_bytes(p.read_bytes())
write_json(
ARTIFACT / "tasks.json",
[
minimal_task(s, p, k, "cpu").manifest()
for s, p, k in itertools.product(
(0, 1), (0, 1, 2), ("selective", "coherent", "independent")
)
],
)
print(json.dumps({"event": "frozen", "hash": digest(ARTIFACT / "lock.json")}), flush=True)
def verify():
lock = json.loads((ARTIFACT / "lock.json").read_text())
for path, sha in lock["files"].items():
assert digest(ROOT / path) == sha, f"Frozen source changed: {path}"
return lock
def tune(seed, device, arms):
model = load_parent(ROOT / f"results/bios-path-minimal-v1/seed-{seed}/parent.pt", device)
for arm in arms:
tasks = []
configs = []
for kind, cfg in itertools.product(
("selective", "coherent", "independent"), candidate_configs(arm)
):
tasks.append(minimal_task(seed, 0, kind, device))
configs.append(cfg)
records = edit_batch(
model, 0, tasks, arm, configs, 256, OUTPUT / "tune" / f"seed-{seed}" / arm
)
print(
json.dumps({"event": "tune", "seed": seed, "arm": arm, "records": len(records)}),
flush=True,
)
def select():
# Shared soft penalty per object: objective held fixed across the two solvers.
summaries = {}
for arm in ARMS:
rows = []
for seed in (0, 1):
records = json.loads(
(OUTPUT / "tune" / f"seed-{seed}" / arm / "metrics.json").read_text()
)
for record in records:
metrics = record["timeline"][-1]["sets"]
rows.append(
dict(
config=record["config"],
v_broken=metrics["V"]["broken"],
e_nll=metrics["E"]["nll"],
)
)
aggregated = []
for cfg in candidate_configs(arm):
matching = [x for x in rows if x["config"] == cfg]
assert len(matching) == 6
aggregated.append(
dict(
config=cfg,
v_broken=sum(x["v_broken"] for x in matching),
e_nll=sum(x["e_nll"] for x in matching) / 6,
)
)
summaries[arm] = aggregated
choices = {}
for obj in ("func", "repr"):
soft = [f"{obj}-soft-{solver}" for solver in ("adam", "gn")]
levels = sorted({x["config"]["level"] for x in summaries[soft[0]]})
candidates = []
for level in levels:
best = [
min(
(x for x in summaries[arm] if x["config"]["level"] == level),
key=lambda x: (x["v_broken"], x["e_nll"]),
)
for arm in soft
]
candidates.append(
(sum(x["v_broken"] for x in best), sum(x["e_nll"] for x in best), best)
)
best = min(candidates, key=lambda x: x[:2])[2]
for arm, row in zip(soft, best, strict=True):
choices[arm] = row["config"]
for solver in ("adam", "gn"):
arm = f"{obj}-hard-{solver}"
choices[arm] = min(summaries[arm], key=lambda x: (x["v_broken"], x["e_nll"]))["config"]
write_json(ARTIFACT / "tuning-summary.json", summaries)
write_json(
ARTIFACT / "selected.json",
dict(
created=datetime.now(timezone.utc).isoformat(),
choices=choices,
rule=(
"Minimize summed V broken, then mean E NLL; soft penalty shared across solvers; "
"final accepted-budget sample; no U"
),
),
)
print(json.dumps(choices), flush=True)
def main_runs(seed, device, arms):
selected = json.loads((ARTIFACT / "selected.json").read_text())["choices"]
model = load_parent(ROOT / f"results/bios-path-minimal-v1/seed-{seed}/parent.pt", device)
for arm in arms:
tasks = [
minimal_task(seed, p, k, device)
for p, k in itertools.product((0, 1, 2), ("selective", "coherent", "independent"))
]
records = edit_batch(
model,
0,
tasks,
arm,
[copy.deepcopy(selected[arm]) for _ in tasks],
1024,
OUTPUT / "main" / f"seed-{seed}" / arm,
)
print(
json.dumps({"event": "main", "seed": seed, "arm": arm, "records": len(records)}),
flush=True,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("command", choices=("freeze", "tune", "select", "main"))
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--device", default="cuda:0")
parser.add_argument("--arms", nargs="+", default=list(ARMS))
args = parser.parse_args()
torch.set_num_threads(2)
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.allow_tf32 = False
if args.command == "freeze":
freeze()
return
verify()
if args.command == "select":
select()
return
print(
json.dumps(
{
"event": "start",
"command": args.command,
"seed": args.seed,
"gpu": torch.cuda.get_device_name(args.device),
"visible": os.getenv("CUDA_VISIBLE_DEVICES"),
}
),
flush=True,
)
(tune if args.command == "tune" else main_runs)(args.seed, torch.device(args.device), args.arms)
if __name__ == "__main__":
main()
|