Datasets:
File size: 4,393 Bytes
472babc | 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 | #!/usr/bin/env python3
"""GPU fix loop on data/fix_todo.jsonl with the clean labeler: up to --rounds rounds, each round regenerates
the first still-failing band of every row (prefix = game + earlier bands, --n candidates, first one passing
the rule check is spliced in) and re-checks. Rows with 3+ bad bands first try a whole new set.
Writes labels/fix/fix_wK.jsonl {key, labels, src, bad_after} + .done."""
import argparse
import json
import sys
import time
import types
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
import grounding as G # noqa: E402
import train as T # noqa: E402
from repair_labels import bad_bands # noqa: E402
I = "/iopsstor/scratch/cscs/ibadanin/projects/llm_chess/"
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", default=I + "results/labeler/ckpt_full_gemma3_1b_clean")
ap.add_argument("--worker", type=int, required=True)
ap.add_argument("--n-workers", type=int, required=True)
ap.add_argument("--rounds", type=int, default=5)
ap.add_argument("--n", type=int, default=8)
ap.add_argument("--sub", type=int, default=0, help="rerun one worker's rows split over --n-sub GPUs")
ap.add_argument("--n-sub", type=int, default=1)
a = ap.parse_args()
out = Path(I + f"labels/fix/fix_w{a.worker}" + (f"s{a.sub}" if a.n_sub > 1 else "") + ".jsonl")
mark = Path(str(out) + (".ok" if a.n_sub > 1 else ".done"))
out.parent.mkdir(parents=True, exist_ok=True)
rows = [json.loads(l) for i, l in enumerate(open(I + "data/fix_todo.jsonl")) if i % a.n_workers == a.worker]
rows = rows[a.sub::a.n_sub]
from transformers import AutoTokenizer # noqa: E402
from vllm import LLM, SamplingParams # noqa: E402
cfg = types.SimpleNamespace(input="compact", tag=0, special_tokens=0, max_in=2048)
tok = AutoTokenizer.from_pretrained(a.ckpt)
llm = LLM(model=a.ckpt, dtype="bfloat16", max_model_len=2560, gpu_memory_utilization=0.9,
max_num_seqs=1024, max_num_batched_tokens=32768, enable_prefix_caching=True)
sp_line = SamplingParams(n=a.n, temperature=0.8, max_tokens=120, stop=["\n"])
sp_set = SamplingParams(n=a.n, temperature=0.8, max_tokens=320)
enc = lambda text: {"prompt_token_ids": tok(text, add_special_tokens=True)["input_ids"][-2300:]} # noqa: E731
for r in rows:
r["bad_now"] = bad_bands(r["user"], r["labels"])
print(f"worker {a.worker}: {len(rows)} rows, {sum(map(len, (r['bad_now'] for r in rows)))} bad bands", flush=True)
t0 = time.time()
multi = [r for r in rows if len(r["bad_now"]) >= 3]
for r, o in zip(multi, llm.generate([enc(T.prompt_text(r, cfg, None)) for r in multi], sp_set, use_tqdm=False)):
sets = [s for s in (T.parse_joint(c.text) for c in o.outputs) if all(s.get(b) for b in T.BANDS)]
if sets:
best = min(sets, key=lambda s: len(bad_bands(r["user"], s)))
if len(bad_bands(r["user"], best)) < len(r["bad_now"]):
r["labels"], r["src"], r["bad_now"] = {b: best[b] for b in T.BANDS}, "replace", bad_bands(r["user"], best)
for k in range(a.rounds):
todo = [r for r in rows if r["bad_now"]]
if not todo:
break
ps = []
for r in todo:
b = r["bad_now"][0]
head = "".join(f"{T.BAND_NAME[x]}: {r['labels'][x].strip()}\n" for x in T.BANDS[: T.BANDS.index(b)])
ps.append(enc(T.prompt_text(r, cfg, None) + head + f"{T.BAND_NAME[b]}:"))
stuck = 0
for r, o in zip(todo, llm.generate(ps, sp_line, use_tqdm=False)):
b = r["bad_now"][0]
ok = [c.text.strip() for c in o.outputs if c.text.strip() and not bad_bands(r["user"], {b: c.text.strip()})]
if ok:
r["labels"] = dict(r["labels"], **{b: ok[0]})
r["src"] = "patch" if r["src"] == "orig" else r["src"]
else:
stuck += 1
r["bad_now"] = sorted(bad_bands(r["user"], r["labels"]), key=lambda x: (x == b and not ok, T.BANDS.index(x)))
left = sum(len(r["bad_now"]) for r in rows)
print(f"round {k + 1}: tried {len(todo)} stuck {stuck} bad bands left {left} ({time.time() - t0:.0f}s)", flush=True)
with open(str(out) + ".tmp", "w") as fo:
for r in rows:
fo.write(json.dumps({"key": r["key"], "labels": r["labels"], "src": r["src"], "bad_after": r["bad_now"]}) + "\n")
Path(str(out) + ".tmp").rename(out)
mark.write_text(json.dumps({"rows": len(rows), "bad_left": sum(len(r["bad_now"]) for r in rows),
"sec": round(time.time() - t0)}))
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