#!/usr/bin/env python3 """Fail fast when an experiment mixes incompatible eval/calibration settings.""" from __future__ import annotations import argparse import hashlib import json from collections import defaultdict from pathlib import Path def sha256_file(path: Path) -> str: digest = hashlib.sha256() with open(path, "rb") as file: for chunk in iter(lambda: file.read(1 << 20), b""): digest.update(chunk) return digest.hexdigest()[:16] def expected_eval(config: dict, corpus: str) -> dict: eval_cfg = config["eval"] corpus_path = Path(corpus) return { "corpus": str(corpus_path), "corpus_sha256": sha256_file(corpus_path), "max_sequences": eval_cfg["max_sequences"], "max_tokens": eval_cfg["max_tokens"], "min_tokens": eval_cfg["min_tokens"], } def config_hash(value: dict) -> str: return hashlib.sha256(json.dumps(value, sort_keys=True).encode("utf-8")).hexdigest()[:16] def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--config", type=Path, required=True) parser.add_argument("--root", type=Path, default=None) parser.add_argument("--json-out", type=Path, default=None) args = parser.parse_args() config = json.loads(args.config.read_text()) root = args.root or args.config.parent eval_cfg = config["eval"] calib_cfg = config["calibration"] corpora = {Path(path).name: path for path in eval_cfg["corpora"].values()} expected_by_name = {name: expected_eval(config, path) for name, path in corpora.items()} errors: list[str] = [] warnings: list[str] = [] ppl_rows = [] baseline_by_corpus: dict[str, list[float]] = defaultdict(list) for path in sorted(root.rglob("ppl_*.json")): data = json.loads(path.read_text()) corpus_name = Path(data.get("corpus", "")).name row = {"path": str(path), "corpus": corpus_name} ppl_rows.append(row) if corpus_name not in corpora: errors.append(f"{path}: unknown corpus {corpus_name!r}") continue expected = expected_by_name[corpus_name] if data.get("chunks") != expected["max_sequences"]: errors.append( f"{path}: chunks={data.get('chunks')} != config max_sequences={expected['max_sequences']}" ) if data.get("tokens") is not None and data.get("chunks"): approx = data["chunks"] * expected["max_tokens"] - data["chunks"] if abs(int(data["tokens"]) - approx) > expected["max_sequences"]: warnings.append( f"{path}: tokens={data.get('tokens')} differs from approx {approx}" ) if data.get("config"): if data["config"] != expected: errors.append(f"{path}: embedded eval config differs from model config") if data.get("config_hash") != config_hash(expected): errors.append(f"{path}: config_hash mismatch") else: warnings.append(f"{path}: legacy result without config/config_hash") if "bf16_ppl" in data: baseline_by_corpus[corpus_name].append(float(data["bf16_ppl"])) for corpus_name, values in baseline_by_corpus.items(): if max(values) - min(values) > 0.01: errors.append( f"BF16 baseline drift for {corpus_name}: {sorted(set(round(value, 4) for value in values))}" ) calibration_rows = [] calibration_corpora: set[Path] = set( Path(path) for path in calib_cfg.get("corpora", {}).values() ) qad_corpus = config.get("qad", {}).get("corpus") if qad_corpus: calibration_corpora.add(Path(qad_corpus)) for path in sorted(root.rglob("calibration.json")): data = json.loads(path.read_text()) row = {"path": str(path), "corpus": Path(data.get("corpus", "")).name} calibration_rows.append(row) if data.get("corpus"): calibration_corpora.add(Path(data["corpus"])) checks = { "samples": calib_cfg["samples"], "max_tokens": calib_cfg["max_tokens"], "output_block": calib_cfg["output_block"], } for key, expected in checks.items(): if data.get(key) != expected: errors.append(f"{path}: {key}={data.get(key)!r} != config {expected!r}") damping = float(data.get("damping", -1)) if abs(damping - float(calib_cfg["damping"])) > 1e-12: errors.append(f"{path}: damping={damping} != config {calib_cfg['damping']}") eval_corpora = {Path(path) for path in corpora.values()} def long_lines(path: Path) -> set[str]: return { line.strip() for line in path.read_text(encoding="utf-8").splitlines() if len(line.strip()) >= 20 } for eval_corpus in sorted(eval_corpora): if not eval_corpus.exists(): continue lines = [ line.strip() for line in eval_corpus.read_text(encoding="utf-8").splitlines() if len(line.strip()) >= 20 ] duplicates = len(lines) - len(set(lines)) if duplicates: errors.append(f"{eval_corpus}: {duplicates} duplicate long lines; dedup required") overlap_checks = 0 for calibration_corpus in sorted(calibration_corpora): if not calibration_corpus.exists(): errors.append(f"missing calibration corpus: {calibration_corpus}") continue for eval_corpus in sorted(eval_corpora): if not eval_corpus.exists(): errors.append(f"missing eval corpus: {eval_corpus}") continue overlap_checks += 1 if calibration_corpus.resolve() == eval_corpus.resolve(): errors.append(f"calibration and eval use the same file: {eval_corpus}") continue overlap = long_lines(calibration_corpus) & long_lines(eval_corpus) if overlap: sample = next(iter(overlap))[:80] errors.append( f"corpus overlap: {calibration_corpus} vs {eval_corpus} " f"({len(overlap)} lines, e.g. {sample!r})" ) report = { "config": str(args.config), "config_name": config.get("name"), "ppl_rows": len(ppl_rows), "calibration_rows": len(calibration_rows), "corpus_overlap_checks": overlap_checks, "errors": errors, "warnings": warnings, "ok": not errors, } text = json.dumps(report, indent=2, ensure_ascii=False) print(text) if args.json_out: args.json_out.parent.mkdir(parents=True, exist_ok=True) args.json_out.write_text(text + "\n") raise SystemExit(0 if not errors else 1) if __name__ == "__main__": main()