| """Reproduce the frozen V4.2 FP32 comparison in a separate local output directory. |
| |
| Scores the preselected best-LM checkpoint (epoch 20, chosen on complete LM BPC) |
| on the four pinned nonblind pools. The original October 20 evaluation script |
| remains with its recorded outputs; this entry adds only an explicit output path. |
| """ |
| import argparse |
| import copy |
| import datetime |
| import json |
| import math |
| import sys |
| import time |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[2] |
| sys.path.insert(0, str(ROOT / 'src')) |
| import torch |
| from vimeml.training.data import file_sha, write_json |
| from vimeml.training.infer_v4 import JapaneseLMV4 |
| from vimeml.posttraining.infer import TRACKS, read_snapshot |
| from vimeml.benchmarks.evaluate_ajimee import rerank, summarize, metrics |
| from vimeml.benchmarks.standard_ime import source_metrics |
| from vimeml.benchmarks.comparison import paired, rows as read_rows |
|
|
| OUT = None |
| DOWNLOAD = ROOT / 'outputs/training-v42-20261020' |
| CHECKPOINT = DOWNLOAD / 'remote/checkpoints/best-lm.pt' |
| BASELINES = { |
| 'v21': ROOT / 'outputs/evaluation-v4-20261015/v21', |
| 'v4b': ROOT / 'outputs/evaluation-v4-b12cosine-20261015/best', |
| 'v41-epoch13': ROOT / 'outputs/evaluation-v41-20261016/best-ordinary/ordinary', |
| 'v41-epoch16': ROOT / 'outputs/evaluation-v41-20261016/last/ordinary', |
| } |
|
|
|
|
| def read(path): |
| return json.loads(path.read_text(encoding='utf-8')) |
|
|
|
|
| def main(): |
| global OUT |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument('--output', type=Path, required=True, |
| help='Separate empty output directory; completed results are never overwritten') |
| args = parser.parse_args() |
| OUT = args.output.resolve() |
| if OUT.exists() and any(OUT.iterdir()): |
| parser.error('Use an empty output directory to preserve prior evaluations') |
| if not torch.cuda.is_available(): |
| parser.error('The recorded V4.2 comparison uses an FP32 CUDA GPU') |
| receipt = read(DOWNLOAD / 'download-receipt.json') |
| assert receipt['status'] == 'verified' |
| digest = file_sha(CHECKPOINT) |
| assert digest == next(r['sha256'] for r in receipt['files'] if r['path'] == 'checkpoints/best-lm.pt') |
| frozen = {track: read_snapshot(track) for track in TRACKS} |
| torch.set_num_threads(4) |
| torch.backends.cuda.matmul.allow_tf32 = False |
| torch.backends.cudnn.allow_tf32 = False |
| torch.set_float32_matmul_precision('highest') |
| lm = JapaneseLMV4(CHECKPOINT, ROOT / 'artifacts/tokenizers/v4-a/ja-unigram-8192', 'cuda') |
| assert lm.metadata['checkpoint_step'] == 1081942 |
| assert lm.model.parameter_count() == 15070848 |
| assert all(torch.isfinite(p).all().item() for p in lm.model.parameters()) |
| OUT.mkdir(parents=True, exist_ok=True) |
| write_json(OUT / 'plan.json', { |
| 'created_utc': datetime.datetime.now(datetime.timezone.utc).isoformat(), |
| 'repo': receipt['repo_id'], 'revision': receipt['revision'], |
| 'checkpoint_sha256': digest, 'checkpoint_step': 1081942, |
| 'selection': 'best complete LM validation BPC; epoch20 selected before local IME evaluation', |
| 'tracks': {k: {'cases': len(v), 'sha256': TRACKS[k][1]} for k, v in frozen.items()}, |
| 'semantics': 'full-vocabulary suffix logP sum; joint context+candidate tokenization; no final EOS, truncation, or tuning; stable ties', |
| 'precision': 'FP32; TF32 disabled', 'blind_read': False, 'test_read': False, |
| 'script_sha256': file_sha(Path(__file__)), |
| }) |
| write_json(OUT / 'environment.json', {'torch': str(torch.__version__), 'cuda': torch.version.cuda, |
| 'gpu': torch.cuda.get_device_name(), 'parameters': lm.model.parameter_count(), 'model': lm.metadata}) |
| results = {} |
| with torch.inference_mode(): |
| for track, original in frozen.items(): |
| rows = copy.deepcopy(original) |
| began = time.perf_counter() |
| rerank(lm, rows) |
| dest = OUT / track |
| dest.mkdir() |
| (dest / 'scores.jsonl').write_text(''.join(json.dumps(r, ensure_ascii=False, allow_nan=False) + '\n' for r in rows), encoding='utf-8') |
| result = {'metrics': summarize(rows), 'elapsed_seconds': time.perf_counter() - began, |
| 'source_metrics': source_metrics(rows, metrics) if 'source' in rows[0] else None} |
| write_json(dest / 'metrics.json', result) |
| result['comparisons'] = {} |
| for name, folder in BASELINES.items(): |
| base = read_rows(folder / track / 'scores.jsonl') |
| assert len(base) == len(rows) |
| for a, b in zip(base, rows, strict=True): |
| assert all(a[k] == b[k] for k in ['id', 'query', 'left_context', 'answers', 'candidates']) |
| assert a['orders']['azookey'] == b['orders']['azookey'] |
| comparison = paired(base, rows) |
| comparison['paired'].pop('significant_at_0_05', None) |
| write_json(dest / f'paired-{name}.json', comparison) |
| result['comparisons'][name] = comparison['paired'] |
| results[track] = result |
| print(json.dumps({'track': track, 'top1': result['metrics']['all']['lm_context_sum']['top1_correct'], |
| 'comparisons': result['comparisons']}, ensure_ascii=False), flush=True) |
| for track in TRACKS: |
| read_snapshot(track) |
| assert file_sha(CHECKPOINT) == digest |
| write_json(OUT / 'summary.json', {'status': 'complete', 'results': results, 'blind_read': False, 'test_read': False}) |
| print('V42_LOCAL_EVALUATION_COMPLETE', flush=True) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
