| """Compare three frozen pretraining checkpoints on reviewed validation excerpts.""" |
| import argparse |
| import collections |
| import gc |
| import hashlib |
| import json |
| import math |
| from pathlib import Path |
| import sqlite3 |
| import time |
|
|
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoConfig, AutoModelForCausalLM, GenerationConfig, set_seed |
|
|
| from classical_tokenizer import ClassicalTokenizer |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| RUN = ROOT / 'runs/classical-smollm2-trunk' |
| PACK = ROOT / 'unified/evaluation-tasks-v1/validation-001' |
| CHECKPOINTS = {'anchor-24415': 'pre-decay-anchor-00024415', |
| 'best-27500': 'checkpoint-00027500', 'final-30518': 'checkpoint-00030518'} |
| |
| SELECTION = [ |
| (0, '料理手順', '染て網杓子', 'なかすがよし', None, |
| '冒頭は前文からの続き。料理の操作を含むまとまった段落を与える。'), |
| (3, '仏教説話', 'さつなれば。', '善女りうわうをくわんじやうし給ふ。', None, |
| '冒頭は前文からの続き。祈雨の場面までを入力する。'), |
| (12, '怪談・口語叙述', '正兵へ', 'のぞくまいそといつただけうすきみわろく', None, |
| '翻刻の細かい改行を保存。覗くことを禁じられる文脈を与える。'), |
| (14, '産業書の序文', '垝をしたゝめ', '蚕の患なからしめさせん事を欲するなり', '万延改元', |
| '末尾の欠字記号を含む年記を対象範囲から除外。原文の曖昧な語は修正しない。'), |
| (38, '平安随筆', '經など習ひて、', 'ふと覺ゆれ。', None, |
| 'Wikisource見出しを除外し、本文の完結した一文を入力する。'), |
| (62, '平安会話', 'かたはらなる人', '家の主と、定め申すべきことのはべるなり」といへば、', None, |
| '発言と地の文を含む会話本文。38と同作品群のため独立8作品とは扱わない。'), |
| (46, '軍記・候文', '但し当家の運命、', '強く御返答候処、', '【 NDLJP:', |
| 'ページ標識の手前までを参照継続に使う。継続が短い例として明記する。'), |
| (55, '漢文体の軍法', '一先陳之輩', '太不可然。', '以下闕脱', |
| '欠脱表示の手前まで。原文の校訂注〔命歟〕は保存し、読解の曖昧さを明記する。'), |
| ] |
|
|
|
|
| def sha(path): |
| h = hashlib.sha256() |
| with open(path, 'rb') as f: |
| for block in iter(lambda: f.read(1 << 20), b''): |
| h.update(block) |
| return h.hexdigest() |
|
|
|
|
| def write_json(path, value): |
| Path(path).write_text(json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False) + '\n') |
|
|
|
|
| def prepare(tokenizer): |
| manifest = json.loads((PACK / 'manifest.json').read_text()) |
| assert manifest['split'] == 'validation' |
| assert sha(PACK / 'cases.jsonl') == manifest['files']['cases.jsonl'] |
| assert sha(RUN / 'export/tokenizer.model') == manifest['tokenizer_sha256'] |
| cases = [json.loads(x) for x in (PACK / 'cases.jsonl').read_text().splitlines()] |
| split_rows = {r['id']: r for r in map(json.loads, (ROOT / 'unified/training-tokens-v1/build-006/splits.jsonl').read_text().splitlines())} |
| db = sqlite3.connect('file:' + str(ROOT / 'unified/pretrain-v1/build-002/build.sqlite3') + '?mode=ro', uri=True) |
| selected = [] |
| try: |
| for index, style, begin, end, stop, note in SELECTION: |
| case = cases[index] |
| assert split_rows[case['source_id']]['split'] == 'validation' |
| assert split_rows[case['source_id']]['group'] == case['group'] |
| text = db.execute('SELECT text FROM units WHERE id=?', (case['source_id'],)).fetchone()[0] |
| assert hashlib.sha256(text.encode()).hexdigest() == case['text_sha256'] |
| assert text[case['span_start']:case['span_end']] == case['raw_episode'] |
| raw = case['raw_episode'] |
| start = raw.index(begin) |
| boundary = raw.index(end, start) + len(end) |
| finish = raw.index(stop, boundary) if stop else len(raw) |
| prompt = raw[start:boundary] |
| reference = raw[boundary:finish] |
| assert len(reference) >= 24 |
| assert not any(x in prompt + reference for x in ('■', '□', '〓', '【 NDLJP:', '==')) |
| ids = tokenizer.raw_encode(prompt + reference) |
| assert tokenizer.decode(ids) == prompt + reference |
| selected.append(dict(id=f'case-{len(selected)+1:02d}', fixture_index=index, |
| fixture_id=case['id'], title=case['title'], source=case['source'], group=case['group'], |
| era=case['era'], style=style, source_id=case['source_id'], source_text_sha256=case['text_sha256'], |
| source_start=case['span_start'] + start, source_end=case['span_start'] + finish, |
| prompt=prompt, reference=reference, review_note=note, |
| review_status='Codex reviewed selected excerpt; not expert certification', |
| prompt_tokens=len(tokenizer.raw_encode(prompt)) + 1)) |
| finally: |
| db.close() |
| return selected |
|
|
|
|
| def repetition(ids, n=4): |
| grams = [tuple(ids[i:i+n]) for i in range(len(ids)-n+1)] |
| return 1 - len(set(grams)) / len(grams) if grams else 0. |
|
|
|
|
| @torch.inference_mode() |
| def score(model, tokenizer, case): |
| |
| text = case['prompt'] + case['reference'] |
| ids = torch.tensor([[1] + tokenizer.raw_encode(text)], device='cuda') |
| with torch.autocast('cuda', dtype=torch.bfloat16): |
| logits = model(input_ids=ids[:, :-1], use_cache=False).logits |
| total = float(F.cross_entropy(logits.float().reshape(-1, logits.shape[-1]), ids[:, 1:].reshape(-1), reduction='sum')) |
| return dict(raw_characters=len(text), predicted_tokens=ids.shape[1]-1, |
| nll_sum=total, nll=total/(ids.shape[1]-1), bpc=total/(math.log(2)*len(text))) |
|
|
|
|
| def summary(rows, scores): |
| result = {} |
| for checkpoint in CHECKPOINTS: |
| s = [r for r in scores if r['checkpoint'] == checkpoint] |
| by_source = {} |
| for source in ('minhon', 'wikisource'): |
| chosen = [r for r in s if r['source'] == source] |
| by_source[source] = sum(r['nll_sum'] for r in chosen) / (math.log(2)*sum(r['raw_characters'] for r in chosen)) |
| result[checkpoint] = dict(by_source_bpc=by_source, |
| equal_source_bpc=sum(by_source.values())/len(by_source), modes={}) |
| for mode in ('greedy', 'sampling'): |
| rs = [r for r in rows if r['checkpoint'] == checkpoint and r['mode'] == mode] |
| result[checkpoint]['modes'][mode] = dict(examples=len(rs), |
| eos_stops=sum(r['stopped_with_eos'] for r in rs), |
| mean_repeated_token_4gram_fraction=sum(r['repeated_token_4gram_fraction'] for r in rs)/len(rs), |
| mean_generated_tokens=sum(len(r['generated_ids']) for r in rs)/len(rs)) |
| return result |
|
|
|
|
| def report(output, cases, rows, scores, stats): |
| lines = ['# 事前学習3 checkpointの生成比較', '', |
| 'validationから事前に選んだ8例・7作品群。学習やtest評価は行っていない。選択範囲はCodexが目視確認し、原文位置・SHA256・splitを照合した。既存64例全体の専門家点検を完了したものではない。', '', |
| '本文続きを生成するbaseモデルとして、同一prompt・BOS・既存tokenizerを使用。各生成は最大128新規token。greedyとtemperature=0.8/top_p=0.95のsamplingを比較。samplingは例ごとに同一seedを3モデルへ設定するが、各モデルの確率分布は異なる。反復抑制の追加設定は入れていない。', '', |
| '| checkpoint | 等出典平均BPC | greedy反復4gram比 | sampling反復4gram比 |', |
| '|---|---:|---:|---:|'] |
| for name, row in stats.items(): |
| lines.append(f"| {name} | {row['equal_source_bpc']:.4f} | {row['modes']['greedy']['mean_repeated_token_4gram_fraction']:.3f} | {row['modes']['sampling']['mean_repeated_token_4gram_fraction']:.3f} |") |
| lines += ['', 'BPCはpromptと参照継続を含む同一原文範囲全体のteacher-forced指標で、低いほどよい。反復比は生成tokenの4gramが既出である割合の平均。自然な繰り返しも数えるため品質判定そのものではない。8例の同一作品群を含む小規模比較で、有意差や汎化を保証しない。', |
| 'EOS終了数はsummary.jsonに保存。baseモデルには本文を続ける役割があるため、EOSが出ないこと自体を失敗とはしない。原文参照の完全再現も成功条件ではなく、文体・話題・文章のつながりを併せて読む。', ''] |
| for case in cases: |
| lines += [f"## {case['id']} {case['title']} — {case['style']}", '', |
| f"出典: {case['source']} / {case['era']} / {case['source_id']}。prompt {case['prompt_tokens']} token。", '', |
| '点検メモ: ' + case['review_note'], '', '### 入力', '', '```text', case['prompt'], '```', '', |
| '### 原文の参照継続', '', '```text', case['reference'], '```', ''] |
| for mode in ('greedy', 'sampling'): |
| lines += ['### ' + mode, ''] |
| for name in CHECKPOINTS: |
| row = next(r for r in rows if r['id'] == case['id'] and r['mode'] == mode and r['checkpoint'] == name) |
| lines += [f"**{name}**（{len(row['generated_ids'])} token、EOS={row['stopped_with_eos']}、反復比={row['repeated_token_4gram_fraction']:.3f}）", '', '```text', row['generated'], '```', ''] |
| (output / 'COMPARISON.md').write_text('\n'.join(lines)) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument('--output', default='runs/pretrain-generation-comparison-001') |
| args = parser.parse_args() |
| output = Path(args.output) |
| output.mkdir(parents=True, exist_ok=False) |
| torch.set_num_threads(4) |
| tokenizer = ClassicalTokenizer(RUN / 'export/tokenizer.model') |
| cases = prepare(tokenizer) |
| write_json(output / 'cases.json', cases) |
| model_hashes = {name: sha(RUN / 'checkpoints' / directory / 'model.safetensors') for name, directory in CHECKPOINTS.items()} |
| write_json(output / 'manifest.json', dict(split='validation', seed_rule='20261027 + case_index', |
| max_new_tokens=128, temperature=0.8, top_p=0.95, checkpoints=CHECKPOINTS, |
| checkpoint_sha256=model_hashes, tokenizer_sha256=sha(RUN / 'export/tokenizer.model'), |
| fixture_sha256=sha(PACK / 'cases.jsonl'), script_sha256=sha(__file__), |
| case_count=len(cases), independent_groups=len({r['group'] for r in cases}), |
| case_file_sha256=sha(output / 'cases.json'), device='cuda', weights='float32', autocast='bfloat16')) |
| scores, rows = [], [] |
| start = time.monotonic() |
| for name, directory in CHECKPOINTS.items(): |
| model = AutoModelForCausalLM.from_pretrained(RUN / 'checkpoints' / directory, |
| config=AutoConfig.from_pretrained(RUN / 'export', local_files_only=True), |
| generation_config=GenerationConfig.from_pretrained(RUN / 'export', local_files_only=True), |
| local_files_only=True, attn_implementation='sdpa', dtype=torch.float32).cuda().eval() |
| for index, case in enumerate(cases): |
| scores.append(dict(id=case['id'], checkpoint=name, source=case['source'], **score(model, tokenizer, case))) |
| ids = torch.tensor([[1] + tokenizer.raw_encode(case['prompt'])], device='cuda') |
| for mode in ('greedy', 'sampling'): |
| set_seed(20261027 + index) |
| config = GenerationConfig(max_new_tokens=128, do_sample=mode == 'sampling', |
| eos_token_id=2, pad_token_id=3, bos_token_id=1, use_cache=True) |
| if mode == 'sampling': |
| config.temperature, config.top_p = 0.8, 0.95 |
| with torch.inference_mode(), torch.autocast('cuda', dtype=torch.bfloat16): |
| generated = model.generate(input_ids=ids, attention_mask=torch.ones_like(ids), generation_config=config) |
| new = generated[0, ids.shape[1]:].tolist() |
| row = dict(id=case['id'], checkpoint=name, source=case['source'], mode=mode, |
| seed=20261027 + index, generated_ids=new, |
| generated=tokenizer.decode(new, skip_special_tokens=True), stopped_with_eos=bool(new and new[-1] == 2), |
| repeated_token_4gram_fraction=repetition([i for i in new if i not in (1, 2, 3)])) |
| rows.append(row) |
| with (output / 'generations.jsonl').open('a') as f: |
| f.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + '\n') |
| print(json.dumps(dict(checkpoint=name, case=case['id'], completed_generations=len(rows), |
| elapsed_seconds=time.monotonic()-start)), flush=True) |
| del model |
| gc.collect() |
| torch.cuda.empty_cache() |
| stats = summary(rows, scores) |
| write_json(output / 'scores.json', scores) |
| write_json(output / 'summary.json', stats) |
| report(output, cases, rows, scores, stats) |
| assert len(rows) == 48 and len(scores) == 24 |
| unchanged = {name: sha(RUN / 'checkpoints' / directory / 'model.safetensors') for name, directory in CHECKPOINTS.items()} |
| assert unchanged == model_hashes |
| write_json(output / 'verification.json', dict(complete=True, generations=len(rows), scores=len(scores), |
| checkpoints_unchanged=True, cases_verified_against_validation_source=True, |
| artifact_sha256={p.name: sha(p) for p in output.iterdir() if p.is_file()}, |
| elapsed_seconds=time.monotonic()-start)) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
