| """Print a Wikisource sample with token IDs, pieces, and frozen-stream checks.""" |
| import csv |
| import hashlib |
| import json |
| from pathlib import Path |
| import sqlite3 |
|
|
| import numpy as np |
|
|
| from classical_tokenizer import ClassicalTokenizer |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
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|
|
| def sha(path): |
| return hashlib.sha256(Path(path).read_bytes()).hexdigest() |
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|
| def main(): |
| output = ROOT / 'runs/wikisource-tokenization-inspection-001' |
| output.mkdir(exist_ok=False) |
| |
| cases = json.loads((ROOT / 'runs/pretrain-generation-comparison-001/cases.json').read_text()) |
| case = next(r for r in cases if r['id'] == 'case-05') |
| source_id = case['source_id'] |
| text = case['prompt'] |
| tokenizer_path = ROOT / 'runs/classical-smollm2-trunk/export/tokenizer.model' |
| tokenizer = ClassicalTokenizer(tokenizer_path) |
| splits = ROOT / 'unified/training-tokens-v1/build-006' |
| manifest = json.loads((splits / 'manifest.json').read_text()) |
| assert sha(tokenizer_path) == manifest['tokenizer_sha256'] |
| record = next(r for r in map(json.loads, (splits / 'splits.jsonl').read_text().splitlines()) if r['id'] == source_id) |
| assert record['source'] == 'wikisource' and record['split'] == 'validation' |
| with sqlite3.connect('file:' + str(ROOT / 'unified/pretrain-v1/build-002/build.sqlite3') + '?mode=ro', uri=True) as db: |
| document = db.execute('SELECT text FROM units WHERE id=?', (source_id,)).fetchone()[0] |
| assert hashlib.sha256(document.encode()).hexdigest() == record['text_sha256'] |
| assert document[case['source_start']:case['source_start']+len(text)] == text |
| full_ids = [1] + tokenizer.raw_encode(document) + [2] |
| stream = np.memmap(splits / f"wikisource.{record['split']}.bin", dtype='<u4', mode='r') |
| saved = stream[record['split_offset']:record['split_offset']+record['tokens']] |
| assert full_ids == saved.tolist(), 'Frozen document tokens differ from re-encoding' |
| assert hashlib.sha256(np.asarray(full_ids, dtype='<u4').tobytes()).hexdigest() == record['token_sha256'] |
| ids = tokenizer.raw_encode(text) |
| hf_ids = tokenizer(text, add_special_tokens=False)['input_ids'] |
| assert ids == hf_ids |
| assert tokenizer(text)['input_ids'] == [1] + ids |
| decoded = tokenizer.decode(ids) |
| assert decoded == text |
| assert '▁' not in text |
| proto = tokenizer.sp_model.encode(text, out_type='immutable_proto') |
| assert [p.id for p in proto.pieces] == ids |
| tokens = [] |
| for i, p in enumerate(proto.pieces): |
| assert text[p.begin:p.end] == p.surface |
| tokens.append(dict(index=i, id=p.id, piece=p.piece, surface=p.surface, |
| begin=p.begin, end=p.end, byte_fallback=tokenizer.sp_model.is_byte(p.id))) |
| result = dict(title=case['title'], source_id=source_id, split=record['split'], |
| sample_start=case['source_start'], sample_end=case['source_start']+len(text), |
| text=text, decoded=decoded, characters=len(text), body_tokens=len(ids), |
| characters_per_token=len(text)/len(ids), unknown_tokens=ids.count(0), |
| byte_fallback_tokens=sum(t['byte_fallback'] for t in tokens), |
| newline_characters=text.count('\n'), roundtrip_exact=decoded == text, |
| hf_adapter_matches_sentencepiece=True, full_record_matches_saved_pretraining_stream=True, |
| full_record_tokens=len(full_ids), tokenizer_sha256=sha(tokenizer_path), |
| input_ids=ids, hf_default_ids=[1]+ids, pretraining_bos_eos_ids=[1]+ids+[2], tokens=tokens) |
| (output / 'inspection.json').write_text(json.dumps(result, ensure_ascii=False, indent=2) + '\n') |
| with (output / 'tokens.tsv').open('w', newline='') as f: |
| writer = csv.writer(f, delimiter='\t') |
| writer.writerow(['index', 'id', 'piece_json', 'surface_json', 'begin', 'end', 'byte_fallback']) |
| for t in tokens: |
| writer.writerow([t['index'], t['id'], json.dumps(t['piece'], ensure_ascii=False), |
| json.dumps(t['surface'], ensure_ascii=False), t['begin'], t['end'], t['byte_fallback']]) |
| lines = ['# Wikisourceトークナイズ出力', '', f"作品：{case['title']}", '', |
| f"source_id：`{source_id}`（validation）。モデルと同じ32,000語彙のtokenizerを使用。", '', |
| '## 原文', '', '```text', text, '```', '', '## トークン表記（本文のみ）', '', |
| '各pieceをJSON文字列で表示し、` | `で区切る。`\\n`は改行、`▁`はSentencePieceの空白表記。区切り線と引用符は表示用で入力には含まない。', '', |
| '```text', ' | '.join(json.dumps(t['piece'], ensure_ascii=False) for t in tokens), '```', '', |
| '## トークンID（本文のみ）', '', '```json', json.dumps(ids), '```', '', |
| '## 位置・ID・表記の対応', '', '| 順番 | ID | piece | 原文の文字位置 |', '|---:|---:|---|---|'] |
| for t in tokens: |
| piece = json.dumps(t['piece'], ensure_ascii=False).replace('|', '\\|') |
| lines.append(f"| {t['index']} | {t['id']} | `{piece}` | [{t['begin']}, {t['end']}) |") |
| lines += ['', '## 確認結果', '', |
| f"- 原文{len(text)}文字、本文{len(ids)}token、{len(text)/len(ids):.3f}文字/token。", |
| f"- UNK={result['unknown_tokens']}、byte fallback={result['byte_fallback_tokens']}、原文改行={text.count(chr(10))}。", |
| '- encode→decodeで原文と完全一致。HFアダプタとSentencePieceの本文IDも一致。', |
| f"- このサンプルを含む元レコード全体{len(full_ids)}tokenを再encodeし、保存済み事前学習用binと全IDが完全一致。token_sha256も一致。", |
| '- この表示のID列は本文のみ。事前学習データは文書全体にBOS ID 1とEOS ID 2を付ける。試用スクリプトの入力はBOSだけを付け、続きを生成する。', |
| '- 部分抜粋のtoken分割は切り出し境界の影響を受け得るため、保存済みbinとの照合は元レコード全体で行った。', |
| '- 1例での確認であり、全作品・外字・byte fallback・特殊文字の網羅検査や、語彙効率の最適性の証明ではない。', '', |
| '## デコード結果', '', '```text', decoded, '```', ''] |
| (output / 'INSPECTION.md').write_text('\n'.join(lines)) |
| print(json.dumps({k: v for k, v in result.items() if k not in ('tokens', 'hf_default_ids', 'pretraining_bos_eos_ids')}, ensure_ascii=False, indent=2)) |
| print('PIECES:', ' | '.join(json.dumps(t['piece'], ensure_ascii=False) for t in tokens)) |
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|
|
| if __name__ == '__main__': |
| main() |
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|
