Clean tokenizer swap migration
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#!/usr/bin/env python3
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import argparse
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import gzip
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import json
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import re
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import time
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from collections import Counter
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from pathlib import Path
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from transformers import AutoTokenizer
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SCIENCE_KEYWORDS = re.compile(
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r"\b(physics|chemistry|biology|biological|medical|medicine|clinical|anatomy|physiology|genetics|ecology|astronomy|geology|neuroscience|experiment|hypothesis|laboratory|scientific|research|disease|protein|cell|molecule|atom|energy|force|gravity|electric|magnetic|quantum|planet|star|organism|evolution|climate|weather|ecosystem|bacteria|virus|vaccine|enzyme|hormone|blood|brain|heart|lung|kidney|cancer|therapy|diagnosis)\b",
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re.I,
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)
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def clean_text(text):
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if text is None:
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return ""
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text = str(text).replace("\x00", " ")
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text = re.sub(r"[ \t\r\f\v]+", " ", text)
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text = re.sub(r"\n{4,}", "\n\n\n", text)
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return text.strip()
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def first_user_assistant(messages):
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user = None
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assistant = None
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for msg in messages or []:
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role = msg.get("role")
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content = clean_text(msg.get("content"))
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if not content:
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continue
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if role == "user" and user is None:
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user = content
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elif role == "assistant" and user is not None:
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assistant = content
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break
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return user, assistant
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def qa_text(user, assistant):
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if not user or not assistant:
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return ""
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return f"Question:\n{user}\n\nAnswer:\n{assistant}"
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def extract_row_text(row):
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if isinstance(row, dict) and row.get("messages"):
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u, a = first_user_assistant(row.get("messages"))
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if u and a:
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return qa_text(u, a)
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for key in ("text", "content", "markdown", "raw_content"):
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if isinstance(row, dict):
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text = clean_text(row.get(key))
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if text:
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return text
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return ""
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def write_record(writer, stats, source, text, ntok, metadata=None):
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idx = stats["docs"]
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writer.write(json.dumps({
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"id": f"science_fix_{idx:09d}",
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"category": "science",
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"source": source,
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"text": text,
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"token_count": ntok,
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"metadata": metadata or {},
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}, ensure_ascii=False) + "\n")
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stats["docs"] += 1
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stats["tokens"] += ntok
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stats["tokens_by_source"][source] += ntok
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def copy_existing(existing, writer, target):
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tokens = docs = 0
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if not existing.exists() or existing.stat().st_size < 1024:
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return tokens, docs
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with gzip.open(existing, "rt", encoding="utf-8", errors="replace") as f:
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for line in f:
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if not line.strip() or tokens >= target:
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continue
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try:
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rec = json.loads(line)
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except json.JSONDecodeError:
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continue
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ntok = int(rec.get("token_count") or 0)
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if ntok <= 0:
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continue
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writer.write(json.dumps(rec, ensure_ascii=False) + "\n")
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tokens += ntok
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docs += 1
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return tokens, docs
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def add_jsonl(path, source, tok, writer, stats, args, capability_filter=None):
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with open(path, "r", encoding="utf-8", errors="replace") as f:
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for line in f:
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if stats["tokens"] >= args.target_tokens:
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break
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if not line.strip():
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continue
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try:
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row = json.loads(line)
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except json.JSONDecodeError:
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continue
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if capability_filter and row.get("capability") not in capability_filter:
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continue
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text = extract_row_text(row)
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if not text:
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stats["rejected"]["empty"] += 1
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continue
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ntok = len(tok.encode(text, add_special_tokens=False))
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if ntok < args.min_tokens:
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stats["rejected"]["too_short"] += 1
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continue
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if ntok > args.max_doc_tokens:
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stats["rejected"]["too_long"] += 1
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continue
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write_record(writer, stats, source, text, ntok, {k: row.get(k) for k in ("id", "capability", "source_id", "split") if k in row})
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if stats["docs"] % args.log_every == 0:
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print(json.dumps({"event": "progress", "docs": stats["docs"], "tokens": stats["tokens"], "target": args.target_tokens, "source": source, "elapsed_sec": time.time() - stats["started_at"]}, ensure_ascii=False), flush=True)
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def add_fineweb_edu_parquets(paths, tok, writer, stats, args):
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import pyarrow.parquet as pq
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for path in paths:
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if stats["tokens"] >= args.target_tokens:
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break
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source = f"fineweb_edu_science_supplement:{path.name}"
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before = stats["tokens"]
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pf = pq.ParquetFile(path)
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for batch in pf.iter_batches(batch_size=args.parquet_batch_size):
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if stats["tokens"] >= args.target_tokens:
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break
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for row in batch.to_pylist():
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if stats["tokens"] >= args.target_tokens:
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break
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text = extract_row_text(row)
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if not text or not SCIENCE_KEYWORDS.search(text[:12000]):
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stats["rejected"]["not_science_like"] += 1
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continue
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ntok = len(tok.encode(text, add_special_tokens=False))
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if ntok < args.min_tokens:
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stats["rejected"]["too_short"] += 1
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continue
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if ntok > args.max_doc_tokens:
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stats["rejected"]["too_long"] += 1
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continue
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write_record(writer, stats, source, text, ntok, {k: row.get(k) for k in ("url", "dump", "language", "language_score") if k in row})
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if stats["docs"] % args.log_every == 0:
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print(json.dumps({"event": "progress", "docs": stats["docs"], "tokens": stats["tokens"], "target": args.target_tokens, "source": source, "elapsed_sec": time.time() - stats["started_at"]}, ensure_ascii=False), flush=True)
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print(json.dumps({"event": "source_done", "source": source, "tokens_added": stats["tokens"] - before, "total_tokens": stats["tokens"]}, ensure_ascii=False), flush=True)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--tokenizer", default="model_building/generated_models/Qwen3-0.6B-DSV4-tokenizer-remap-v2")
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ap.add_argument("--out-dir", default="data/cpt_docmix_5b_sources_8192_20260614")
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ap.add_argument("--target-tokens", type=int, default=150_000_000)
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ap.add_argument("--min-tokens", type=int, default=128)
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ap.add_argument("--max-doc-tokens", type=int, default=32768)
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ap.add_argument("--log-every", type=int, default=5000)
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ap.add_argument("--parquet-batch-size", type=int, default=1000)
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args = ap.parse_args()
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base = Path.cwd()
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out_dir = base / args.out_dir
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doc_dir = out_dir / "documents"
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final_out = doc_dir / "science.jsonl.gz"
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tmp_out = doc_dir / "science.jsonl.gz.tmp"
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if tmp_out.exists():
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tmp_out.unlink()
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tok = AutoTokenizer.from_pretrained(base / args.tokenizer, trust_remote_code=True)
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stats = {"target_tokens": args.target_tokens, "tokens": 0, "docs": 0, "tokens_by_source": Counter(), "rejected": Counter(), "started_at": time.time(), "sources": []}
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with gzip.open(tmp_out, "wt", encoding="utf-8") as writer:
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t, d = copy_existing(final_out, writer, args.target_tokens)
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stats["tokens"] += t; stats["docs"] += d; stats["tokens_by_source"]["existing_science_docmix"] += t
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print(json.dumps({"event": "copied_existing", "docs": d, "tokens": t}, ensure_ascii=False), flush=True)
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mix = base / "data/training_mix_v4_train1m_test2p8k_noupsample_nobbh_20260611/train_1m.jsonl"
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if mix.exists() and stats["tokens"] < args.target_tokens:
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before = stats["tokens"]
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add_jsonl(mix, "training_mix_v4_science_logic", tok, writer, stats, args, {"science_reasoning", "logic"})
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stats["sources"].append({"source": str(mix), "tokens": stats["tokens"] - before})
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paths = sorted((base / "data/raw_parquets/fineweb_edu").glob("*.parquet"))
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if paths and stats["tokens"] < args.target_tokens:
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add_fineweb_edu_parquets(paths, tok, writer, stats, args)
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if stats["tokens"] < args.target_tokens:
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raise SystemExit(f"only collected {stats['tokens']} / {args.target_tokens} tokens")
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if final_out.exists():
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final_out.replace(final_out.with_suffix(".jsonl.gz.underfilled_20260614"))
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tmp_out.replace(final_out)
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stats["elapsed_sec"] = time.time() - stats["started_at"]
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stats["tokens_by_source"] = dict(stats["tokens_by_source"])
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stats["rejected"] = dict(stats["rejected"])
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(out_dir / "science_5b_fix_stats.json").write_text(json.dumps(stats, ensure_ascii=False, indent=2), encoding="utf-8")
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(out_dir / ".science_5b_ready").write_text(json.dumps({"tokens": stats["tokens"], "docs": stats["docs"], "elapsed_sec": stats["elapsed_sec"]}, ensure_ascii=False), encoding="utf-8")
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print(json.dumps({"event": "done", "tokens": stats["tokens"], "docs": stats["docs"], "elapsed_sec": stats["elapsed_sec"], "output": str(final_out)}, ensure_ascii=False), flush=True)
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if __name__ == "__main__":
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main()
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