File size: 5,399 Bytes
91ac3cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import numpy as np
from tqdm import tqdm
from transformers import AutoTokenizer
from kawk50m.cli import configure_utf8_stdio
from kawk50m.io import discover_paths, iter_documents
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Tokenize clean JSONL shards into flat uint16 streams."
)
parser.add_argument("--input-dir", default="data/clean")
parser.add_argument("--tokenizer-dir", default="artifacts/tokenizer-20k")
parser.add_argument("--output-dir", default="data/tokenized")
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--max-train-tokens", type=int, default=None)
parser.add_argument("--max-validation-tokens", type=int, default=None)
parser.add_argument("--max-test-tokens", type=int, default=None)
return parser.parse_args()
def tokenize_split(
*,
split: str,
paths: list[Path],
tokenizer: Any,
output_path: Path,
batch_size: int,
max_tokens: int | None,
) -> dict[str, Any]:
documents_seen = 0
documents_written = 0
token_count = 0
text_batch: list[str] = []
def flush(handle: Any) -> bool:
nonlocal documents_written, token_count
if not text_batch:
return False
encoded_batch = tokenizer(
text_batch,
add_special_tokens=False,
return_attention_mask=False,
return_token_type_ids=False,
)["input_ids"]
reached_limit = False
for token_ids in encoded_batch:
tokens = list(token_ids)
if not tokens or tokens[-1] != tokenizer.eos_token_id:
tokens.append(tokenizer.eos_token_id)
if max_tokens is not None:
remaining = max_tokens - token_count
if remaining <= 0:
reached_limit = True
break
if len(tokens) > remaining:
tokens = tokens[:remaining]
reached_limit = True
if tokens:
np.asarray(tokens, dtype=np.uint16).tofile(handle)
token_count += len(tokens)
documents_written += 1
if reached_limit:
break
text_batch.clear()
return reached_limit
with output_path.open("wb") as handle:
progress = tqdm(iter_documents(paths), desc=split, unit="docs")
for document in progress:
documents_seen += 1
text_batch.append(document["text"])
if len(text_batch) >= batch_size and flush(handle):
break
else:
flush(handle)
return {
"split": split,
"output": str(output_path),
"input_files": len(paths),
"documents_seen": documents_seen,
"documents_written": documents_written,
"tokens": token_count,
"bytes": output_path.stat().st_size,
"dtype": "uint16",
}
def main() -> None:
configure_utf8_stdio()
args = parse_args()
if args.batch_size <= 0:
raise ValueError("--batch-size must be positive")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
existing = [
*(output_dir / f"{split}.bin" for split in ("train", "validation", "test")),
output_dir / "metadata.json",
]
existing = [path for path in existing if path.exists()]
if existing:
raise FileExistsError(
f"{existing[0]} already exists. Choose a new output directory."
)
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_dir, use_fast=False, legacy=True
)
tokenizer.model_max_length = int(1e30)
if len(tokenizer) > np.iinfo(np.uint16).max:
raise ValueError("uint16 output requires a vocabulary no larger than 65,535")
if tokenizer.eos_token_id is None:
raise ValueError("Tokenizer must define eos_token_id")
input_dir = Path(args.input_dir)
limits = {
"train": args.max_train_tokens,
"validation": args.max_validation_tokens,
"test": args.max_test_tokens,
}
summaries: dict[str, Any] = {
"tokenizer": str(Path(args.tokenizer_dir).resolve()),
"vocab_size": len(tokenizer),
"eos_token_id": tokenizer.eos_token_id,
"splits": {},
}
for split in ("train", "validation", "test"):
paths = discover_paths([str(input_dir / f"{split}-*.jsonl")])
if not paths:
print(f"Skipping {split}: no input shards")
continue
summary = tokenize_split(
split=split,
paths=paths,
tokenizer=tokenizer,
output_path=output_dir / f"{split}.bin",
batch_size=args.batch_size,
max_tokens=limits[split],
)
summaries["splits"][split] = summary
if "train" not in summaries["splits"]:
raise FileNotFoundError("No train shards were found")
metadata_path = output_dir / "metadata.json"
with metadata_path.open("w", encoding="utf-8") as handle:
json.dump(summaries, handle, ensure_ascii=False, indent=2)
handle.write("\n")
print(json.dumps(summaries, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
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