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Phase 1B: Encode raw corpus into binary token shards.
Input:
Raw .txt files
tokenizer.json
tokenizer_config.json
Output:
train.bin
val.bin
train.meta.json
val.meta.json
Each line is treated as one document.
For a cleaner corpus, prepare one paragraph/document per line.
"""
import argparse
import random
from pathlib import Path
import os
import sys
# Add project root to python path so 'src' can be imported
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
import numpy as np
from tokenizers import Tokenizer
from tqdm import tqdm
from src.common.utils import ensure_dir, load_json, save_json
def collect_text_files(input_dir: str) -> list[Path]:
"""
Input:
input_dir: Folder containing .txt files.
Output:
List of Path objects.
"""
files = sorted(Path(input_dir).rglob("*.txt"))
if not files:
raise FileNotFoundError(f"No .txt files found in {input_dir}")
return files
def choose_dtype(vocab_size: int):
"""
Input:
vocab_size: Tokenizer vocabulary size.
Output:
uint16 if possible, otherwise uint32.
"""
if vocab_size <= 65535:
return np.uint16
return np.uint32
def encode_and_write(
input_dir: str,
tokenizer_path: str,
tokenizer_config_path: str,
output_dir: str,
val_fraction: float,
seed: int,
batch_size: int,
) -> None:
"""
Input:
input_dir: Raw corpus folder.
tokenizer_path: Path to tokenizer.json.
tokenizer_config_path: Path to tokenizer_config.json.
output_dir: Destination for .bin files.
val_fraction: Fraction of lines for validation.
seed: Random split seed.
batch_size: Number of lines encoded at once.
Output:
train.bin / val.bin and metadata.
"""
ensure_dir(output_dir)
tokenizer = Tokenizer.from_file(tokenizer_path)
tok_cfg = load_json(tokenizer_config_path)
vocab_size = tok_cfg["vocab_size"]
eos_id = tok_cfg["special_token_ids"]["eos_token"]
dtype = choose_dtype(vocab_size)
train_path = Path(output_dir) / "train.bin"
val_path = Path(output_dir) / "val.bin"
# Remove old files to avoid appending to previous runs.
train_path.unlink(missing_ok=True)
val_path.unlink(missing_ok=True)
rng = random.Random(seed)
files = collect_text_files(input_dir)
train_tokens = 0
val_tokens = 0
train_docs = 0
val_docs = 0
def flush_lines(lines: list[str]) -> None:
nonlocal train_tokens, val_tokens, train_docs, val_docs
if not lines:
return
encodings = tokenizer.encode_batch(lines)
with open(train_path, "ab") as train_f, open(val_path, "ab") as val_f:
for enc in encodings:
ids = enc.ids + [eos_id]
arr = np.asarray(ids, dtype=dtype)
if rng.random() < val_fraction:
arr.tofile(val_f)
val_tokens += len(arr)
val_docs += 1
else:
arr.tofile(train_f)
train_tokens += len(arr)
train_docs += 1
buffer = []
for file in tqdm(files, desc="Encoding files"):
with open(file, "r", encoding="utf-8", errors="ignore") as f:
for line in f:
line = line.strip()
if not line:
continue
buffer.append(line)
if len(buffer) >= batch_size:
flush_lines(buffer)
buffer = []
flush_lines(buffer)
train_meta = {
"path": str(train_path),
"dtype": np.dtype(dtype).name,
"n_tokens": train_tokens,
"n_docs": train_docs,
"vocab_size": vocab_size,
}
val_meta = {
"path": str(val_path),
"dtype": np.dtype(dtype).name,
"n_tokens": val_tokens,
"n_docs": val_docs,
"vocab_size": vocab_size,
}
save_json(train_meta, Path(output_dir) / "train.meta.json")
save_json(val_meta, Path(output_dir) / "val.meta.json")
print("Encoding complete.")
print(train_meta)
print(val_meta)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", type=str, required=True)
parser.add_argument("--tokenizer_path", type=str, required=True)
parser.add_argument("--tokenizer_config_path", type=str, required=True)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--val_fraction", type=float, default=0.001)
parser.add_argument("--seed", type=int, default=1337)
parser.add_argument("--batch_size", type=int, default=1024)
args = parser.parse_args()
encode_and_write(
input_dir=args.input_dir,
tokenizer_path=args.tokenizer_path,
tokenizer_config_path=args.tokenizer_config_path,
output_dir=args.output_dir,
val_fraction=args.val_fraction,
seed=args.seed,
batch_size=args.batch_size,
)
if __name__ == "__main__":
main()
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