File size: 5,710 Bytes
fd87777 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """
Corpus'u tokenize edip nanoGPT formatinda .bin olarak kaydet.
HIZLANDIRMA:
- tokenizer.encode_batch (Rust + multi-threaded, single encode'dan ~5-8x hizli)
- Dosyaya inkremental yazma (RAM'de tum array tutulmuyor)
- Buyuk batch (5000 satir) — ic icine girmeden ust uste tokenize
Cikti:
data/train.bin (uint16 token id'leri)
data/val.bin
data/meta.pkl
"""
import argparse
import pickle
import time
from pathlib import Path
import numpy as np
from tokenizers import Tokenizer
from tqdm import tqdm
DATA_DIR = Path(__file__).parent / "data"
def encode_file(tokenizer, in_path: Path, out_path: Path, eot_id: int,
batch_size: int = 5000, dtype=np.uint16):
print(f"\n{in_path.name} -> {out_path.name}")
t0 = time.time()
# Satir sayisini once say (progress bar icin)
print(" satir sayiliyor...", end=" ", flush=True)
n_lines = 0
with open(in_path, "r", encoding="utf-8") as f:
for _ in f:
n_lines += 1
print(f"{n_lines:,}")
total_tokens = 0
# Append modunda binary yaz — RAM'de tum array tutmuyoruz
out_path.unlink(missing_ok=True)
with open(in_path, "r", encoding="utf-8") as f, \
open(out_path, "ab", buffering=1024*1024) as out_f:
pbar = tqdm(total=n_lines, desc="tokenize", smoothing=0.05)
batch = []
def flush(batch):
if not batch:
return 0
# encode_batch Rust + multi-threaded, ic icine birden fazla cumle alir
encs = tokenizer.encode_batch(batch)
# Tum id'leri ve EOT'leri tek bir array'de birlestir
all_ids = []
for enc in encs:
all_ids.extend(enc.ids)
all_ids.append(eot_id)
arr = np.array(all_ids, dtype=dtype)
out_f.write(arr.tobytes())
return len(arr)
for line in f:
line = line.strip()
if not line:
pbar.update(1)
continue
batch.append(line)
if len(batch) >= batch_size:
total_tokens += flush(batch)
pbar.update(len(batch))
pbar.set_postfix(tokens=f"{total_tokens/1e6:.1f}M")
batch.clear()
# Son kalan
if batch:
total_tokens += flush(batch)
pbar.update(len(batch))
pbar.close()
elapsed = time.time() - t0
size_mb = out_path.stat().st_size / 1e6
speed = total_tokens / elapsed / 1e6
print(f" [OK] {total_tokens:,} token, {size_mb:.1f} MB, "
f"{elapsed:.1f}s ({speed:.2f}M token/s)")
return total_tokens
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--tokenizer", type=str,
default=str(DATA_DIR / "tokenizer-tr-16k.json"))
parser.add_argument("--train-in", type=str,
default=str(DATA_DIR / "corpus_train_v3.txt"))
parser.add_argument("--val-in", type=str, default=None,
help="Val corpus (yoksa val atlanir)")
parser.add_argument("--train-out", type=str, default=None,
help="Train .bin cikti yolu (yoksa data/train.bin)")
parser.add_argument("--val-out", type=str, default=None,
help="Val .bin cikti yolu (yoksa data/val.bin)")
parser.add_argument("--meta-out", type=str, default=None,
help="Meta pickle yolu (yoksa data/meta.pkl)")
parser.add_argument("--batch-size", type=int, default=5000)
args = parser.parse_args()
# DATA_DIR yoksa oluştur (Lightning AI fresh env)
DATA_DIR.mkdir(parents=True, exist_ok=True)
train_out = Path(args.train_out) if args.train_out else (DATA_DIR / "train.bin")
val_out = Path(args.val_out) if args.val_out else (DATA_DIR / "val.bin")
meta_out = Path(args.meta_out) if args.meta_out else (DATA_DIR / "meta.pkl")
train_out.parent.mkdir(parents=True, exist_ok=True)
val_out.parent.mkdir(parents=True, exist_ok=True)
tokenizer = Tokenizer.from_file(args.tokenizer)
vocab_size = tokenizer.get_vocab_size()
eot_id = tokenizer.token_to_id("<|endoftext|>")
print(f"Vocab: {vocab_size} EOT id: {eot_id} Batch: {args.batch_size}")
print(f"Train: {args.train_in} -> {train_out}")
if args.val_in:
print(f"Val: {args.val_in} -> {val_out}")
else:
print(f"Val: atlandi")
if vocab_size > 65535:
raise ValueError("Vocab 65535'ten buyuk, uint16 yetmez. uint32 kullan.")
in_path = Path(args.train_in)
if not in_path.exists():
raise FileNotFoundError(f"Train input yok: {in_path}")
train_tokens = encode_file(tokenizer, in_path, train_out, eot_id,
batch_size=args.batch_size)
val_tokens = 0
if args.val_in:
val_in = Path(args.val_in)
if not val_in.exists():
print(f"UYARI: Val input yok ({val_in}), atlandi")
else:
val_tokens = encode_file(tokenizer, val_in, val_out, eot_id,
batch_size=args.batch_size)
meta = {
"vocab_size": vocab_size,
"eot_id": eot_id,
"tokenizer_path": args.tokenizer,
"train_tokens": train_tokens,
"val_tokens": val_tokens,
"train_out": str(train_out),
"val_out": str(val_out) if val_tokens else None,
}
with open(meta_out, "wb") as f:
pickle.dump(meta, f)
print(f"\n[OK] Hazir.")
print(f" Train: {train_tokens:,} token -> {train_out}")
if val_tokens:
print(f" Val: {val_tokens:,} token -> {val_out}")
print(f" Meta: {meta_out}")
if __name__ == "__main__":
main()
|