File size: 4,566 Bytes
ffc0c0c | 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 | #!/usr/bin/env python3
"""
prepare_data.py
===============
Build tokenized binary files for training from Cosmopedia using streaming.
Outputs:
data/train.bin
data/val.bin
data/test.bin
Dataset:
HuggingFaceTB/cosmopedia (streaming)
"""
import os
from pathlib import Path
import numpy as np
import tiktoken
from datasets import load_dataset
from tqdm.auto import tqdm
# Local project cache for reproducibility and resume behavior.
os.environ.setdefault("HF_HOME", "./hf_cache")
os.environ.setdefault("HF_DATASETS_CACHE", "./hf_cache/datasets")
os.environ.setdefault("HF_HUB_CACHE", "./hf_cache/hub")
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
DATA_DIR = Path("data")
DATA_DIR.mkdir(parents=True, exist_ok=True)
CACHE_DIR = "./hf_cache"
DATASET_NAME = "HuggingFaceTB/cosmopedia"
DATASET_CONFIG = os.environ.get("DATASET_CONFIG", "stories")
# Stream only first N rows by default, matching your requested pattern.
MAX_EXAMPLES = int(os.environ.get("MAX_EXAMPLES", "1000000"))
# Deterministic split from one stream: 98% train, 1% val, 1% test.
TRAIN_FRAC = float(os.environ.get("TRAIN_FRAC", "0.98"))
VAL_FRAC = float(os.environ.get("VAL_FRAC", "0.01"))
# Flush chunks to disk to keep RAM bounded.
FLUSH_TOKENS = int(os.environ.get("FLUSH_TOKENS", "2000000"))
enc = tiktoken.get_encoding("gpt2")
EOT = enc.eot_token
def extract_text(row: dict) -> str:
"""Extract a usable text field across possible Cosmopedia schemas."""
if "text" in row and isinstance(row["text"], str):
return row["text"].strip()
if "content" in row and isinstance(row["content"], str):
return row["content"].strip()
parts = []
for key in ("prompt", "question", "instruction", "input", "answer", "response", "output"):
val = row.get(key)
if isinstance(val, str) and val.strip():
parts.append(val.strip())
return "\n\n".join(parts).strip()
def encode_text(text: str):
ids = enc.encode_ordinary(text)
ids.append(EOT)
return ids
def flush_tokens(fp, buffer_tokens):
if not buffer_tokens:
return 0
arr = np.asarray(buffer_tokens, dtype=np.uint16)
arr.tofile(fp)
n = int(arr.size)
buffer_tokens.clear()
return n
def pick_split(i: int, total: int) -> str:
train_cut = int(total * TRAIN_FRAC)
val_cut = train_cut + int(total * VAL_FRAC)
if i < train_cut:
return "train"
if i < val_cut:
return "val"
return "test"
if __name__ == "__main__":
print("Loading Cosmopedia (streaming)...")
# This follows your requested style while allowing MAX_EXAMPLES override.
dataset = load_dataset(
DATASET_NAME,
DATASET_CONFIG,
split="train",
streaming=True,
cache_dir=CACHE_DIR,
)
out_paths = {
"train": DATA_DIR / "train.bin",
"val": DATA_DIR / "val.bin",
"test": DATA_DIR / "test.bin",
}
for p in out_paths.values():
if p.exists():
p.unlink()
buffers = {"train": [], "val": [], "test": []}
counts_examples = {"train": 0, "val": 0, "test": 0}
counts_tokens = {"train": 0, "val": 0, "test": 0}
with open(out_paths["train"], "ab") as f_train, open(out_paths["val"], "ab") as f_val, open(out_paths["test"], "ab") as f_test:
fps = {"train": f_train, "val": f_val, "test": f_test}
progress = tqdm(total=MAX_EXAMPLES, desc="Streaming+Encoding", unit="doc")
for i, row in enumerate(dataset):
if i >= MAX_EXAMPLES:
break
text = extract_text(row)
if not text:
progress.update(1)
continue
split = pick_split(i, MAX_EXAMPLES)
toks = encode_text(text)
buffers[split].extend(toks)
counts_examples[split] += 1
# Flush all splits together so val/test are written even if their
# individual buffers never reach FLUSH_TOKENS (they're only 1% each).
if len(buffers["train"]) >= FLUSH_TOKENS:
for s in ("train", "val", "test"):
counts_tokens[s] += flush_tokens(fps[s], buffers[s])
progress.update(1)
progress.close()
for split in ("train", "val", "test"):
counts_tokens[split] += flush_tokens(fps[split], buffers[split])
print("\nDone.")
for split in ("train", "val", "test"):
print(f"{split:>5}: {counts_examples[split]:>10,} docs -> {counts_tokens[split]:>12,} tokens")
print(f"Saved files in: {DATA_DIR.resolve()}")
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