File size: 5,793 Bytes
7cffee1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | import argparse
import hashlib
import json
import os
import numpy as np
import pyarrow.parquet as pq
from huggingface_hub import snapshot_download
from tokenizers import Tokenizer, decoders, models, pre_tokenizers, trainers
from transformers import PreTrainedTokenizerFast
DEFAULTS = {
"dataset": "SlayerLab/polish-dynaword",
"parquet": "data/european_hplt_v3_pl/european_hplt_v3_pl.parquet",
"out": "data/",
"vocab_size": 16384,
"eos_token": "<|eos|>",
"text_column": "text",
"seed": 1337,
}
def load_config(path):
cfg = dict(DEFAULTS)
if path and os.path.isfile(path):
with open(path) as f:
raw = json.load(f)
data = raw.get("data", {})
tok = raw.get("tokenizer", {})
if "dataset" in data:
cfg["dataset"] = data["dataset"]
if "parquet" in data:
cfg["parquet"] = data["parquet"]
if "out" in data:
cfg["out"] = data["out"]
if "vocab_size" in tok:
cfg["vocab_size"] = tok["vocab_size"]
if "eos_token" in tok:
cfg["eos_token"] = tok["eos_token"]
return cfg
def ensure_parquet(path, dataset, repo_root):
if os.path.isfile(path):
return path
snapshot_download(
repo_id=dataset,
repo_type="dataset",
allow_patterns=[path],
local_dir=repo_root,
)
if not os.path.isfile(path):
raise FileNotFoundError(path)
return path
def iter_documents(path, column, limit):
pf = pq.ParquetFile(path)
seen = 0
for batch in pf.iter_batches(batch_size=1000, columns=[column]):
for doc in batch.column(0).to_pylist():
if doc is None:
continue
yield doc
seen += 1
if limit is not None and seen >= limit:
return
def train_tokenizer(tokenizer_path, docs, vocab_size, eos_token):
tokenizer = Tokenizer(models.BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
tokenizer.decoder = decoders.ByteLevel()
trainer = trainers.BpeTrainer(
vocab_size=vocab_size,
special_tokens=[eos_token],
initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
seed=DEFAULTS["seed"],
)
tokenizer.train_from_iterator(docs, trainer=trainer)
tokenizer.save(tokenizer_path)
def encode_stream(path, docs, tokenizer, eos_id):
num_tokens = 0
buffer = np.empty(65536, dtype=np.uint16)
filled = 0
with open(path, "wb") as f:
for batch in batch_documents(docs, 1000):
encodings = tokenizer(batch, add_special_tokens=False)["input_ids"]
for ids in encodings:
ids.append(eos_id)
if filled + len(ids) > buffer.size:
f.write(buffer[:filled].tobytes())
filled = 0
if len(ids) > buffer.size:
arr = np.asarray(ids, dtype=np.uint16)
f.write(arr.tobytes())
num_tokens += arr.size
continue
buffer[filled : filled + len(ids)] = ids
filled += len(ids)
num_tokens += len(ids)
if filled:
f.write(buffer[:filled].tobytes())
return num_tokens
def batch_documents(docs, size):
batch = []
for doc in docs:
batch.append(doc)
if len(batch) == size:
yield batch
batch = []
if batch:
yield batch
def sha256_of(path):
h = hashlib.sha256()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(1 << 20), b""):
h.update(chunk)
return h.hexdigest()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="config.json")
parser.add_argument("--out", default=None)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--dataset", default=None)
parser.add_argument("--parquet", default=None)
parser.add_argument("--text-column", default=None)
args = parser.parse_args()
cfg = load_config(args.config)
if args.out is not None:
cfg["out"] = args.out
if args.dataset is not None:
cfg["dataset"] = args.dataset
if args.parquet is not None:
cfg["parquet"] = args.parquet
if args.text_column is not None:
cfg["text_column"] = args.text_column
repo_root = os.path.dirname(os.path.abspath(__file__))
out_dir = cfg["out"]
os.makedirs(out_dir, exist_ok=True)
parquet_path = ensure_parquet(cfg["parquet"], cfg["dataset"], repo_root)
tokenizer_path = os.path.join(out_dir, "tokenizer.json")
tokens_path = os.path.join(out_dir, "tokens.bin")
meta_path = os.path.join(out_dir, "meta.json")
if not os.path.isfile(tokenizer_path):
train_tokenizer(
tokenizer_path,
iter_documents(parquet_path, cfg["text_column"], args.limit),
cfg["vocab_size"],
cfg["eos_token"],
)
tokenizer = PreTrainedTokenizerFast(
tokenizer_file=tokenizer_path, eos_token=cfg["eos_token"]
)
tokenizer.save_pretrained(out_dir)
eos_id = tokenizer.convert_tokens_to_ids(cfg["eos_token"])
num_tokens = encode_stream(
tokens_path,
iter_documents(parquet_path, cfg["text_column"], args.limit),
tokenizer,
eos_id,
)
meta = {
"num_tokens": num_tokens,
"eos_id": eos_id,
"vocab_size": cfg["vocab_size"],
"sha256": sha256_of(tokens_path),
}
with open(meta_path, "w") as f:
json.dump(meta, f, indent=2)
print(f"num_tokens {num_tokens}")
print(f"sha256 {meta['sha256']}")
if __name__ == "__main__":
main()
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