File size: 2,378 Bytes
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import os
import numpy as np
from tqdm import tqdm
from datasets import load_dataset
from transformers import LlamaTokenizerFast
num_proc = 4
block_size = 1024
if __name__ == "__main__":
# features: ['text', 'id', 'dump', 'url', 'date', 'file_path', 'language', 'language_score', 'token_count']
ds = load_dataset("HuggingFaceFW/fineweb", name="sample-10BT", num_proc=num_proc)
split_ds = ds["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
split_ds['val'] = split_ds.pop('test')
tokenizer = LlamaTokenizerFast.from_pretrained(
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
pad_token="<s>",
model_max_length=None,
add_bos_token=False,
add_eos_token=True,
)
# tokenize the dataset.
# it does the truncation, padding, and overlowing
# into a new sequence with it's own bos and eos token for us.
def process(example):
ids = tokenizer(
example['text'],
truncation=False,
return_length=True,
padding=False,
return_tensors='pt',
)['input_ids'][0]
out = {'ids': ids, 'len': len(ids)}
return out
tokenized = split_ds.map(
process,
remove_columns=ds['train'].column_names,
desc="tokenizing the splits",
num_proc=num_proc,
)
# concatenate all the ids in each dataset into one large file we can use for training
for split, dset in tokenized.items():
arr_len = np.sum(dset['len'], dtype=np.uint64)
filename = os.path.join(os.path.dirname(__file__), f'{split}.bin')
dtype = np.uint16 # (can do since vocab_size == 32000 is < 2**16)
arr = np.memmap(filename, dtype=dtype, mode='w+', shape=(arr_len,))
total_batches = 1024
idx = 0
for batch_idx in tqdm(range(total_batches), desc=f'writing {filename}'):
# Batch together samples for faster write
batch = dset.shard(num_shards=total_batches, index=batch_idx, contiguous=True).with_format('numpy')
arr_batch = np.concatenate(batch['ids'], axis=0)
# Write into mmap
arr[idx : idx + len(arr_batch)] = arr_batch
idx += len(arr_batch)
arr.flush()
# train.bin is ~22GB, val.bin ~8.5MB
# train has ~11B tokens (11,774,429,883)
# val has ~5.9M tokens (5,908,112) |