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75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
# saves the openwebtext dataset to a binary file for training. following was helpful:
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# https://github.com/HazyResearch/flash-attention/blob/main/training/src/datamodules/language_modeling_hf.py
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import os
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from tqdm import tqdm
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import numpy as np
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import tiktoken
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from datasets import load_dataset # huggingface datasets
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# number of workers in .map() call
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# good number to use is ~order number of cpu cores // 2
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num_proc = 8
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# takes 54GB in huggingface .cache dir, about 8M documents (8,013,769)
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dataset = load_dataset("openwebtext")
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# owt by default only contains the 'train' split, so create a test split
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split_dataset = dataset["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
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split_dataset['val'] = split_dataset.pop('test') # rename the test split to val
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# this results in:
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# >>> split_dataset
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# DatasetDict({
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# train: Dataset({
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# features: ['text'],
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# num_rows: 8009762
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# })
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# val: Dataset({
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# features: ['text'],
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# num_rows: 4007
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# })
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# })
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# we now want to tokenize the dataset. first define the encoding function (gpt2 bpe)
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enc = tiktoken.get_encoding("gpt2")
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def process(example):
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ids = enc.encode_ordinary(example['text']) # encode_ordinary ignores any special tokens
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ids.append(enc.eot_token) # add the end of text token, e.g. 50256 for gpt2 bpe
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# note: I think eot should be prepended not appended... hmm. it's called "eot" though...
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out = {'ids': ids, 'len': len(ids)}
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return out
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# tokenize the dataset
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tokenized = split_dataset.map(
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process,
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remove_columns=['text'],
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desc="tokenizing the splits",
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num_proc=num_proc,
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)
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# concatenate all the ids in each dataset into one large file we can use for training
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for split, dset in tokenized.items():
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arr_len = np.sum(dset['len'])
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filename = os.path.join(os.path.dirname(__file__), f'{split}.bin')
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dtype = np.uint16 # (can do since enc.max_token_value == 50256 is < 2**16)
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arr = np.memmap(filename, dtype=dtype, mode='w+', shape=(arr_len,))
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total_batches = 1024
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idx = 0
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for batch_idx in tqdm(range(total_batches), desc=f'writing {filename}'):
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# Batch together samples for faster write
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batch = dset.shard(num_shards=total_batches, index=batch_idx, contiguous=True).with_format('numpy')
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arr_batch = np.concatenate(batch['ids'])
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# Write into mmap
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arr[idx : idx + len(arr_batch)] = arr_batch
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idx += len(arr_batch)
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arr.flush()
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# train.bin is ~17GB, val.bin ~8.5MB
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# train has ~9B tokens (9,035,582,198)
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# val has ~4M tokens (4,434,897)
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# to read the bin files later, e.g. with numpy:
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# m = np.memmap('train.bin', dtype=np.uint16, mode='r')
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