88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
import torch
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from datasets import Dataset, load_from_disk
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class ConstantLengthDataset:
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"""
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Iterable dataset that returns constant length chunks of tokens from stream of text files.
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Args:
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tokenizer (Tokenizer): The processor used for proccessing the data.
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dataset (dataset.Dataset): Dataset with text files.
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infinite (bool): If True the iterator is reset after dataset reaches end else stops.
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seq_length (int): Length of token sequences to return.
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num_of_sequences (int): Number of token sequences to keep in buffer.
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chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
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"""
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def __init__(
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self,
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tokenizer,
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dataset,
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infinite=False,
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seq_length=1024,
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num_of_sequences=1024,
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chars_per_token=3.6,
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content_field="content",
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):
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self.tokenizer = tokenizer
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self.concat_token_id = tokenizer.eos_token_id
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self.dataset = dataset
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self.seq_length = seq_length
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self.infinite = infinite
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self.current_size = 0
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self.max_buffer_size = seq_length * chars_per_token * num_of_sequences
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self.content_field = content_field
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def __call__(self):
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def gen():
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for x in self:
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yield x
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return gen()
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def __iter__(self):
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for buffer in self._read_dataset_into_buffer():
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yield from self._tokenize(buffer)
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def _tokenize(self, buffer):
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tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
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all_token_ids = []
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for tokenized_input in tokenized_inputs:
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all_token_ids.extend(tokenized_input + [self.concat_token_id])
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for i in range(0, len(all_token_ids), self.seq_length):
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input_ids = all_token_ids[i : i + self.seq_length]
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if len(input_ids) < self.seq_length:
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input_ids = all_token_ids[-self.seq_length :]
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if len(input_ids) == self.seq_length:
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self.current_size += 1
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yield dict(input_ids=input_ids, labels=input_ids)
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def _read_dataset_into_buffer(self):
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iterator = iter(self.dataset)
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more_examples = True
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while more_examples:
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buffer, buffer_len = [], 0
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while True:
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if buffer_len >= self.max_buffer_size:
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break
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try:
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buffer.append(next(iterator)[self.content_field])
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buffer_len += len(buffer[-1])
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except StopIteration:
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if self.infinite:
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iterator = iter(self.dataset)
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else:
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more_examples = False
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break
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yield buffer
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def load_dataset(tokenizer, filepath, **kwargs):
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ds = load_from_disk(filepath)
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ds = Dataset.from_generator(ConstantLengthDataset(tokenizer, ds, **kwargs))
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return ds
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