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from dataclasses import dataclass |
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from typing import Any, Dict, List, Union |
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import numpy as np |
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import torch |
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from transformers import PreTrainedTokenizer |
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@dataclass |
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class DataCollatorForLanguageModeling: |
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""" |
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Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they |
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are not all of the same length. |
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Args: |
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tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]): |
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The tokenizer used for encoding the data. |
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varlen (`bool`): |
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Whether to return sequences with variable lengths. |
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If `True`, the offsets indicating the start and end of each sequence will be returned. |
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For example, if the sequence lengths are `[4, 8, 12]`, |
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the returned `input_ids` will be a long flattened tensor of shape `[1, 24]`, with `offsets` being `[0, 4, 12, 24]`. |
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If `False`, the `input_ids` with shape `[batch_size, seq_len]` will be returned directly. |
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return_tensors (`str`): |
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The type of Tensor to return. Allowable values are "pt". |
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<Tip> |
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For best performance, this data collator should be used with a dataset having items that are dictionaries or |
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BatchEncoding, with the `"special_tokens_mask"` key, as returned by a [`PreTrainedTokenizer`] or a |
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[`PreTrainedTokenizerFast`] with the argument `return_special_tokens_mask=True`. |
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</Tip>""" |
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tokenizer: PreTrainedTokenizer |
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varlen: bool = False |
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return_tensors: str = "pt" |
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def __call__( |
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self, |
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examples: List[Union[List[int], Dict[str, Any]]] |
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) -> Dict[str, Any]: |
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if not isinstance(examples[0], Dict): |
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examples = [{'input_ids': x} for x in examples] |
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if isinstance(examples[0]['input_ids'], List): |
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examples = [{'input_ids': torch.tensor(x['input_ids'], dtype=torch.long)} for x in examples] |
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elif isinstance(examples[0]['input_ids'], np.ndarray): |
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examples = [{'input_ids': torch.from_numpy(x['input_ids'])} for x in examples] |
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if not self.varlen: |
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length_of_first = examples[0]['input_ids'].size(0) |
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if all(x['input_ids'].size(0) == length_of_first for x in examples): |
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batch = {'input_ids': torch.stack([x['input_ids'] for x in examples], dim=0)} |
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else: |
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if self.tokenizer._pad_token is None: |
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raise ValueError( |
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f"You are attempting to pad samples but the tokenizer you are using " |
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f"({self.tokenizer.__class__.__name__}) does not have a pad token." |
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) |
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batch = self.tokenizer.pad(examples, return_tensors=self.return_tensors, return_attention_mask=False) |
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else: |
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batch = {'input_ids': torch.cat([x['input_ids'] for x in examples], dim=0).unsqueeze(0)} |
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if self.tokenizer.add_bos_token: |
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offsets = [] |
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if batch['input_ids'][0, 0] != self.tokenizer.bos_token_id: |
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offsets.append(torch.tensor([0], dtype=torch.long)) |
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offsets.append(torch.where(batch['input_ids'].eq(self.tokenizer.bos_token_id))[1]) |
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offsets.append(torch.tensor([len(batch['input_ids'][0])], dtype=torch.long)) |
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batch['offsets'] = torch.cat(offsets, dim=0) |
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elif self.tokenizer.add_eos_token: |
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offsets = [torch.tensor([0], dtype=torch.long)] |
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offsets.append(torch.where(batch['input_ids'].eq(self.tokenizer.eos_token_id))[1] + 1) |
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if batch['input_ids'][0, -1] != self.tokenizer.eos_token_id: |
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offsets.append(torch.tensor([len(batch['input_ids'][0])], dtype=torch.long)) |
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batch['offsets'] = torch.cat(offsets, dim=0) |
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else: |
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raise ValueError("You must allow the tokenizer to add either a bos or eos token as separators.") |
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labels = batch['input_ids'].clone() |
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if self.tokenizer.pad_token_id is not None: |
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labels[labels == self.tokenizer.pad_token_id] = -100 |
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batch["labels"] = labels |
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return batch |
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