wehub-resource-sync
125 lines
6.1 KiB
Python
125 lines
6.1 KiB
Python
import torch
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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from transformers import BatchEncoding, PreTrainedTokenizerBase
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from transformers.data.data_collator import (
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DataCollatorMixin,
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_torch_collate_batch,
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)
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from transformers.file_utils import PaddingStrategy
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from typing import NewType
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InputDataClass = NewType("InputDataClass", Any)
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def pre_calc_rel_mat(segment_ids):
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valid_span = torch.zeros((segment_ids.shape[0], segment_ids.shape[1], segment_ids.shape[1]),
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device=segment_ids.device, dtype=torch.bool)
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for i in range(segment_ids.shape[0]):
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for j in range(segment_ids.shape[1]):
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valid_span[i, j, :] = segment_ids[i, :] == segment_ids[i, j]
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return valid_span
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@dataclass
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class DataCollatorForKeyValueExtraction(DataCollatorMixin):
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"""
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Data collator that will dynamically pad the inputs received, as well as the labels.
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Args:
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tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
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The tokenizer used for encoding the data.
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padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
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maximum acceptable input length for the model if that argument is not provided.
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* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
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different lengths).
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max_length (:obj:`int`, `optional`):
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Maximum length of the returned list and optionally padding length (see above).
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
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The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
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"""
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tokenizer: PreTrainedTokenizerBase
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padding: Union[bool, str, PaddingStrategy] = True
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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label_pad_token_id: int = -100
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def __call__(self, features):
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label_name = "label" if "label" in features[0].keys() else "labels"
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labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
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images = None
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if "images" in features[0]:
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images = torch.stack([torch.tensor(d.pop("images")) for d in features])
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IMAGE_LEN = int(images.shape[-1] / 16) * int(images.shape[-1] / 16) + 1
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batch = self.tokenizer.pad(
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features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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# Conversion to tensors will fail if we have labels as they are not of the same length yet.
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return_tensors="pt" if labels is None else None,
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)
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if images is not None:
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batch["images"] = images
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batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) and k == 'attention_mask' else v
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for k, v in batch.items()}
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visual_attention_mask = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long)
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batch["attention_mask"] = torch.cat([batch['attention_mask'], visual_attention_mask], dim=1)
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if labels is None:
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return batch
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has_bbox_input = "bbox" in features[0]
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has_position_input = "position_ids" in features[0]
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padding_idx=self.tokenizer.pad_token_id
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sequence_length = torch.tensor(batch["input_ids"]).shape[1]
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padding_side = self.tokenizer.padding_side
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if padding_side == "right":
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batch["labels"] = [label + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels]
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if has_bbox_input:
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batch["bbox"] = [bbox + [[0, 0, 0, 0]] * (sequence_length - len(bbox)) for bbox in batch["bbox"]]
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if has_position_input:
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batch["position_ids"] = [position_id + [padding_idx] * (sequence_length - len(position_id))
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for position_id in batch["position_ids"]]
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else:
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batch["labels"] = [[self.label_pad_token_id] * (sequence_length - len(label)) + label for label in labels]
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if has_bbox_input:
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batch["bbox"] = [[[0, 0, 0, 0]] * (sequence_length - len(bbox)) + bbox for bbox in batch["bbox"]]
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if has_position_input:
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batch["position_ids"] = [[padding_idx] * (sequence_length - len(position_id))
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+ position_id for position_id in batch["position_ids"]]
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if 'segment_ids' in batch:
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assert 'position_ids' in batch
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for i in range(len(batch['segment_ids'])):
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batch['segment_ids'][i] = batch['segment_ids'][i] + [batch['segment_ids'][i][-1] + 1] * (sequence_length - len(batch['segment_ids'][i])) + [
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batch['segment_ids'][i][-1] + 2] * IMAGE_LEN
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batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) else v for k, v in batch.items()}
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if 'segment_ids' in batch:
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valid_span = pre_calc_rel_mat(
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segment_ids=batch['segment_ids']
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)
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batch['valid_span'] = valid_span
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del batch['segment_ids']
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if images is not None:
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visual_labels = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long) * -100
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batch["labels"] = torch.cat([batch['labels'], visual_labels], dim=1)
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return batch
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