| import torch | |
| import torch.nn as nn | |
| from torchcrf import CRF | |
| from transformers import BertPreTrainedModel, BertModel, BertForTokenClassification | |
| from transformers.modeling_outputs import TokenClassifierOutput | |
| class BertCrfForTokenClassification(BertPreTrainedModel): | |
| _keys_to_ignore_on_load_unexpected = [r"pooler"] | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.config = config | |
| self.bert = BertModel(config, add_pooling_layer=False) | |
| self.dropout = nn.Dropout( | |
| config.classifier_dropout | |
| if config.classifier_dropout is not None | |
| else config.hidden_dropout_prob | |
| ) | |
| self.classifier = nn.Linear(config.hidden_size, config.num_labels) | |
| self.crf = CRF(config.num_labels, batch_first=True) | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_ids=None, | |
| attention_mask=None, | |
| token_type_ids=None, | |
| position_ids=None, | |
| head_mask=None, | |
| inputs_embeds=None, | |
| labels=None, | |
| output_attentions=None, | |
| output_hidden_states=None, | |
| return_dict=None, | |
| ): | |
| return_dict = ( | |
| return_dict if return_dict is not None else self.config.use_return_dict | |
| ) | |
| outputs = self.bert( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| token_type_ids=token_type_ids, | |
| position_ids=position_ids, | |
| head_mask=head_mask, | |
| inputs_embeds=inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| sequence_output = outputs[0] | |
| sequence_output = self.dropout(sequence_output) | |
| logits = self.classifier(sequence_output) | |
| dummy_logits = torch.zeros_like(logits).to(logits.device) | |
| valid_lens = attention_mask.sum(dim=1) - 2 | |
| logits = logits[:, 1:] | |
| labels_mask = torch.arange(logits.size(1)).to( | |
| valid_lens.device | |
| ) < valid_lens.unsqueeze(1) | |
| seq_label_ids = self.crf.decode(logits, mask=labels_mask) | |
| loss = None | |
| if labels is not None: | |
| labels = labels[:, 1:] | |
| is_pad = labels == -100 | |
| labels.masked_fill_(is_pad, 0) | |
| assert torch.eq(~is_pad, labels_mask).all().item(), "mask assertion failed " | |
| loss = -self.crf(logits, labels, mask=labels_mask, reduction="token_mean") | |
| padded_list = torch.nn.utils.rnn.pad_sequence( | |
| [torch.tensor(lst) for lst in seq_label_ids], | |
| batch_first=True, | |
| padding_value=0, | |
| ) | |
| padded_list = torch.nn.functional.pad( | |
| padded_list, (0, logits.size(1) - padded_list.shape[1]) | |
| ) | |
| padded_list = torch.nn.functional.one_hot( | |
| padded_list, num_classes=logits.size(2) | |
| ) | |
| assert dummy_logits.size(1) == padded_list.size(1) + 1, "size assertion failed" | |
| dummy_logits[:, 1:] = padded_list | |
| if not return_dict: | |
| output = (dummy_logits,) + outputs[2:] | |
| return ((loss,) + output) if loss is not None else output | |
| return TokenClassifierOutput( | |
| loss=loss, | |
| logits=dummy_logits, | |
| hidden_states=outputs.hidden_states, | |
| attentions=outputs.attentions, | |
| ) | |