| from typing import Optional, Tuple, Union |
|
|
| import torch |
| from torch import nn |
| from torch.nn import CrossEntropyLoss, BCEWithLogitsLoss, MSELoss |
| from transformers import BertPreTrainedModel, BertModel, BertForSequenceClassification |
| from transformers.modeling_outputs import SequenceClassifierOutput |
|
|
| from trc_model.temporal_relation_classification_config import TemporalRelationClassificationConfig |
|
|
|
|
| class TokenPooler(nn.Module): |
| def __init__(self, config): |
| super().__init__() |
| self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
| self.activation = nn.Tanh() |
|
|
| def forward(self, token_tensor: torch.Tensor) -> torch.Tensor: |
| |
| |
| pooled_output = self.dense(token_tensor) |
| pooled_output = self.activation(pooled_output) |
| return pooled_output |
|
|
|
|
| class TemporalRelationClassification(BertForSequenceClassification): |
| config_class = TemporalRelationClassificationConfig |
|
|
| def __init__(self, config): |
| super().__init__(config) |
| self.num_labels = config.num_labels |
| self.special_markers = config.special_markers |
| self.pool_tokens = config.pool_tokens |
| self.ES_ID = config.ES_ID |
| self.EMS1 = config.EMS1 |
| self.EMS2 = config.EMS2 |
| self.architecture = config.architecture |
| self.config = config |
|
|
| self.bert = BertModel.from_pretrained(config.base_lm) |
| if self.bert.config.vocab_size != config.vocab_size: |
| self.bert.resize_token_embeddings(config.vocab_size) |
|
|
| classifier_dropout = ( |
| config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob |
| ) |
| if config.pool_tokens: |
| self.ems_1_pooler = TokenPooler(config) |
| self.ems_2_pooler = TokenPooler(config) |
| self.e_1_pooler = TokenPooler(config) |
| self.e_2_pooler = TokenPooler(config) |
|
|
| self.dropout = nn.Dropout(classifier_dropout) |
|
|
| self.classification_layers = None |
| if self.architecture == 'SEQ_CLS': |
| self.classification_layers = nn.Sequential( |
| nn.Linear(config.hidden_size, config.num_labels) |
| ) |
| if self.architecture == 'EMP': |
| self.e_1_linear = nn.Linear(config.hidden_size * 2, config.hidden_size) |
| self.e_2_linear = nn.Linear(config.hidden_size * 2, config.hidden_size) |
|
|
| if self.architecture in ['ESS', 'EF', 'EMP']: |
| self.classification_layers = nn.Sequential( |
| nn.Linear(config.hidden_size * 2, config.hidden_size), |
| nn.Linear(config.hidden_size, config.num_labels) |
| ) |
|
|
| |
| |
|
|
| def _get_entities_and_start_markers_indices(self, input_ids): |
| if not self.special_markers: |
| event_1_start, event_2_start = torch.tensor( |
| [(ids == self.ES_ID).nonzero().squeeze().tolist() for ids in input_ids]).T |
| return event_1_start, event_1_start + 1, event_2_start, event_2_start + 1 |
|
|
| em1_s = torch.tensor([(ids == self.EMS1).nonzero().item() for ids in input_ids], device=self.device) |
| entity_1 = em1_s + 1 |
|
|
| em2_s = torch.tensor([(ids == self.EMS2).nonzero().item() for ids in input_ids], device=self.device) |
| entity_2 = em2_s + 1 |
| return em1_s, entity_1, em2_s, entity_2 |
|
|
| def forward( |
| self, |
| input_ids: Optional[torch.Tensor] = None, |
| attention_mask: Optional[torch.Tensor] = None, |
| token_type_ids: Optional[torch.Tensor] = None, |
| position_ids: Optional[torch.Tensor] = None, |
| head_mask: Optional[torch.Tensor] = None, |
| inputs_embeds: Optional[torch.Tensor] = None, |
| labels: Optional[torch.Tensor] = None, |
| output_attentions: Optional[bool] = None, |
| output_hidden_states: Optional[bool] = None, |
| return_dict: Optional[bool] = None, |
| ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: |
| r""" |
| labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
| Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
| config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
| `config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
| """ |
| 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, |
| ) |
|
|
| logits = None |
| if self.architecture == 'SEQ_CLS': |
| pooled_output = outputs[1] |
|
|
| pooled_output = self.dropout(pooled_output) |
| logits = self.classification_layers(pooled_output) |
| else: |
| sequence_output = outputs[0] |
|
|
| sequence_output = self.dropout(sequence_output) |
|
|
| entity_mark_1_s, entity_1, entity_mark_2_s, entity_2 = self._get_entities_and_start_markers_indices( |
| input_ids) |
|
|
| e1_start_mark_tensors = sequence_output[torch.arange(sequence_output.size(0)), entity_mark_1_s] |
| e2_start_mark_tensors = sequence_output[torch.arange(sequence_output.size(0)), entity_mark_2_s] |
|
|
| e1_tensor = sequence_output[torch.arange(sequence_output.size(0)), entity_1] |
| e2_tensor = sequence_output[torch.arange(sequence_output.size(0)), entity_2] |
|
|
| if self.pool_tokens: |
| e1_start_mark_tensors = self.ems_1_pooler(e1_start_mark_tensors) |
| e2_start_mark_tensors = self.ems_2_pooler(e2_start_mark_tensors) |
|
|
| e1_tensor = self.e_1_pooler(e1_tensor) |
| e2_tensor = self.e_2_pooler(e2_tensor) |
|
|
| if self.architecture == 'ESS': |
| e_start_markers_cat = torch.cat((e1_start_mark_tensors, e2_start_mark_tensors), 1) |
| logits = self.classification_layers(e_start_markers_cat) |
|
|
| if self.architecture == 'EF': |
| events_cat = torch.cat((e1_tensor, e2_tensor), 1) |
| logits = self.classification_layers(events_cat) |
|
|
| if self.architecture == 'EMP': |
| e1_and_start_mark = self.e_1_linear(torch.cat((e1_start_mark_tensors, e1_tensor), 1)) |
| e2_and_start_mark = self.e_2_linear(torch.cat((e2_start_mark_tensors, e2_tensor), 1)) |
| both_e_cat = torch.cat((e1_and_start_mark, e2_and_start_mark), 1) |
| logits = self.classification_layers(both_e_cat) |
|
|
| loss = None |
| if labels is not None: |
| loss_fct = CrossEntropyLoss() |
| loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
|
|
| if not return_dict: |
| output = (logits,) + outputs[2:] |
| return ((loss,) + output) if loss is not None else output |
|
|
| return SequenceClassifierOutput( |
| loss=loss, |
| logits=logits, |
| hidden_states=outputs.hidden_states, |
| attentions=outputs.attentions, |
| ) |
|
|
