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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:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
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)
)
# Initialize weights and apply final processing
# self.post_init()
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,
)
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