from typing import Optional, Dict, Any import torch from transformers import AutoModel, PreTrainedModel from transformers.activations import GELUActivation, ClippedGELUActivation from transformers.configuration_utils import PretrainedConfig from transformers.modeling_utils import PoolerEndLogits from .configuration_relik import RelikReaderConfig class RelikReaderSample: def __init__(self, **kwargs): super().__setattr__("_d", {}) self._d = kwargs def __getattribute__(self, item): return super(RelikReaderSample, self).__getattribute__(item) def __getattr__(self, item): if item.startswith("__") and item.endswith("__"): # this is likely some python library-specific variable (such as __deepcopy__ for copy) # better follow standard behavior here raise AttributeError(item) elif item in self._d: return self._d[item] else: return None def __setattr__(self, key, value): if key in self._d: self._d[key] = value else: super().__setattr__(key, value) activation2functions = { "relu": torch.nn.ReLU(), "gelu": GELUActivation(), "gelu_10": ClippedGELUActivation(-10, 10), } class PoolerEndLogitsBi(PoolerEndLogits): def __init__(self, config: PretrainedConfig): super().__init__(config) self.dense_1 = torch.nn.Linear(config.hidden_size, 2) def forward( self, hidden_states: torch.FloatTensor, start_states: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, p_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if p_mask is not None: p_mask = p_mask.unsqueeze(-1) logits = super().forward( hidden_states, start_states, start_positions, p_mask, ) return logits class RelikReaderSpanModel(PreTrainedModel): config_class = RelikReaderConfig def __init__(self, config: RelikReaderConfig, *args, **kwargs): super().__init__(config) # Transformer model declaration self.config = config self.transformer_model = ( AutoModel.from_pretrained(self.config.transformer_model) if self.config.num_layers is None else AutoModel.from_pretrained( self.config.transformer_model, num_hidden_layers=self.config.num_layers ) ) self.transformer_model.resize_token_embeddings( self.transformer_model.config.vocab_size + self.config.additional_special_symbols ) self.activation = self.config.activation self.linears_hidden_size = self.config.linears_hidden_size self.use_last_k_layers = self.config.use_last_k_layers # named entity detection layers self.ned_start_classifier = self._get_projection_layer( self.activation, last_hidden=2, layer_norm=False ) self.ned_end_classifier = PoolerEndLogits(self.transformer_model.config) # END entity disambiguation layer self.ed_start_projector = self._get_projection_layer(self.activation) self.ed_end_projector = self._get_projection_layer(self.activation) self.training = self.config.training # criterion self.criterion = torch.nn.CrossEntropyLoss() def _get_projection_layer( self, activation: str, last_hidden: Optional[int] = None, input_hidden=None, layer_norm: bool = True, ) -> torch.nn.Sequential: head_components = [ torch.nn.Dropout(0.1), torch.nn.Linear( self.transformer_model.config.hidden_size * self.use_last_k_layers if input_hidden is None else input_hidden, self.linears_hidden_size, ), activation2functions[activation], torch.nn.Dropout(0.1), torch.nn.Linear( self.linears_hidden_size, self.linears_hidden_size if last_hidden is None else last_hidden, ), ] if layer_norm: head_components.append( torch.nn.LayerNorm( self.linears_hidden_size if last_hidden is None else last_hidden, self.transformer_model.config.layer_norm_eps, ) ) return torch.nn.Sequential(*head_components) def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: mask = mask.unsqueeze(-1) if next(self.parameters()).dtype == torch.float16: logits = logits * (1 - mask) - 65500 * mask else: logits = logits * (1 - mask) - 1e30 * mask return logits def _get_model_features( self, input_ids: torch.Tensor, attention_mask: torch.Tensor, token_type_ids: Optional[torch.Tensor], ): model_input = { "input_ids": input_ids, "attention_mask": attention_mask, "output_hidden_states": self.use_last_k_layers > 1, } if token_type_ids is not None: model_input["token_type_ids"] = token_type_ids model_output = self.transformer_model(**model_input) if self.use_last_k_layers > 1: model_features = torch.cat( model_output[1][-self.use_last_k_layers :], dim=-1 ) else: model_features = model_output[0] return model_features def compute_ned_end_logits( self, start_predictions, start_labels, model_features, prediction_mask, batch_size, ) -> Optional[torch.Tensor]: # todo: maybe when constraining on the spans, # we should not use a prediction_mask for the end tokens. # at least we should not during training imo start_positions = start_labels if self.training else start_predictions start_positions_indices = ( torch.arange(start_positions.size(1), device=start_positions.device) .unsqueeze(0) .expand(batch_size, -1)[start_positions > 0] ).to(start_positions.device) if len(start_positions_indices) > 0: expanded_features = torch.cat( [ model_features[i].unsqueeze(0).expand(x, -1, -1) for i, x in enumerate(torch.sum(start_positions > 0, dim=-1)) if x > 0 ], dim=0, ).to(start_positions_indices.device) expanded_prediction_mask = torch.cat( [ prediction_mask[i].unsqueeze(0).expand(x, -1) for i, x in enumerate(torch.sum(start_positions > 0, dim=-1)) if x > 0 ], dim=0, ).to(expanded_features.device) end_logits = self.ned_end_classifier( hidden_states=expanded_features, start_positions=start_positions_indices, p_mask=expanded_prediction_mask, ) return end_logits return None def compute_classification_logits( self, model_features, special_symbols_mask, prediction_mask, batch_size, start_positions=None, end_positions=None, ) -> torch.Tensor: if start_positions is None or end_positions is None: start_positions = torch.zeros_like(prediction_mask) end_positions = torch.zeros_like(prediction_mask) model_start_features = self.ed_start_projector(model_features) model_end_features = self.ed_end_projector(model_features) model_end_features[start_positions > 0] = model_end_features[end_positions > 0] model_ed_features = torch.cat( [model_start_features, model_end_features], dim=-1 ) # computing ed features classes_representations = torch.sum(special_symbols_mask, dim=1)[0].item() special_symbols_representation = model_ed_features[special_symbols_mask].view( batch_size, classes_representations, -1 ) logits = torch.bmm( model_ed_features, torch.permute(special_symbols_representation, (0, 2, 1)), ) logits = self._mask_logits(logits, prediction_mask) return logits def forward( self, input_ids: torch.Tensor, attention_mask: torch.Tensor, token_type_ids: Optional[torch.Tensor] = None, prediction_mask: Optional[torch.Tensor] = None, special_symbols_mask: Optional[torch.Tensor] = None, start_labels: Optional[torch.Tensor] = None, end_labels: Optional[torch.Tensor] = None, use_predefined_spans: bool = False, *args, **kwargs, ) -> Dict[str, Any]: batch_size, seq_len = input_ids.shape model_features = self._get_model_features( input_ids, attention_mask, token_type_ids ) ned_start_labels = None # named entity detection if required if use_predefined_spans: # no need to compute spans ned_start_logits, ned_start_probabilities, ned_start_predictions = ( None, None, torch.clone(start_labels) if start_labels is not None else torch.zeros_like(input_ids), ) ned_end_logits, ned_end_probabilities, ned_end_predictions = ( None, None, torch.clone(end_labels) if end_labels is not None else torch.zeros_like(input_ids), ) ned_start_predictions[ned_start_predictions > 0] = 1 ned_end_predictions[ned_end_predictions > 0] = 1 else: # compute spans # start boundary prediction ned_start_logits = self.ned_start_classifier(model_features) ned_start_logits = self._mask_logits(ned_start_logits, prediction_mask) ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1) ned_start_predictions = ned_start_probabilities.argmax(dim=-1) # end boundary prediction ned_start_labels = ( torch.zeros_like(start_labels) if start_labels is not None else None ) if ned_start_labels is not None: ned_start_labels[start_labels == -100] = -100 ned_start_labels[start_labels > 0] = 1 ned_end_logits = self.compute_ned_end_logits( ned_start_predictions, ned_start_labels, model_features, prediction_mask, batch_size, ) if ned_end_logits is not None: ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1) ned_end_predictions = torch.argmax(ned_end_probabilities, dim=-1) else: ned_end_logits, ned_end_probabilities = None, None ned_end_predictions = ned_start_predictions.new_zeros(batch_size) # flattening end predictions # (flattening can happen only if the # end boundaries were not predicted using the gold labels) if not self.training: flattened_end_predictions = torch.clone(ned_start_predictions) flattened_end_predictions[flattened_end_predictions > 0] = 0 batch_start_predictions = list() for elem_idx in range(batch_size): batch_start_predictions.append( torch.where(ned_start_predictions[elem_idx] > 0)[0].tolist() ) # check that the total number of start predictions # is equal to the end predictions total_start_predictions = sum(map(len, batch_start_predictions)) total_end_predictions = len(ned_end_predictions) assert ( total_start_predictions == 0 or total_start_predictions == total_end_predictions ), ( f"Total number of start predictions = {total_start_predictions}. " f"Total number of end predictions = {total_end_predictions}" ) curr_end_pred_num = 0 for elem_idx, bsp in enumerate(batch_start_predictions): for sp in bsp: ep = ned_end_predictions[curr_end_pred_num].item() if ep < sp: ep = sp # if we already set this span throw it (no overlap) if flattened_end_predictions[elem_idx, ep] == 1: ned_start_predictions[elem_idx, sp] = 0 else: flattened_end_predictions[elem_idx, ep] = 1 curr_end_pred_num += 1 ned_end_predictions = flattened_end_predictions start_position, end_position = ( (start_labels, end_labels) if self.training else (ned_start_predictions, ned_end_predictions) ) # Entity disambiguation ed_logits = self.compute_classification_logits( model_features, special_symbols_mask, prediction_mask, batch_size, start_position, end_position, ) ed_probabilities = torch.softmax(ed_logits, dim=-1) ed_predictions = torch.argmax(ed_probabilities, dim=-1) # output build output_dict = dict( batch_size=batch_size, ned_start_logits=ned_start_logits, ned_start_probabilities=ned_start_probabilities, ned_start_predictions=ned_start_predictions, ned_end_logits=ned_end_logits, ned_end_probabilities=ned_end_probabilities, ned_end_predictions=ned_end_predictions, ed_logits=ed_logits, ed_probabilities=ed_probabilities, ed_predictions=ed_predictions, ) # compute loss if labels if start_labels is not None and end_labels is not None and self.training: # named entity detection loss # start if ned_start_logits is not None: ned_start_loss = self.criterion( ned_start_logits.view(-1, ned_start_logits.shape[-1]), ned_start_labels.view(-1), ) else: ned_start_loss = 0 # end if ned_end_logits is not None: ned_end_labels = torch.zeros_like(end_labels) ned_end_labels[end_labels == -100] = -100 ned_end_labels[end_labels > 0] = 1 ned_end_loss = self.criterion( ned_end_logits, ( torch.arange( ned_end_labels.size(1), device=ned_end_labels.device ) .unsqueeze(0) .expand(batch_size, -1)[ned_end_labels > 0] ).to(ned_end_labels.device), ) else: ned_end_loss = 0 # entity disambiguation loss start_labels[ned_start_labels != 1] = -100 ed_labels = torch.clone(start_labels) ed_labels[end_labels > 0] = end_labels[end_labels > 0] ed_loss = self.criterion( ed_logits.view(-1, ed_logits.shape[-1]), ed_labels.view(-1), ) output_dict["ned_start_loss"] = ned_start_loss output_dict["ned_end_loss"] = ned_end_loss output_dict["ed_loss"] = ed_loss output_dict["loss"] = ned_start_loss + ned_end_loss + ed_loss return output_dict class RelikReaderREModel(PreTrainedModel): config_class = RelikReaderConfig def __init__(self, config, *args, **kwargs): super().__init__(config) # Transformer model declaration # self.transformer_model_name = transformer_model self.config = config self.transformer_model = ( AutoModel.from_pretrained(config.transformer_model) if config.num_layers is None else AutoModel.from_pretrained( config.transformer_model, num_hidden_layers=config.num_layers ) ) self.transformer_model.resize_token_embeddings( self.transformer_model.config.vocab_size + config.additional_special_symbols ) # named entity detection layers self.ned_start_classifier = self._get_projection_layer( config.activation, last_hidden=2, layer_norm=False ) self.ned_end_classifier = PoolerEndLogitsBi(self.transformer_model.config) self.entity_type_loss = ( config.entity_type_loss if hasattr(config, "entity_type_loss") else False ) self.relation_disambiguation_loss = ( config.relation_disambiguation_loss if hasattr(config, "relation_disambiguation_loss") else False ) input_hidden_ents = 2 * self.transformer_model.config.hidden_size self.re_subject_projector = self._get_projection_layer( config.activation, input_hidden=input_hidden_ents ) self.re_object_projector = self._get_projection_layer( config.activation, input_hidden=input_hidden_ents ) self.re_relation_projector = self._get_projection_layer(config.activation) if self.entity_type_loss or self.relation_disambiguation_loss: self.re_entities_projector = self._get_projection_layer( config.activation, input_hidden=2 * self.transformer_model.config.hidden_size, ) self.re_definition_projector = self._get_projection_layer( config.activation, ) self.re_classifier = self._get_projection_layer( config.activation, input_hidden=config.linears_hidden_size, last_hidden=2, layer_norm=False, ) if self.entity_type_loss or self.relation_disambiguation_loss: self.re_ed_classifier = self._get_projection_layer( config.activation, input_hidden=config.linears_hidden_size, last_hidden=2, layer_norm=False, ) self.training = config.training # criterion self.criterion = torch.nn.CrossEntropyLoss() def _get_projection_layer( self, activation: str, last_hidden: Optional[int] = None, input_hidden=None, layer_norm: bool = True, ) -> torch.nn.Sequential: head_components = [ torch.nn.Dropout(0.1), torch.nn.Linear( self.transformer_model.config.hidden_size * self.config.use_last_k_layers if input_hidden is None else input_hidden, self.config.linears_hidden_size, ), activation2functions[activation], torch.nn.Dropout(0.1), torch.nn.Linear( self.config.linears_hidden_size, self.config.linears_hidden_size if last_hidden is None else last_hidden, ), ] if layer_norm: head_components.append( torch.nn.LayerNorm( self.config.linears_hidden_size if last_hidden is None else last_hidden, self.transformer_model.config.layer_norm_eps, ) ) return torch.nn.Sequential(*head_components) def _mask_logits(self, logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: mask = mask.unsqueeze(-1) if next(self.parameters()).dtype == torch.float16: logits = logits * (1 - mask) - 65500 * mask else: logits = logits * (1 - mask) - 1e30 * mask return logits def _get_model_features( self, input_ids: torch.Tensor, attention_mask: torch.Tensor, token_type_ids: Optional[torch.Tensor], ): model_input = { "input_ids": input_ids, "attention_mask": attention_mask, "output_hidden_states": self.config.use_last_k_layers > 1, } if token_type_ids is not None: model_input["token_type_ids"] = token_type_ids model_output = self.transformer_model(**model_input) if self.config.use_last_k_layers > 1: model_features = torch.cat( model_output[1][-self.config.use_last_k_layers :], dim=-1 ) else: model_features = model_output[0] return model_features def compute_ned_end_logits( self, start_predictions, start_labels, model_features, prediction_mask, batch_size, ) -> Optional[torch.Tensor]: # todo: maybe when constraining on the spans, # we should not use a prediction_mask for the end tokens. # at least we should not during training imo start_positions = start_labels if self.training else start_predictions start_positions_indices = ( torch.arange(start_positions.size(1), device=start_positions.device) .unsqueeze(0) .expand(batch_size, -1)[start_positions > 0] ).to(start_positions.device) if len(start_positions_indices) > 0: expanded_features = torch.cat( [ model_features[i].unsqueeze(0).expand(x, -1, -1) for i, x in enumerate(torch.sum(start_positions > 0, dim=-1)) if x > 0 ], dim=0, ).to(start_positions_indices.device) expanded_prediction_mask = torch.cat( [ prediction_mask[i].unsqueeze(0).expand(x, -1) for i, x in enumerate(torch.sum(start_positions > 0, dim=-1)) if x > 0 ], dim=0, ).to(expanded_features.device) # mask all tokens before start_positions_indices ie, mask all tokens with # indices < start_positions_indices with 1, ie. [range(x) for x in start_positions_indices] expanded_prediction_mask = torch.stack( [ torch.cat( [ torch.ones(x, device=expanded_features.device), expanded_prediction_mask[i, x:], ] ) for i, x in enumerate(start_positions_indices) if x > 0 ], dim=0, ).to(expanded_features.device) end_logits = self.ned_end_classifier( hidden_states=expanded_features, start_positions=start_positions_indices, p_mask=expanded_prediction_mask, ) return end_logits return None def compute_relation_logits( self, model_entity_features, special_symbols_features, ) -> torch.Tensor: model_subject_features = self.re_subject_projector(model_entity_features) model_object_features = self.re_object_projector(model_entity_features) special_symbols_start_representation = self.re_relation_projector( special_symbols_features ) re_logits = torch.einsum( "bse,bde,bfe->bsdfe", model_subject_features, model_object_features, special_symbols_start_representation, ) re_logits = self.re_classifier(re_logits) return re_logits def compute_entity_logits( self, model_entity_features, special_symbols_features, ) -> torch.Tensor: model_ed_features = self.re_entities_projector(model_entity_features) special_symbols_ed_representation = self.re_definition_projector( special_symbols_features ) logits = torch.einsum( "bce,bde->bcde", model_ed_features, special_symbols_ed_representation, ) logits = self.re_ed_classifier(logits) start_logits = self._mask_logits( logits, (model_entity_features == -100) .all(2) .long() .unsqueeze(2) .repeat(1, 1, torch.sum(model_entity_features, dim=1)[0].item()), ) return logits def compute_loss(self, logits, labels, mask=None): logits = logits.view(-1, logits.shape[-1]) labels = labels.view(-1).long() if mask is not None: return self.criterion(logits[mask], labels[mask]) return self.criterion(logits, labels) def compute_ned_end_loss(self, ned_end_logits, end_labels): if ned_end_logits is None: return 0 ned_end_labels = torch.zeros_like(end_labels) ned_end_labels[end_labels == -100] = -100 ned_end_labels[end_labels > 0] = 1 return self.compute_loss(ned_end_logits, ned_end_labels) def compute_ned_type_loss( self, disambiguation_labels, re_ned_entities_logits, ned_type_logits, re_entities_logits, entity_types, ): if self.entity_type_loss and self.relation_disambiguation_loss: return self.compute_loss(disambiguation_labels, re_ned_entities_logits) if self.entity_type_loss: return self.compute_loss( disambiguation_labels[:, :, :entity_types], ned_type_logits ) if self.relation_disambiguation_loss: return self.compute_loss(disambiguation_labels, re_entities_logits) return 0 def compute_relation_loss(self, relation_labels, re_logits): return self.compute_loss( re_logits, relation_labels, relation_labels.view(-1) != -100 ) def forward( self, input_ids: torch.Tensor, attention_mask: torch.Tensor, token_type_ids: torch.Tensor, prediction_mask: Optional[torch.Tensor] = None, special_symbols_mask: Optional[torch.Tensor] = None, special_symbols_mask_entities: Optional[torch.Tensor] = None, start_labels: Optional[torch.Tensor] = None, end_labels: Optional[torch.Tensor] = None, disambiguation_labels: Optional[torch.Tensor] = None, relation_labels: Optional[torch.Tensor] = None, is_validation: bool = False, is_prediction: bool = False, *args, **kwargs, ) -> Dict[str, Any]: batch_size = input_ids.shape[0] model_features = self._get_model_features( input_ids, attention_mask, token_type_ids ) # named entity detection if is_prediction and start_labels is not None: ned_start_logits, ned_start_probabilities, ned_start_predictions = ( None, None, torch.zeros_like(start_labels), ) ned_end_logits, ned_end_probabilities, ned_end_predictions = ( None, None, torch.zeros_like(end_labels), ) ned_start_predictions[start_labels > 0] = 1 ned_end_predictions[end_labels > 0] = 1 ned_end_predictions = ned_end_predictions[~(end_labels == -100).all(2)] else: # start boundary prediction ned_start_logits = self.ned_start_classifier(model_features) ned_start_logits = self._mask_logits( ned_start_logits, prediction_mask ) # why? ned_start_probabilities = torch.softmax(ned_start_logits, dim=-1) ned_start_predictions = ned_start_probabilities.argmax(dim=-1) # end boundary prediction ned_start_labels = ( torch.zeros_like(start_labels) if start_labels is not None else None ) # start_labels contain entity id at their position, we just need 1 for start of entity if ned_start_labels is not None: ned_start_labels[start_labels > 0] = 1 # compute end logits only if there are any start predictions. # For each start prediction, n end predictions are made ned_end_logits = self.compute_ned_end_logits( ned_start_predictions, ned_start_labels, model_features, prediction_mask, batch_size, ) # For each start prediction, n end predictions are made based on # binary classification ie. argmax at each position. ned_end_probabilities = torch.softmax(ned_end_logits, dim=-1) ned_end_predictions = ned_end_probabilities.argmax(dim=-1) if is_prediction or is_validation: end_preds_count = ned_end_predictions.sum(1) # If there are no end predictions for a start prediction, remove the start prediction ned_start_predictions[ned_start_predictions == 1] = ( end_preds_count != 0 ).long() ned_end_predictions = ned_end_predictions[end_preds_count != 0] if end_labels is not None: end_labels = end_labels[~(end_labels == -100).all(2)] start_position, end_position = ( (start_labels, end_labels) if (not is_prediction and not is_validation) else (ned_start_predictions, ned_end_predictions) ) start_counts = (start_position > 0).sum(1) ned_end_predictions = ned_end_predictions.split(start_counts.tolist()) # We can only predict relations if we have start and end predictions if (end_position > 0).sum() > 0: ends_count = (end_position > 0).sum(1) model_subject_features = torch.cat( [ torch.repeat_interleave( model_features[start_position > 0], ends_count, dim=0 ), # start position features torch.repeat_interleave(model_features, start_counts, dim=0)[ end_position > 0 ], # end position features ], dim=-1, ) ents_count = torch.nn.utils.rnn.pad_sequence( torch.split(ends_count, start_counts.tolist()), batch_first=True, padding_value=0, ).sum(1) model_subject_features = torch.nn.utils.rnn.pad_sequence( torch.split(model_subject_features, ents_count.tolist()), batch_first=True, padding_value=-100, ) if is_validation or is_prediction: model_subject_features = model_subject_features[:, :30, :] # entity disambiguation. Here relation_disambiguation_loss would only be useful to # reduce the number of candidate relations for the next step, but currently unused. if self.entity_type_loss or self.relation_disambiguation_loss: (re_ned_entities_logits) = self.compute_entity_logits( model_subject_features, model_features[ special_symbols_mask | special_symbols_mask_entities ].view(batch_size, -1, model_features.shape[-1]), ) entity_types = torch.sum(special_symbols_mask_entities, dim=1)[0].item() ned_type_logits = re_ned_entities_logits[:, :, :entity_types] re_entities_logits = re_ned_entities_logits[:, :, entity_types:] if self.entity_type_loss: ned_type_probabilities = torch.softmax(ned_type_logits, dim=-1) ned_type_predictions = ned_type_probabilities.argmax(dim=-1) ned_type_predictions = ned_type_predictions.argmax(dim=-1) re_entities_probabilities = torch.softmax(re_entities_logits, dim=-1) re_entities_predictions = re_entities_probabilities.argmax(dim=-1) else: ( ned_type_logits, ned_type_probabilities, re_entities_logits, re_entities_probabilities, ) = (None, None, None, None) ned_type_predictions, re_entities_predictions = ( torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device), torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device), ) # Compute relation logits re_logits = self.compute_relation_logits( model_subject_features, model_features[special_symbols_mask].view( batch_size, -1, model_features.shape[-1] ), ) re_probabilities = torch.softmax(re_logits, dim=-1) # we set a thresshold instead of argmax in cause it needs to be tweaked re_predictions = re_probabilities[:, :, :, :, 1] > 0.5 # re_predictions = re_probabilities.argmax(dim=-1) re_probabilities = re_probabilities[:, :, :, :, 1] else: ( ned_type_logits, ned_type_probabilities, re_entities_logits, re_entities_probabilities, ) = (None, None, None, None) ned_type_predictions, re_entities_predictions = ( torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device), torch.zeros([batch_size, 1], dtype=torch.long).to(input_ids.device), ) re_logits, re_probabilities, re_predictions = ( torch.zeros( [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long ).to(input_ids.device), torch.zeros( [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long ).to(input_ids.device), torch.zeros( [batch_size, 1, 1, special_symbols_mask.sum(1)[0]], dtype=torch.long ).to(input_ids.device), ) # output build output_dict = dict( batch_size=batch_size, ned_start_logits=ned_start_logits, ned_start_probabilities=ned_start_probabilities, ned_start_predictions=ned_start_predictions, ned_end_logits=ned_end_logits, ned_end_probabilities=ned_end_probabilities, ned_end_predictions=ned_end_predictions, ned_type_logits=ned_type_logits, ned_type_probabilities=ned_type_probabilities, ned_type_predictions=ned_type_predictions, re_entities_logits=re_entities_logits, re_entities_probabilities=re_entities_probabilities, re_entities_predictions=re_entities_predictions, re_logits=re_logits, re_probabilities=re_probabilities, re_predictions=re_predictions, ) if ( start_labels is not None and end_labels is not None and relation_labels is not None ): ned_start_loss = self.compute_loss(ned_start_logits, ned_start_labels) ned_end_loss = self.compute_ned_end_loss(ned_end_logits, end_labels) if self.entity_type_loss or self.relation_disambiguation_loss: ned_type_loss = self.compute_ned_type_loss( disambiguation_labels, re_ned_entities_logits, ned_type_logits, re_entities_logits, entity_types, ) relation_loss = self.compute_relation_loss(relation_labels, re_logits) # compute loss. We can skip the relation loss if we are in the first epochs (optional) if self.entity_type_loss or self.relation_disambiguation_loss: output_dict["loss"] = ( ned_start_loss + ned_end_loss + relation_loss + ned_type_loss ) / 4 output_dict["ned_type_loss"] = ned_type_loss else: output_dict["loss"] = ( ned_start_loss + ned_end_loss + relation_loss ) / 3 output_dict["ned_start_loss"] = ned_start_loss output_dict["ned_end_loss"] = ned_end_loss output_dict["re_loss"] = relation_loss return output_dict