Create modelling_nar.py

modelling_nar.py

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+from transformers.modeling_outputs import TokenClassifierOutput
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, AutoModel, AutoConfig, BertConfig
+from torch.nn import CrossEntropyLoss
+from typing import Optional, Tuple, Union
+import logging, json, os
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_info(label_map):
+    num_token_labels_dict = {task: len(labels) for task, labels in label_map.items()}
+    return num_token_labels_dict
+
+
+class ModelForSequenceAndTokenClassification(PreTrainedModel):
+    def __init__(self, config, num_sequence_labels, num_token_labels, do_classif=False):
+        super().__init__(config)
+        self.num_token_labels = num_token_labels
+        self.num_sequence_labels = num_sequence_labels
+        self.config = config
+        self.do_classif = do_classif
+
+        self.bert = AutoModel.from_config(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+
+        # For token classification
+        self.token_classifier = nn.Linear(config.hidden_size, self.num_token_labels)
+
+        if do_classif:
+            # For the entire sequence classification
+            self.sequence_classifier = nn.Linear(
+                config.hidden_size, self.num_sequence_labels
+            )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = AutoConfig
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def do_classif(self):
+        return self.do_classif
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    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,
+        token_labels: Optional[torch.Tensor] = None,
+        sequence_labels: Optional[torch.Tensor] = None,
+        offset_mapping: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[
+        Union[Tuple[torch.Tensor], SequenceClassifierOutput],
+        Union[Tuple[torch.Tensor], TokenClassifierOutput],
+    ]:
+        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,
+        )
+
+        # For token classification
+        token_output = outputs[0]
+
+        token_output = self.dropout(token_output)
+        token_logits = self.token_classifier(token_output)
+
+        if self.do_classif:
+            # For the entire sequence classification
+            pooled_output = outputs[1]
+
+            pooled_output = self.dropout(pooled_output)
+            sequence_logits = self.sequence_classifier(pooled_output)
+
+        # Computing the loss as the average of both losses
+        loss = None
+        if token_labels is not None:
+            loss_fct = CrossEntropyLoss()
+            # import pdb;pdb.set_trace()
+            loss_tokens = loss_fct(
+                token_logits.view(-1, self.num_token_labels), token_labels.view(-1)
+            )
+
+            if self.do_classif:
+                if self.config.problem_type == "regression":
+                    loss_fct = MSELoss()
+                    if self.num_sequence_labels == 1:
+                        loss_sequence = loss_fct(
+                            sequence_logits.squeeze(), sequence_labels.squeeze()
+                        )
+                    else:
+                        loss_sequence = loss_fct(sequence_logits, sequence_labels)
+                if self.config.problem_type == "single_label_classification":
+                    loss_fct = CrossEntropyLoss()
+                    loss_sequence = loss_fct(
+                        sequence_logits.view(-1, self.num_sequence_labels),
+                        sequence_labels.view(-1),
+                    )
+                elif self.config.problem_type == "multi_label_classification":
+                    loss_fct = BCEWithLogitsLoss()
+                    loss_sequence = loss_fct(sequence_logits, sequence_labels)
+
+                loss = loss_tokens + loss_sequence
+            else:
+                loss = loss_tokens
+
+        if not return_dict:
+            if self.do_classif:
+                output = (
+                    sequence_logits,
+                    token_logits,
+                ) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+            else:
+                output = (token_logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+        if self.do_classif:
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=sequence_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            ), TokenClassifierOutput(
+                loss=loss,
+                logits=token_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+        else:
+            return TokenClassifierOutput(
+                loss=loss,
+                logits=token_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )