Add `classifier.py`

classifier.py

@@ -0,0 +1,118 @@
+from typing import Optional, Union, Tuple
+
+import torch
+from torch import nn
+from transformers.modeling_outputs import SequenceClassifierOutput
+from transformers import AutoTokenizer, DebertaV2Model, \
+                         DebertaV2ForSequenceClassification
+
+class ClassificationHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+
+    def __init__(self, config, num_extra_dims):
+        super().__init__()
+        total_dims = config.hidden_size+num_extra_dims
+        self.dense = nn.Linear(total_dims, total_dims)
+        classifier_dropout = config.hidden_dropout_prob
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.out_proj = nn.Linear(total_dims, config.num_labels)
+
+    def forward(self, features, **kwargs):
+        x = self.dropout(features)
+        x = self.dense(x)
+        x = torch.tanh(x)
+        x = self.dropout(x)
+        x = self.out_proj(x)
+        return x
+
+class CustomSequenceClassification(DebertaV2ForSequenceClassification):
+
+    def __init__(self, config, num_extra_dims):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        # might need to rename this depending on the model
+        self.deberta =  DebertaV2Model(config)
+        self.classifier = ClassificationHead(config, num_extra_dims)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        extra_data: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, 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.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # sequence_output will be (batch_size, seq_length, hidden_size)
+        sequence_output = outputs[0]
+
+        # additional data should be (batch_size, num_extra_dims)
+        cls_embedding = sequence_output[:, 0, :]
+
+        output = torch.cat((cls_embedding, extra_data), dim=-1)
+
+        logits = self.classifier(output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = nn.MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = nn.CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = nn.BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        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,
+        )