add model files

multitask_model.py

@@ -0,0 +1,144 @@
+"""
+Implementation borrowed from transformers package and extended to support multiple prediction heads:
+
+https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py
+"""
+
+import torch
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+import transformers
+from transformers import BertTokenizer
+from transformers import models
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+from transformers.models.bert.configuration_bert import BertConfig
+from transformers.models.bert.modeling_bert import (
+    BertPreTrainedModel,
+    BERT_INPUTS_DOCSTRING,
+    _TOKENIZER_FOR_DOC,
+    _CHECKPOINT_FOR_DOC,
+    _CONFIG_FOR_DOC,
+    BertModel,
+)
+
+from transformers.file_utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    def __init__(self, config, **kwargs):
+        super().__init__(transformers.PretrainedConfig())
+        self.num_labels = kwargs.get("task_labels_map", {})
+        self.config = config
+
+        self.bert = BertModel(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        ## add task specific output heads
+        self.classifier1 = nn.Linear(
+            config.hidden_size, list(self.num_labels.values())[0]
+        )
+        self.classifier2 = nn.Linear(
+            config.hidden_size, list(self.num_labels.values())[1]
+        )
+
+        self.init_weights()
+
+    @add_start_docstrings_to_model_forward(
+        BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+    )
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        task_name=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
+            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`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,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = None
+        if task_name == list(self.num_labels.keys())[0]:
+            logits = self.classifier1(pooled_output)
+        elif task_name == list(self.num_labels.keys())[1]:
+            logits = self.classifier2(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels[task_name] == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels[task_name] > 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 = MSELoss()
+                if self.num_labels[task_name] == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    logits.view(-1, self.num_labels[task_name]), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = 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,
+        )