ElectraForMultipleChoice

src/transformers/__init__.py

@@ -318,6 +318,7 @@
         ElectraForMaskedLM,
         ElectraForTokenClassification,
         ElectraPreTrainedModel,
+        ElectraForMultipleChoice,
         ElectraForSequenceClassification,
         ElectraForQuestionAnswering,
         ElectraModel,

src/transformers/modeling_auto.py

@@ -77,6 +77,7 @@
 )
 from .modeling_electra import (
     ElectraForMaskedLM,
+    ElectraForMultipleChoice,
     ElectraForPreTraining,
     ElectraForQuestionAnswering,
     ElectraForSequenceClassification,
@@ -261,6 +262,7 @@
 MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
     [
         (CamembertConfig, CamembertForMultipleChoice),
+        (ElectraConfig, ElectraForMultipleChoice),
         (XLMRobertaConfig, XLMRobertaForMultipleChoice),
         (LongformerConfig, LongformerForMultipleChoice),
         (RobertaConfig, RobertaForMultipleChoice),

src/transformers/modeling_electra.py

@@ -858,3 +858,133 @@ def forward(
             outputs = (total_loss,) + outputs
 
         return outputs  # (loss), start_logits, end_logits, (hidden_states), (attentions)
+
+
+class ElectraPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0, :]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = get_activation("gelu")(
+            pooled_output
+        )  # although BERT uses tanh here, it seems Electra authors used gelu here
+        return pooled_output
+
+
+@add_start_docstrings(
+    """ELECTRA Model with a multiple choice classification head on top (a linear layer on top of
+    the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
+    ELECTRA_INPUTS_DOCSTRING,
+)
+class ElectraForMultipleChoice(ElectraPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.electra = ElectraModel(config)
+        self.pooler = ElectraPooler(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+
+        self.init_weights()
+
+    @add_start_docstrings_to_callable(ELECTRA_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)"))
+    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,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
+            Labels for computing the multiple choice classification loss.
+            Indices should be in ``[0, ..., num_choices-1]`` where `num_choices` is the size of the second dimension
+            of the input tensors. (see `input_ids` above)
+
+    Returns:
+        :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.ElectraConfig`) and inputs:
+        loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :obj:`labels` is provided):
+            Classification loss.
+        classification_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, num_choices)`):
+            `num_choices` is the second dimension of the input tensors. (see `input_ids` above).
+
+            Classification scores (before SoftMax).
+        hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
+            of shape :obj:`(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or ``config.output_attentions=True``):
+            Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
+            :obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+
+    Examples::
+
+        from transformers import ElectraTokenizer, ElectraForMultipleChoice
+        import torch
+
+        tokenizer = ElectraTokenizer.from_pretrained('google/electra-base-discriminator')
+        model = ElectraForMultipleChoice.from_pretrained('google/electra-base-discriminator')
+
+        prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+        choice0 = "It is eaten with a fork and a knife."
+        choice1 = "It is eaten while held in the hand."
+        labels = torch.tensor(0) # choice0 is correct (according to Wikipedia ;))
+
+        encoding = tokenizer.batch_encode_plus([[prompt, choice0], [prompt, choice1]], return_tensors='pt', pad_to_max_length=True)
+        outputs = model(**{k: v.unsqueeze(0) for k,v in encoding.items()}, labels=labels) # batch size is 1
+
+        # the linear classifier still needs to be trained
+        loss, logits = outputs[:2]
+        """
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        discriminator_hidden_states = self.electra(
+            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,
+        )
+
+        sequence_output = discriminator_hidden_states[0]
+
+        pooled_output = self.pooler(sequence_output)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        outputs = (reshaped_logits,) + discriminator_hidden_states[
+            1:
+        ]  # add hidden states and attention if they are here
+
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+            outputs = (loss,) + outputs
+
+        return outputs  # (loss), reshaped_logits, (hidden_states), (attentions)

tests/test_modeling_electra.py

@@ -30,6 +30,7 @@
         ElectraForMaskedLM,
         ElectraForTokenClassification,
         ElectraForPreTraining,
+        ElectraForMultipleChoice,
         ElectraForSequenceClassification,
         ElectraForQuestionAnswering,
     )
@@ -44,6 +45,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
             ElectraModel,
             ElectraForPreTraining,
             ElectraForMaskedLM,
+            ElectraForMultipleChoice,
             ElectraForTokenClassification,
             ElectraForSequenceClassification,
             ElectraForQuestionAnswering,
@@ -308,6 +310,37 @@ def create_and_check_electra_for_question_answering(
             self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length])
             self.check_loss_output(result)
 
+        def create_and_check_electra_for_multiple_choice(
+            self,
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            fake_token_labels,
+        ):
+            config.num_choices = self.num_choices
+            model = ElectraForMultipleChoice(config=config)
+            model.to(torch_device)
+            model.eval()
+            multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+            loss, logits = model(
+                multiple_choice_inputs_ids,
+                attention_mask=multiple_choice_input_mask,
+                token_type_ids=multiple_choice_token_type_ids,
+                labels=choice_labels,
+            )
+            result = {
+                "loss": loss,
+                "logits": logits,
+            }
+            self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices])
+            self.check_loss_output(result)
+
         def prepare_config_and_inputs_for_common(self):
             config_and_inputs = self.prepare_config_and_inputs()
             (
@@ -354,6 +387,10 @@ def test_for_question_answering(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_electra_for_question_answering(*config_and_inputs)
 
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_electra_for_multiple_choice(*config_and_inputs)
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: