remove custom head

roberta.py

@@ -6,145 +6,6 @@ from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss
 from transformers import RobertaForSequenceClassification, RobertaModel
 from transformers.modeling_outputs import SequenceClassifierOutput
 
-
-class RobertaLeakyHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.use_hidden_states = config.use_hidden_states
-        hidden_size = config.hidden_size
-        if self.use_hidden_states:
-            hidden_size *= 2
-
-        self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
-        self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
-        classifier_dropout = (
-            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
-        )
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.out_proj = nn.Linear(hidden_size, config.num_labels)
-
-    def forward(self, features, **kwargs):
-        if 'hidden_states' in kwargs and kwargs['hidden_states'] is not None:
-            if self.use_hidden_states:
-                x = torch.cat(
-                    (
-                        features[:, 0, :],
-                        # take <s> token (equiv. to [CLS]) from hidden states from second from the end
-                        kwargs['hidden_states'][-2][:, 0, :]
-                    ),
-                    dim=1
-                )
-            else:
-                x = features[:, 0, :] + kwargs['hidden_states'][-2][:, 0, :]
-            del kwargs['hidden_states']
-        else:
-            x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-            if self.use_hidden_states:
-                x = torch.cat(
-                    (
-                        features[:, 0, :],
-                        torch.zeros(x.size(), dtype=x.dtype, device=x.device)
-                    ),
-                    dim=1
-                )
-
-        x = self.dense_1(x)
-        x = torch.relu(x)
-        x = self.dropout(x)
-
-        x = self.dense_2(x)
-        x = torch.relu(x)
-        x = self.dropout(x)
-
-        x = self.out_proj(x)
-        return x
-
-
-class RobertaLeaky(RobertaForSequenceClassification):
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.roberta = RobertaModel(config, add_pooling_layer=False)
-        self.classifier = RobertaLeakyHead(config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            attention_mask: 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[torch.Tensor], 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.roberta(
-            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 or self.config.use_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = outputs[0]
-        logits = self.classifier(sequence_output, hidden_states=outputs.hidden_states)
-
-        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 = 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 = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), 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,
-        )
-
 # Simple version #
 
 class RobertaClassificationHeadCustomSimple(nn.Module):

run_glue.py

@@ -49,11 +49,10 @@ from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
-from roberta import RobertaLeaky, RobertaForSequenceClassificationCustomSimple, RobertaForSequenceClassificationCustom, RobertaForSequenceClassificationCustomAlternative
+from roberta import RobertaForSequenceClassificationCustomSimple, RobertaForSequenceClassificationCustom, RobertaForSequenceClassificationCustomAlternative
 from gpt2 import GPT2ForSequenceClassificationCustomSimple, GPT2ForSequenceClassificationCustom
 
 MODEL_NAME_TO_CLASS = {
-    'roberta_leaky': RobertaLeaky,
     'roberta_simple': RobertaForSequenceClassificationCustomSimple,
     'roberta_hidden': RobertaForSequenceClassificationCustom,
     'roberta_hidden_v2': RobertaForSequenceClassificationCustomAlternative,