TypeError: forward() got an unexpected keyword argument 'labels'

🤗Transformers
1

I’ve read other people having similar errors, but none of the answers really helped with the error I’m getting now.
When I call:
model = BertWithFeats(bert_config, params)
I get the error above, I don’t understand the reason since BertForSequenceClassification should have labels in its arguments. Any help is really appreciated, thanks a lot!

class BertWithFeats(BertForSequenceClassification):
    def __init__(self, bert__config, params):
        super(BertWithFeats, self).__init__(bert_config)
        self.params=params
        self.tokenizer = params.get('tokenizer')
        self.topic = params.get('topic')

        # Store the number of labels, which tells us whether this is a
        # classification or regression task.
        self.num_labels = bert_config.num_labels
        # Calculate the combined vector length.
        self.classifier = FeatureAdaptedClassificationHead(bert_config)
        self.config = bert_config


    def forward(self, input_ids, attention_mask=None, token_type_ids=None,
                labels=None):

        outputs = self.bert(    
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            labels=labels
        )

        combined_feats=aggregate_features(outputs, self.tokenizer, inputs_ids, self.topic)
        print(combined_feats.shape)
        logits = self.classifier(combined_feats)

        loss = None
        # compute the loss as it is done in the original robertaforseqclassification code
        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 the result as it is done in the original code, loss, logits, hidden states and attentions
        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class FeatureAdaptedClassificationHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)

    def forward(self, features, **kwargs):
        x = features 
        x = self.dropout(x)
        x = self.dense(x)
        x = torch.tanh(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x
2

Inside you do

   outputs = self.bert(    
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            labels=labels
        )

but the bert attribute of this model is a BertModel which does not expect labels.

3

Oh ok, I’ve just had a look at the documentation again and have seen the reason. Would there be a way of directly calling BertForSequenceClassification instead of BertModel?

4

You should call the superclass forward method if that’s what you want.

5

Thanks for the reply, I’m posting my solution below in case anybody else is trying to do something similar :slight_smile:

class BertWithFeats(BertPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        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)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)  

    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=True,
        return_dict=True,
        tokenizer=None, 
        topic=None, 
        freeze_bert = None, 
        num_feats=None, 
        keep_stopwords = None,
        combined_feats = None, 
        sum_layers = None
    ):
        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,
        )

       
        combined_feats=aggregate_features(outputs, tokenizer, input_ids, topic, 
                                     num_feats, keep_stopwords, sum_layers).to(device)
        combined_feats = self.dropout(combined_feats)
        logits = self.classifier(combined_feats)

        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,
        )
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