modeling_roberta.py

from transformers import RobertaModel, BertPreTrainedModel, RobertaConfig
from transformers.modeling_roberta import RobertaClassificationHead
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, KLDivLoss

ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "roberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
    "roberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
    "roberta-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
    "distilroberta-base": "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
    "roberta-base-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
    "roberta-large-openai-detector": "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}

class RobertaForSequenceClassification_v2(BertPreTrainedModel):
    config_class = RobertaConfig
    pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "roberta"

    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels

        self.roberta = RobertaModel(config)
        self.classifier = RobertaClassificationHead(config)
    
    def get_embeddings(self):
        return self.roberta.get_input_embeddings()

    def forward(
            self,
            input_ids=None,
            attention_mask=None,
            token_type_ids=None,
            position_ids=None,
            head_mask=None,
            inputs_embeds=None,
            labels=None,
        ):
        """
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`):
            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).

        Returns:
            :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.RobertaConfig`) and inputs:
            loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`label` is provided):
                Classification (or regression if config.num_labels==1) loss.
            logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, config.num_labels)`):
                Classification (or regression if config.num_labels==1) 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 ``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 RobertaTokenizer, RobertaForSequenceClassification
            import torch

            tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
            model = RobertaForSequenceClassification.from_pretrained('roberta-base')
            input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
            labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
            outputs = model(input_ids, labels=labels)
            loss, logits = outputs[:2]
        """
        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,
        )
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)
        #print(sequence_output)
        outputs = (logits,) + outputs[2:] + (sequence_output[:, 0, :],)
        if labels is not None:
            if self.num_labels == 1:
                #  We are doing regression
                loss_fct = MSELoss()
                loss = loss_fct(logits.view(-1), labels.view(-1))
            else:
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            outputs = (loss,) + outputs
        #print(outputs)
        return outputs  # (loss), logits, (hidden_states), (attentions)