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from typing import Optional, Union, Tuple |
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import torch |
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from torch import nn |
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from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss |
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from transformers.modeling_outputs import SequenceClassifierOutput, QuestionAnsweringModelOutput, TokenClassifierOutput |
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from .modeling_bert import XLMRobertaPreTrainedModel, XLMRobertaModel |
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from .configuration_xlm_roberta import XLMRobertaFlashConfig |
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class XLMRobertaForSequenceClassification(XLMRobertaPreTrainedModel): |
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def __init__(self, config: XLMRobertaFlashConfig): |
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super().__init__(config) |
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self.num_labels = config.num_labels |
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self.config = config |
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self.roberta = XLMRobertaModel(config) |
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classifier_dropout = ( |
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config.classifier_dropout |
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if config.classifier_dropout is not None |
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else config.hidden_dropout_prob |
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) |
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self.dropout = nn.Dropout(classifier_dropout) |
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self.classifier = nn.Linear(config.hidden_size, config.num_labels) |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.Tensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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token_type_ids: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.Tensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.Tensor] = None, |
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labels: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: |
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r""" |
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labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
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Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
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config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
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`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
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""" |
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return_dict = ( |
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return_dict if return_dict is not None else self.config.use_return_dict |
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) |
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assert head_mask is None |
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assert inputs_embeds is None |
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assert output_attentions is None |
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assert output_hidden_states is None |
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assert return_dict |
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outputs = self.roberta( |
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input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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pooled_output = outputs[1] |
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pooled_output = self.dropout(pooled_output) |
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logits = self.classifier(pooled_output) |
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loss = None |
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if labels is not None: |
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if self.config.problem_type is None: |
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if self.num_labels == 1: |
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self.config.problem_type = "regression" |
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elif self.num_labels > 1 and ( |
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labels.dtype == torch.long or labels.dtype == torch.int |
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): |
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self.config.problem_type = "single_label_classification" |
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else: |
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self.config.problem_type = "multi_label_classification" |
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if self.config.problem_type == "regression": |
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loss_fct = MSELoss() |
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if self.num_labels == 1: |
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loss = loss_fct(logits.squeeze(), labels.squeeze()) |
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else: |
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loss = loss_fct(logits, labels) |
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elif self.config.problem_type == "single_label_classification": |
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loss_fct = CrossEntropyLoss() |
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
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elif self.config.problem_type == "multi_label_classification": |
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loss_fct = BCEWithLogitsLoss() |
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loss = loss_fct(logits, labels) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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return SequenceClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
