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| from bidirectional_mistral import MistralBiModel | |
| from transformers import MistralPreTrainedModel | |
| import torch | |
| import numpy as np | |
| from typing import Optional, List | |
| from torch import nn | |
| from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
| from transformers.modeling_outputs import SequenceClassifierOutputWithPast | |
| class MistralForSequenceClassification(MistralPreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.model = MistralBiModel(config) | |
| self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) | |
| # Initialize weights and apply final processing | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_ids: torch.LongTensor = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| past_key_values: Optional[List[torch.FloatTensor]] = None, | |
| inputs_embeds: Optional[torch.FloatTensor] = None, | |
| labels: Optional[torch.LongTensor] = None, | |
| use_cache: Optional[bool] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| token_type_ids: Optional[bool] = 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 | |
| transformer_outputs = self.model( | |
| input_ids, | |
| attention_mask=attention_mask, | |
| position_ids=position_ids, | |
| past_key_values=past_key_values, | |
| inputs_embeds=inputs_embeds, | |
| use_cache=use_cache, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| pooled_output = transformer_outputs[0][:, 0] | |
| logits = self.score(pooled_output) | |
| 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,) + transformer_outputs[2:] | |
| return ((loss,) + output) if loss is not None else output | |
| return SequenceClassifierOutputWithPast( | |
| loss=loss, | |
| logits=logits, | |
| past_key_values=transformer_outputs.past_key_values, | |
| hidden_states=transformer_outputs.hidden_states, | |
| attentions=transformer_outputs.attentions, | |
| ) | |