| from typing import Optional, Sequence, Tuple, Union | |
| import torch | |
| from torch import nn | |
| from torch.nn import CrossEntropyLoss, functional | |
| from transformers import RobertaConfig | |
| from transformers.modeling_outputs import SequenceClassifierOutput | |
| from transformers.models.roberta.modeling_roberta import RobertaModel, RobertaPooler, RobertaPreTrainedModel | |
| class MishActivation(nn.Module): | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| return x * torch.tanh(torch.nn.functional.softplus(x)) | |
| class NFQAClassificationHead(nn.Module): | |
| def __init__( | |
| self, input_dim: int, num_labels: int, hidden_dims: Sequence[int] = (768, 512), dropout: float = 0.0, | |
| ) -> None: | |
| super().__init__() | |
| self.linear_layers = nn.Sequential(*(nn.Linear(input_dim, dim) for dim in hidden_dims)) | |
| self.classification_layer = torch.nn.Linear(hidden_dims[-1], num_labels) | |
| self.activations = [MishActivation()] * len(hidden_dims) | |
| self.dropouts = [torch.nn.Dropout(p=dropout)] * len(hidden_dims) | |
| def forward(self, inputs: torch.Tensor) -> torch.Tensor: | |
| output = inputs | |
| for layer, activation, dropout in zip(self.linear_layers, self.activations, self.dropouts): | |
| output = dropout(activation(layer(output))) | |
| return self.classification_layer(output) | |
| class RobertaNFQAClassification(RobertaPreTrainedModel): | |
| _keys_to_ignore_on_load_missing = [r'position_ids'] | |
| _DROPOUT = 0.0 | |
| def __init__(self, config: RobertaConfig): | |
| super().__init__(config) | |
| self.num_labels = config.num_labels | |
| self.config = config | |
| self.embedder = RobertaModel(config, add_pooling_layer=True) | |
| self.pooler = RobertaPooler(config) | |
| self.feedforward = NFQAClassificationHead(config.hidden_size, config.num_labels) | |
| self.dropout = torch.nn.Dropout(self._DROPOUT) | |
| self.init_weights() | |
| 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.embedder( | |
| 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, | |
| ) | |
| sequence_output = outputs[0] | |
| logits = self.feedforward(self.dropout(self.pooler(sequence_output))) | |
| loss = None | |
| if labels is not None: | |
| loss_fct = CrossEntropyLoss() | |
| loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) | |
| 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, | |
| ) | |