| from typing import Optional, Tuple, Union | |
| import torch | |
| from torch import nn | |
| from transformers import CLIPTextConfig | |
| from transformers.modeling_outputs import MaskedLMOutput | |
| from transformers.models.clip.modeling_clip import ( | |
| CLIPPreTrainedModel, | |
| CLIPTextTransformer, | |
| ) | |
| from transformers.models.roberta.modeling_roberta import RobertaLMHead | |
| class CLIPTextModelForMaskedLM(CLIPPreTrainedModel): | |
| config_class = CLIPTextConfig | |
| _no_split_modules = ["CLIPEncoderLayer"] | |
| def __init__(self, config: CLIPTextConfig): | |
| super().__init__(config) | |
| self.text_model = CLIPTextTransformer(config) | |
| self.lm_head = RobertaLMHead(config) | |
| self.post_init() | |
| def get_input_embeddings(self) -> nn.Module: | |
| return self.text_model.embeddings.token_embedding | |
| def set_input_embeddings(self, value: nn.Module) -> None: | |
| self.text_model.embeddings.token_embedding = value | |
| def get_output_embeddings(self) -> nn.Module: | |
| return self.lm_head.decoder | |
| def set_output_embeddings(self, value: nn.Module) -> None: | |
| self.lm_head.decoder = value | |
| def forward( | |
| self, | |
| input_ids: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.Tensor] = None, | |
| labels: Optional[torch.Tensor] = None, | |
| output_attentions: Optional[bool] = None, | |
| output_hidden_states: Optional[bool] = None, | |
| return_dict: Optional[bool] = None, | |
| ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: | |
| return_dict = ( | |
| return_dict if return_dict is not None else self.config.use_return_dict | |
| ) | |
| outputs = self.text_model( | |
| input_ids=input_ids, | |
| attention_mask=attention_mask, | |
| position_ids=position_ids, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| sequence_output = outputs[0] | |
| prediction_scores = self.lm_head(sequence_output) | |
| mlm_loss = None | |
| if labels is not None: | |
| loss_fct = nn.CrossEntropyLoss() | |
| mlm_loss = loss_fct( | |
| prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) | |
| ) | |
| if not return_dict: | |
| output = (prediction_scores,) + outputs[2:] | |
| return ((mlm_loss,) + output) if mlm_loss is not None else output | |
| return MaskedLMOutput( | |
| loss=mlm_loss, | |
| logits=prediction_scores, | |
| hidden_states=outputs.hidden_states, | |
| attentions=outputs.attentions, | |
| ) | |