from transformers.models.roberta.modeling_roberta import RobertaEmbeddings, RobertaModel, RobertaForMaskedLM from typing import Optional import torch class RobertaEmbeddingsV2(RobertaEmbeddings): def __init__(self, config): super().__init__(config) self.pad_token_id = config.pad_token_id self.position_embeddings = torch.nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0) # here padding_idx is always 0 def forward( self, input_ids: torch.LongTensor, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values_length: int = 0, ) -> torch.Tensor: inputs_embeds = self.word_embeddings(input_ids) position_ids = self.create_position_ids_from_input_ids(input_ids) position_embeddings = self.position_embeddings(position_ids) embeddings = inputs_embeds + position_embeddings return self.dropout(self.LayerNorm(embeddings)) def create_position_ids_from_input_ids(self, input_ids: torch.LongTensor) -> torch.Tensor: mask = input_ids.ne(self.pad_token_id).int() return torch.cumsum(mask, dim=1).long() * mask class RobertaModelV2(RobertaModel): def __init__(self, config, add_pooling_layer=False): super().__init__(config, add_pooling_layer=add_pooling_layer) self.embeddings = RobertaEmbeddingsV2(config) class RobertaForMaskedLMV2(RobertaForMaskedLM): def __init__(self, config): super().__init__(config) self.roberta = RobertaModelV2(config, add_pooling_layer=False)