outputs = self.bert( 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 ...
token_type_ids: tf.Tensor = None, inputs_embeds: tf.Tensor = None, past_key_values_length=0, training: bool = False, ) -> tf.Tensor: if input_ids is None and inputs_embeds is None: raise ValueError("Need to provide either `input_ids` or `input_embeds`.") input_shape = shape...
embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, # 从这儿能看到,Encoder接收的是embeddings的输出,进...
if input_ids is None and inputs_embeds is None: raise ValueError("Need to provide either `input_ids` or `input_embeds`.") input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: pos...
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, ...
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) # 重新申请的参数需要把它放到device上 if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) ...
_ids, mask, labels = batch# get your token embeddingstoken_embeds=BertModel.bert.get_input_embeddings().weight[token_ids].clone()# track gradient of token embeddingstoken_embeds.requires_grad=True# get model output that contains loss valueouts = BertModel(inputs_embeds=inputs_embeds,labels=...
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None,): outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, ...
(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None):outputs = ()for i, layer in enumerate(self.encoder.layer):if i in self.lora_layers:lora_output = self.lora_...
input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ...