def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RemBertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
def convert_rembert_tf_checkpoint_to_pytorch(tf_checkpoint_path,
bert_config_file,
pytorch_dump_path):
# Initialise PyTorch model
config = RemBertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = RemBertModel(config)
# Load weights from tf checkpoint
load_tf_weights_in_rembert(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
def create_and_check_model(self, config, input_ids, token_type_ids,
input_mask, sequence_labels, token_labels,
choice_labels):
model = RemBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))