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 prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask(
[self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length],
self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size],
self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length],
self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RemBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
input_embedding_size=self.input_embedding_size,
output_embedding_size=self.output_embedding_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels