def convert_tf_checkpoint_to_pytorch(tf_hub_path, pytorch_dump_path, is_encoder_named_decoder, vocab_size, is_encoder):
# Initialise PyTorch model
bert_config = BertConfig.from_pretrained(
"bert-large-cased",
vocab_size=vocab_size,
max_position_embeddings=512,
is_decoder=True,
add_cross_attention=True,
)
bert_config_dict = bert_config.to_dict()
del bert_config_dict["type_vocab_size"]
config = BertGenerationConfig(**bert_config_dict)
if is_encoder:
model = BertGenerationEncoder(config)
else:
model = BertGenerationDecoder(config)
print("Building PyTorch model from configuration: {}".format(str(config)))
# Load weights from tf checkpoint
load_tf_weights_in_bert_generation(
model,
tf_hub_path,
model_class="bert",
is_encoder_named_decoder=is_encoder_named_decoder,
is_encoder=is_encoder,
)
# Save pytorch-model
print("Save PyTorch model and config to {}".format(pytorch_dump_path))
model.save_pretrained(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])
if self.use_labels:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = BertGenerationConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_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,
is_decoder=False,
initializer_range=self.initializer_range,
return_dict=True,
)
return config, input_ids, input_mask, token_labels
def create_slt_transformer(input_vocab_size=1,
output_vocab_size=1,
**bert_params):
if input_vocab_size == 1:
print('WARNING: Input vocab size is 1')
if output_vocab_size == 1:
print('WARNING: Output vocab size is 1')
params = {
'vocab_size': input_vocab_size,
'hidden_size': 512,
'intermediate_size': 2048,
'max_position_embeddings': 500,
'num_attention_heads': 8,
'num_hidden_layers': 3,
'hidden_act': 'relu',
'type_vocab_size': 1,
'hidden_dropout_prob': 0.1,
'attention_probs_dropout_prob': 0.1
}
params.update(bert_params)
config = BertGenerationConfig(**params)
encoder = BertGenerationEncoder(config=config)
params['vocab_size'] = output_vocab_size
decoder_config = BertGenerationConfig(is_decoder=True,
add_cross_attention=True,
**params)
decoder = BertGenerationDecoder(config=decoder_config)
transformer = EncoderDecoderModel(encoder=encoder, decoder=decoder)
def count_parameters(m):
return sum(p.numel() for p in m.parameters() if p.requires_grad)
print(
f'The encoder has {count_parameters(encoder):,} trainable parameters')
print(
f'The decoder has {count_parameters(decoder):,} trainable parameters')
print(
f'The whole model has {count_parameters(transformer):,} trainable parameters'
)
return transformer
def get_config(self):
return BertGenerationConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_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,
is_decoder=False,
initializer_range=self.initializer_range,
)
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False)
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
)
config = BertGenerationConfig()
model = BertGenerationEncoder(config)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
