def build_small_model(self, model_cfg):
encoder_cfg = model_cfg['encoder']['bert']
dataconf = self.task_config.train_data
encoder_network = small_encoder_lib.TransformerEncoder(
vocab_size=encoder_cfg['vocab_size'],
hidden_size=encoder_cfg['hidden_size'],
num_layers=encoder_cfg['num_layers'],
num_attention_heads=encoder_cfg['num_attention_heads'],
intermediate_size=encoder_cfg['intermediate_size'],
activation=tf_utils.get_activation(
encoder_cfg['hidden_activation']),
dropout_rate=encoder_cfg['dropout_rate'],
attention_dropout_rate=encoder_cfg['attention_dropout_rate'],
max_sequence_length=encoder_cfg['max_position_embeddings'],
type_vocab_size=encoder_cfg['type_vocab_size'],
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg['initializer_range']),
net2net_ratio=encoder_cfg['net2net_ratio'],
net2net_layers=encoder_cfg['net2net_layers'],
lightatt_layers=encoder_cfg['lightatt_layers'],
input_pool_name=encoder_cfg['input_pool_name'],
input_pool_size=encoder_cfg['input_pool_size'])
sequence_length = dataconf.seq_length
predict_length = dataconf.max_predictions_per_seq
dummy_inputs = dict(input_mask=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_positions=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_type_ids=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_word_ids=tf.zeros((1, sequence_length),
dtype=tf.int32),
masked_lm_positions=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_input_ids=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_segment_ids=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_lm_weights=tf.zeros((1, predict_length),
dtype=tf.float32))
_ = encoder_network(dummy_inputs)
if 'cls_heads' in model_cfg:
classification_heads = [
layers.ClassificationHead(**cfg)
for cfg in model_cfg['cls_heads']
]
else:
classification_heads = []
model = small_pretrainer.BertPretrainModel(
mlm_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg['initializer_range']),
mlm_activation=tf_utils.get_activation(
encoder_cfg['hidden_activation']),
encoder_network=encoder_network,
classification_heads=classification_heads)
_ = model(dummy_inputs)
return model
def test_bert_pretrainer(self):
"""Validate that the Keras object can be created."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
sequence_length = 512
test_network = transformer_encoder.TransformerEncoder(
vocab_size=vocab_size,
num_layers=2,
max_sequence_length=sequence_length)
# Create a set of 2-dimensional inputs (the first dimension is implicit).
predict_length = 2
dummy_inputs = dict(input_mask=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_positions=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_type_ids=tf.zeros((1, sequence_length),
dtype=tf.int32),
input_word_ids=tf.zeros((1, sequence_length),
dtype=tf.int32),
masked_lm_positions=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_input_ids=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_segment_ids=tf.zeros((1, predict_length),
dtype=tf.int32),
masked_lm_weights=tf.zeros((1, predict_length),
dtype=tf.float32))
_ = test_network(dummy_inputs)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_pretrain_model.BertPretrainModel(
test_network)
# Invoke the trainer model on the inputs. This causes the layer to be built.
outputs = bert_trainer_model(dummy_inputs)
# Validate that the outputs are of the expected shape.
expected_lm_shape = [1, predict_length, vocab_size]
self.assertAllEqual(expected_lm_shape,
outputs['mlm_logits'].shape.as_list())
