def squad_model(bert_config,
max_seq_length,
initializer=None,
hub_module_url=None,
hub_module_trainable=True):
"""Returns BERT Squad model along with core BERT model to import weights.
Args:
bert_config: BertConfig, the config defines the core Bert model.
max_seq_length: integer, the maximum input sequence length.
initializer: Initializer for the final dense layer in the span labeler.
Defaulted to TruncatedNormal initializer.
hub_module_url: TF-Hub path/url to Bert module.
hub_module_trainable: True to finetune layers in the hub module.
Returns:
A tuple of (1) keras model that outputs start logits and end logits and
(2) the core BERT transformer encoder.
"""
if initializer is None:
initializer = tf.keras.initializers.TruncatedNormal(
stddev=bert_config.initializer_range)
if not hub_module_url:
bert_encoder = get_transformer_encoder(bert_config, max_seq_length)
return bert_span_labeler.BertSpanLabeler(
network=bert_encoder, initializer=initializer), bert_encoder
input_word_ids = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_word_ids')
input_mask = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_mask')
input_type_ids = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_type_ids')
core_model = hub.KerasLayer(hub_module_url, trainable=hub_module_trainable)
pooled_output, sequence_output = core_model(
[input_word_ids, input_mask, input_type_ids])
bert_encoder = tf.keras.Model(
inputs={
'input_word_ids': input_word_ids,
'input_mask': input_mask,
'input_type_ids': input_type_ids,
},
outputs=[sequence_output, pooled_output],
name='core_model')
return bert_span_labeler.BertSpanLabeler(
network=bert_encoder, initializer=initializer), bert_encoder
def squad_model(bert_config,
max_seq_length,
float_type,
initializer=None,
hub_module_url=None):
"""Returns BERT Squad model along with core BERT model to import weights.
Args:
bert_config: BertConfig, the config defines the core Bert model.
max_seq_length: integer, the maximum input sequence length.
float_type: tf.dtype, tf.float32 or tf.bfloat16.
initializer: Initializer for the final dense layer in the span labeler.
Defaulted to TruncatedNormal initializer.
hub_module_url: TF-Hub path/url to Bert module.
Returns:
A tuple of (1) keras model that outputs start logits and end logits and
(2) the core BERT transformer encoder.
"""
if initializer is None:
initializer = tf.keras.initializers.TruncatedNormal(
stddev=bert_config.initializer_range)
if not hub_module_url:
bert_encoder = _get_transformer_encoder(bert_config, max_seq_length,
float_type)
return bert_span_labeler.BertSpanLabeler(
network=bert_encoder, initializer=initializer), bert_encoder
input_word_ids = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype=tf.int32,
name='input_word_ids')
input_mask = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype=tf.int32,
name='input_mask')
input_type_ids = tf.keras.layers.Input(shape=(max_seq_length, ),
dtype=tf.int32,
name='input_type_ids')
core_model = hub.KerasLayer(hub_module_url, trainable=True)
_, sequence_output = core_model(
[input_word_ids, input_mask, input_type_ids])
# Sets the shape manually due to a bug in TF shape inference.
# TODO(hongkuny): remove this once shape inference is correct.
sequence_output.set_shape((None, max_seq_length, bert_config.hidden_size))
squad_logits_layer = BertSquadLogitsLayer(initializer=initializer,
float_type=float_type,
name='squad_logits')
start_logits, end_logits = squad_logits_layer(sequence_output)
squad = tf.keras.Model(inputs={
'input_word_ids': input_word_ids,
'input_mask': input_mask,
'input_type_ids': input_type_ids,
},
outputs=[start_logits, end_logits],
name='squad_model')
return squad, core_model
def test_bert_trainer_tensor_call(self):
"""Validate that the Keras object can be invoked."""
# Build a transformer network to use within the BERT trainer. (Here, we use
# a short sequence_length for convenience.)
test_network = networks.TransformerEncoder(
vocab_size=100, num_layers=2, sequence_length=2)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create a set of 2-dimensional data tensors to feed into the model.
word_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
mask = tf.constant([[1, 1], [1, 0]], dtype=tf.int32)
type_ids = tf.constant([[1, 1], [2, 2]], dtype=tf.int32)
# Invoke the trainer model on the tensors. In Eager mode, this does the
# actual calculation. (We can't validate the outputs, since the network is
# too complex: this simply ensures we're not hitting runtime errors.)
_ = bert_trainer_model([word_ids, mask, type_ids])
def test_bert_trainer_named_compilation(self):
"""Validate compilation using explicit output names."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
sequence_length = 512
test_network = networks.TransformerEncoder(
vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Attempt to compile the model using a string-keyed dict of output names to
# loss functions. This will validate that the outputs are named as we
# expect.
bert_trainer_model.compile(
optimizer='sgd',
loss={
'start_positions': 'mse',
'end_positions': 'mse'
})
def test_serialize_deserialize(self):
"""Validate that the BERT trainer can be serialized and deserialized."""
# Build a transformer network to use within the BERT trainer. (Here, we use
# a short sequence_length for convenience.)
test_network = networks.TransformerEncoder(
vocab_size=100, num_layers=2, sequence_length=5)
# Create a BERT trainer with the created network. (Note that all the args
# are different, so we can catch any serialization mismatches.)
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create another BERT trainer via serialization and deserialization.
config = bert_trainer_model.get_config()
new_bert_trainer_model = bert_span_labeler.BertSpanLabeler.from_config(
config)
# Validate that the config can be forced to JSON.
_ = new_bert_trainer_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(bert_trainer_model.get_config(),
new_bert_trainer_model.get_config())
def test_bert_trainer(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 = networks.TransformerEncoder(
vocab_size=vocab_size, num_layers=2, sequence_length=sequence_length)
# Create a BERT trainer with the created network.
bert_trainer_model = bert_span_labeler.BertSpanLabeler(test_network)
# Create a set of 2-dimensional inputs (the first dimension is implicit).
word_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
mask = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
type_ids = tf.keras.Input(shape=(sequence_length,), dtype=tf.int32)
# Invoke the trainer model on the inputs. This causes the layer to be built.
cls_outs = bert_trainer_model([word_ids, mask, type_ids])
# Validate that there are 2 outputs are of the expected shape.
self.assertEqual(2, len(cls_outs))
expected_shape = [None, sequence_length]
for out in cls_outs:
self.assertAllEqual(expected_shape, out.shape.as_list())
def squad_model(bert_config,
max_seq_length,
float_type,
initializer=None,
hub_module_url=None,
use_keras_bert=False):
"""Returns BERT Squad model along with core BERT model to import weights.
Args:
bert_config: BertConfig, the config defines the core Bert model.
max_seq_length: integer, the maximum input sequence length.
float_type: tf.dtype, tf.float32 or tf.bfloat16.
initializer: Initializer for weights in BertSquadLogitsLayer.
hub_module_url: TF-Hub path/url to Bert module.
use_keras_bert: Whether to use keras BERT. Note that when the above
'hub_module_url' is specified, 'use_keras_bert' cannot be True.
Returns:
A tuple of (1) keras model that outputs start logits and end logits and
(2) the core BERT transformer encoder.
Raises:
ValueError: When 'hub_module_url' is specified and 'use_keras_bert' is True.
"""
if hub_module_url and use_keras_bert:
raise ValueError(
'Cannot use hub_module_url and keras BERT at the same time.')
if use_keras_bert:
bert_encoder = _get_transformer_encoder(bert_config, max_seq_length)
return bert_span_labeler.BertSpanLabeler(
network=bert_encoder), bert_encoder
input_word_ids = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_word_ids')
input_mask = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_mask')
input_type_ids = tf.keras.layers.Input(
shape=(max_seq_length,), dtype=tf.int32, name='input_type_ids')
if hub_module_url:
core_model = hub.KerasLayer(hub_module_url, trainable=True)
_, sequence_output = core_model(
[input_word_ids, input_mask, input_type_ids])
# Sets the shape manually due to a bug in TF shape inference.
# TODO(hongkuny): remove this once shape inference is correct.
sequence_output.set_shape((None, max_seq_length, bert_config.hidden_size))
else:
core_model = modeling.get_bert_model(
input_word_ids,
input_mask,
input_type_ids,
config=bert_config,
name='bert_model',
float_type=float_type)
# `BertSquadModel` only uses the sequnce_output which
# has dimensionality (batch_size, sequence_length, num_hidden).
sequence_output = core_model.outputs[1]
if initializer is None:
initializer = tf.keras.initializers.TruncatedNormal(
stddev=bert_config.initializer_range)
squad_logits_layer = BertSquadLogitsLayer(
initializer=initializer, float_type=float_type, name='squad_logits')
start_logits, end_logits = squad_logits_layer(sequence_output)
squad = tf.keras.Model(
inputs={
'input_word_ids': input_word_ids,
'input_mask': input_mask,
'input_type_ids': input_type_ids,
},
outputs=[start_logits, end_logits],
name='squad_model')
return squad, core_model
