def test_multiple_cls_outputs(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
hidden_size = 48
num_layers = 2
test_network = networks.BertEncoderV2(
vocab_size=vocab_size,
num_layers=num_layers,
hidden_size=hidden_size,
max_sequence_length=sequence_length)
bert_trainer_model = bert_pretrainer.BertPretrainerV2(
encoder_network=test_network,
classification_heads=[
layers.MultiClsHeads(inner_dim=5,
cls_list=[('foo', 2), ('bar', 3)])
])
num_token_predictions = 20
# Create a set of 2-dimensional inputs (the first dimension is implicit).
inputs = dict(input_word_ids=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32),
input_mask=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32),
input_type_ids=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32),
masked_lm_positions=tf.keras.Input(
shape=(num_token_predictions, ), dtype=tf.int32))
# Invoke the trainer model on the inputs. This causes the layer to be built.
outputs = bert_trainer_model(inputs)
self.assertEqual(outputs['foo'].shape.as_list(), [None, 2])
self.assertEqual(outputs['bar'].shape.as_list(), [None, 3])
def test_v2_serialize_deserialize(self):
"""Validate that the BERT trainer can be serialized and deserialized."""
# Build a transformer network to use within the BERT trainer.
test_network = networks.BertEncoderV2(vocab_size=100, num_layers=2)
# 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_pretrainer.BertPretrainerV2(
encoder_network=test_network)
# Create another BERT trainer via serialization and deserialization.
config = bert_trainer_model.get_config()
new_bert_trainer_model = bert_pretrainer.BertPretrainerV2.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_pretrainerv2(self, dict_outputs, return_all_encoder_outputs,
use_customized_masked_lm,
has_masked_lm_positions):
"""Validate that the Keras object can be created."""
# Build a transformer network to use within the BERT trainer.
vocab_size = 100
sequence_length = 512
hidden_size = 48
num_layers = 2
test_network = networks.BertEncoderV2(
vocab_size=vocab_size,
num_layers=num_layers,
hidden_size=hidden_size,
max_sequence_length=sequence_length)
_ = test_network(test_network.inputs)
# Create a BERT trainer with the created network.
if use_customized_masked_lm:
customized_masked_lm = layers.MaskedLM(
embedding_table=test_network.get_embedding_table())
else:
customized_masked_lm = None
bert_trainer_model = bert_pretrainer.BertPretrainerV2(
encoder_network=test_network,
customized_masked_lm=customized_masked_lm)
num_token_predictions = 20
# Create a set of 2-dimensional inputs (the first dimension is implicit).
inputs = dict(input_word_ids=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32),
input_mask=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32),
input_type_ids=tf.keras.Input(shape=(sequence_length, ),
dtype=tf.int32))
if has_masked_lm_positions:
inputs['masked_lm_positions'] = tf.keras.Input(
shape=(num_token_predictions, ), dtype=tf.int32)
# Invoke the trainer model on the inputs. This causes the layer to be built.
outputs = bert_trainer_model(inputs)
has_encoder_outputs = True # dict_outputs or return_all_encoder_outputs
expected_keys = ['sequence_output', 'pooled_output']
if has_encoder_outputs:
expected_keys.append('encoder_outputs')
if has_masked_lm_positions:
expected_keys.append('mlm_logits')
self.assertSameElements(outputs.keys(), expected_keys)
# Validate that the outputs are of the expected shape.
expected_lm_shape = [None, num_token_predictions, vocab_size]
if has_masked_lm_positions:
self.assertAllEqual(expected_lm_shape,
outputs['mlm_logits'].shape.as_list())
expected_sequence_output_shape = [None, sequence_length, hidden_size]
self.assertAllEqual(expected_sequence_output_shape,
outputs['sequence_output'].shape.as_list())
expected_pooled_output_shape = [None, hidden_size]
self.assertAllEqual(expected_pooled_output_shape,
outputs['pooled_output'].shape.as_list())
def build_encoder(config: EncoderConfig,
embedding_layer: Optional[tf.keras.layers.Layer] = None,
encoder_cls=None,
bypass_config: bool = False):
"""Instantiate a Transformer encoder network from EncoderConfig.
Args:
config: the one-of encoder config, which provides encoder parameters of a
chosen encoder.
embedding_layer: an external embedding layer passed to the encoder.
encoder_cls: an external encoder cls not included in the supported encoders,
usually used by gin.configurable.
bypass_config: whether to ignore config instance to create the object with
`encoder_cls`.
Returns:
An encoder instance.
"""
if bypass_config:
return encoder_cls()
encoder_type = config.type
encoder_cfg = config.get()
if encoder_cls and encoder_cls.__name__ == "EncoderScaffold":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True)
return encoder_cls(**kwargs)
if encoder_type == "any":
encoder = encoder_cfg.BUILDER(encoder_cfg)
if not isinstance(encoder,
(tf.Module, tf.keras.Model, tf.keras.layers.Layer)):
raise ValueError(
"The BUILDER returns an unexpected instance. The "
"`build_encoder` should returns a tf.Module, "
"tf.keras.Model or tf.keras.layers.Layer. However, "
f"we get {encoder.__class__}")
return encoder
if encoder_type == "mobilebert":
return networks.MobileBERTEncoder(
word_vocab_size=encoder_cfg.word_vocab_size,
word_embed_size=encoder_cfg.word_embed_size,
type_vocab_size=encoder_cfg.type_vocab_size,
max_sequence_length=encoder_cfg.max_sequence_length,
num_blocks=encoder_cfg.num_blocks,
hidden_size=encoder_cfg.hidden_size,
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_act_fn=encoder_cfg.hidden_activation,
hidden_dropout_prob=encoder_cfg.hidden_dropout_prob,
attention_probs_dropout_prob=encoder_cfg.
attention_probs_dropout_prob,
intra_bottleneck_size=encoder_cfg.intra_bottleneck_size,
initializer_range=encoder_cfg.initializer_range,
use_bottleneck_attention=encoder_cfg.use_bottleneck_attention,
key_query_shared_bottleneck=encoder_cfg.
key_query_shared_bottleneck,
num_feedforward_networks=encoder_cfg.num_feedforward_networks,
normalization_type=encoder_cfg.normalization_type,
classifier_activation=encoder_cfg.classifier_activation,
input_mask_dtype=encoder_cfg.input_mask_dtype)
if encoder_type == "albert":
return networks.AlbertEncoder(
vocab_size=encoder_cfg.vocab_size,
embedding_width=encoder_cfg.embedding_width,
hidden_size=encoder_cfg.hidden_size,
num_layers=encoder_cfg.num_layers,
num_attention_heads=encoder_cfg.num_attention_heads,
max_sequence_length=encoder_cfg.max_position_embeddings,
type_vocab_size=encoder_cfg.type_vocab_size,
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,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dict_outputs=True)
if encoder_type == "bigbird":
# TODO(frederickliu): Support use_gradient_checkpointing and update
# experiments to use the EncoderScaffold only.
if encoder_cfg.use_gradient_checkpointing:
return bigbird_encoder.BigBirdEncoder(
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,
num_rand_blocks=encoder_cfg.num_rand_blocks,
block_size=encoder_cfg.block_size,
max_position_embeddings=encoder_cfg.max_position_embeddings,
type_vocab_size=encoder_cfg.type_vocab_size,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
embedding_width=encoder_cfg.embedding_width,
use_gradient_checkpointing=encoder_cfg.
use_gradient_checkpointing)
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate)
attention_cfg = dict(
num_heads=encoder_cfg.num_attention_heads,
key_dim=int(encoder_cfg.hidden_size //
encoder_cfg.num_attention_heads),
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
max_rand_mask_length=encoder_cfg.max_position_embeddings,
num_rand_blocks=encoder_cfg.num_rand_blocks,
from_block_size=encoder_cfg.block_size,
to_block_size=encoder_cfg.block_size,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
norm_first=encoder_cfg.norm_first,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
attention_cls=layers.BigBirdAttention,
attention_cfg=attention_cfg)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cls=layers.TransformerScaffold,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
mask_cls=layers.BigBirdMasks,
mask_cfg=dict(block_size=encoder_cfg.block_size),
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=False,
dict_outputs=True,
layer_idx_as_attention_seed=True)
return networks.EncoderScaffold(**kwargs)
if encoder_type == "kernel":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate)
attention_cfg = dict(
num_heads=encoder_cfg.num_attention_heads,
key_dim=int(encoder_cfg.hidden_size //
encoder_cfg.num_attention_heads),
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
feature_transform=encoder_cfg.feature_transform,
num_random_features=encoder_cfg.num_random_features,
redraw=encoder_cfg.redraw,
is_short_seq=encoder_cfg.is_short_seq,
begin_kernel=encoder_cfg.begin_kernel,
scale=encoder_cfg.scale,
)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
intermediate_size=encoder_cfg.intermediate_size,
intermediate_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
norm_first=encoder_cfg.norm_first,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
attention_cls=layers.KernelAttention,
attention_cfg=attention_cfg)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cls=layers.TransformerScaffold,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
mask_cls=layers.KernelMask,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=False,
dict_outputs=True,
layer_idx_as_attention_seed=True)
return networks.EncoderScaffold(**kwargs)
if encoder_type == "xlnet":
return networks.XLNetBase(
vocab_size=encoder_cfg.vocab_size,
num_layers=encoder_cfg.num_layers,
hidden_size=encoder_cfg.hidden_size,
num_attention_heads=encoder_cfg.num_attention_heads,
head_size=encoder_cfg.head_size,
inner_size=encoder_cfg.inner_size,
dropout_rate=encoder_cfg.dropout_rate,
attention_dropout_rate=encoder_cfg.attention_dropout_rate,
attention_type=encoder_cfg.attention_type,
bi_data=encoder_cfg.bi_data,
two_stream=encoder_cfg.two_stream,
tie_attention_biases=encoder_cfg.tie_attention_biases,
memory_length=encoder_cfg.memory_length,
clamp_length=encoder_cfg.clamp_length,
reuse_length=encoder_cfg.reuse_length,
inner_activation=encoder_cfg.inner_activation,
use_cls_mask=encoder_cfg.use_cls_mask,
embedding_width=encoder_cfg.embedding_width,
initializer=tf.keras.initializers.RandomNormal(
stddev=encoder_cfg.initializer_range))
if encoder_type == "reuse":
embedding_cfg = dict(
vocab_size=encoder_cfg.vocab_size,
type_vocab_size=encoder_cfg.type_vocab_size,
hidden_size=encoder_cfg.hidden_size,
max_seq_length=encoder_cfg.max_position_embeddings,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
dropout_rate=encoder_cfg.dropout_rate)
hidden_cfg = dict(
num_attention_heads=encoder_cfg.num_attention_heads,
inner_dim=encoder_cfg.intermediate_size,
inner_activation=tf_utils.get_activation(
encoder_cfg.hidden_activation),
output_dropout=encoder_cfg.dropout_rate,
attention_dropout=encoder_cfg.attention_dropout_rate,
norm_first=encoder_cfg.norm_first,
kernel_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
reuse_attention=encoder_cfg.reuse_attention,
use_relative_pe=encoder_cfg.use_relative_pe,
pe_max_seq_length=encoder_cfg.pe_max_seq_length,
max_reuse_layer_idx=encoder_cfg.max_reuse_layer_idx)
kwargs = dict(
embedding_cfg=embedding_cfg,
hidden_cls=layers.ReuseTransformer,
hidden_cfg=hidden_cfg,
num_hidden_instances=encoder_cfg.num_layers,
pooled_output_dim=encoder_cfg.hidden_size,
pooler_layer_initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
return_all_layer_outputs=False,
dict_outputs=True,
feed_layer_idx=True,
recursive=True)
return networks.EncoderScaffold(**kwargs)
if encoder_type == "query_bert":
embedding_layer = layers.FactorizedEmbedding(
vocab_size=encoder_cfg.vocab_size,
embedding_width=encoder_cfg.embedding_size,
output_dim=encoder_cfg.hidden_size,
initializer=tf.keras.initializers.TruncatedNormal(
stddev=encoder_cfg.initializer_range),
name="word_embeddings")
return networks.BertEncoderV2(
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),
output_range=encoder_cfg.output_range,
embedding_layer=embedding_layer,
return_all_encoder_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True,
norm_first=encoder_cfg.norm_first)
bert_encoder_cls = networks.BertEncoder
if encoder_type == "bert_v2":
bert_encoder_cls = networks.BertEncoderV2
# Uses the default BERTEncoder configuration schema to create the encoder.
# If it does not match, please add a switch branch by the encoder type.
return bert_encoder_cls(
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),
output_range=encoder_cfg.output_range,
embedding_width=encoder_cfg.embedding_size,
embedding_layer=embedding_layer,
return_all_encoder_outputs=encoder_cfg.return_all_encoder_outputs,
dict_outputs=True,
norm_first=encoder_cfg.norm_first)