official/nlp/modeling/networks/bert_encoder_test.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for transformer-based bert encoder network."""
# Import libraries
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.networks import bert_encoder
class BertEncoderTest(tf.test.TestCase, parameterized.TestCase):
def tearDown(self):
super(BertEncoderTest, self).tearDown()
tf_keras.mixed_precision.set_global_policy("float32")
@parameterized.named_parameters(
("encoder_v2", bert_encoder.BertEncoderV2),
("encoder_v1", bert_encoder.BertEncoder),
)
def test_dict_outputs_network_creation(self, encoder_cls):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
if encoder_cls is bert_encoder.BertEncoderV2:
kwargs = {}
else:
kwargs = dict(dict_outputs=True)
test_network = encoder_cls(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
**kwargs)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
self.assertIsInstance(test_network.transformer_layers, list)
self.assertLen(test_network.transformer_layers, 3)
self.assertIsInstance(test_network.pooler_layer, tf_keras.layers.Dense)
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
@parameterized.named_parameters(
("encoder_v2", bert_encoder.BertEncoderV2),
("encoder_v1", bert_encoder.BertEncoder),
)
def test_dict_outputs_all_encoder_outputs_network_creation(self, encoder_cls):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
test_network = encoder_cls(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
dict_outputs=True)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
all_encoder_outputs = dict_outputs["encoder_outputs"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertLen(all_encoder_outputs, 3)
for data in all_encoder_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
@parameterized.named_parameters(
("encoder_v2", bert_encoder.BertEncoderV2),
("encoder_v1", bert_encoder.BertEncoder),
)
def test_dict_outputs_network_creation_return_attention_scores(
self, encoder_cls):
hidden_size = 32
sequence_length = 21
num_attention_heads = 5
num_layers = 3
# Create a small BertEncoder for testing.
test_network = encoder_cls(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
num_layers=num_layers,
return_attention_scores=True,
dict_outputs=True)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
all_attention_outputs = dict_outputs["attention_scores"]
expected_data_shape = [
None, num_attention_heads, sequence_length, sequence_length
]
self.assertLen(all_attention_outputs, num_layers)
for data in all_attention_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_attention_outputs[-1].dtype)
@parameterized.named_parameters(
("encoder_v2", bert_encoder.BertEncoderV2),
("encoder_v1", bert_encoder.BertEncoder),
)
def test_dict_outputs_network_creation_return_word_embeddings(
self, encoder_cls):
hidden_size = 32
sequence_length = 21
num_attention_heads = 5
num_layers = 3
# Create a small BertEncoder for testing.
test_network = encoder_cls(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
num_layers=num_layers,
return_word_embeddings=True,
dict_outputs=True)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
word_embeddings = dict_outputs["word_embeddings"]
expected_data_shape = [None, sequence_length, hidden_size]
self.assertAllEqual(expected_data_shape, word_embeddings.shape)
# The default output dtype is float32.
self.assertAllEqual(tf.float32, word_embeddings[-1].dtype)
@parameterized.named_parameters(
("encoder_v2", bert_encoder.BertEncoderV2),
("encoder_v1", bert_encoder.BertEncoder),
)
def test_dict_outputs_network_creation_with_float16_dtype(self, encoder_cls):
hidden_size = 32
sequence_length = 21
tf_keras.mixed_precision.set_global_policy("mixed_float16")
# Create a small BertEncoder for testing.
test_network = encoder_cls(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
dict_outputs=True)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# If float_dtype is set to float16, the data output is float32 (from a layer
# norm) and pool output should be float16.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float16, pooled.dtype)
@parameterized.named_parameters(
("all_sequence_encoder_v1", bert_encoder.BertEncoder, None, 21),
("output_range_encoder_v1", bert_encoder.BertEncoder, 1, 1),
("all_sequence_encoder_v2", bert_encoder.BertEncoderV2, None, 21),
("output_range_encoder_v2", bert_encoder.BertEncoderV2, 1, 1),
)
def test_dict_outputs_network_invocation(
self, encoder_cls, output_range, out_seq_len):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
# Create a small BertEncoder for testing.
test_network = encoder_cls(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=output_range,
dict_outputs=True)
# Create the inputs (note that 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)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
# Create a model based off of this network:
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[1], out_seq_len)
# Creates a BertEncoder with max_sequence_length != sequence_length
max_sequence_length = 128
test_network = encoder_cls(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
dict_outputs=True)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[1], sequence_length)
# Creates a BertEncoder with embedding_width != hidden_size
test_network = encoder_cls(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
embedding_width=16,
dict_outputs=True)
dict_outputs = test_network(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
data = dict_outputs["sequence_output"]
pooled = dict_outputs["pooled_output"]
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[-1], hidden_size)
self.assertTrue(hasattr(test_network, "_embedding_projection"))
def test_embeddings_as_inputs(self):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoderV2(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3)
# Create the inputs (note that 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)
test_network.build(
dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
embeddings = test_network.get_embedding_layer()(word_ids)
# Calls with the embeddings.
dict_outputs = test_network(
dict(
input_word_embeddings=embeddings,
input_mask=mask,
input_type_ids=type_ids))
all_encoder_outputs = dict_outputs["encoder_outputs"]
pooled = dict_outputs["pooled_output"]
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertLen(all_encoder_outputs, 3)
for data in all_encoder_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
vocab_size=100,
hidden_size=32,
num_layers=3,
num_attention_heads=2,
max_sequence_length=21,
type_vocab_size=12,
inner_dim=1223,
inner_activation="relu",
output_dropout=0.05,
attention_dropout=0.22,
initializer="glorot_uniform",
output_range=-1,
embedding_width=16,
embedding_layer=None,
norm_first=False)
with self.subTest("BertEncoder"):
network = bert_encoder.BertEncoder(**kwargs)
# Validate that the config can be forced to JSON.
_ = network.to_json()
# Tests model saving/loading with SavedModel.
model_path = self.get_temp_dir() + "/model"
network.save(model_path)
_ = tf_keras.models.load_model(model_path)
# Test model saving/loading with Keras V3.
keras_path = self.get_temp_dir() + "/model.keras"
network.save(keras_path)
_ = tf_keras.models.load_model(keras_path)
with self.subTest("BertEncoderV2"):
new_net = bert_encoder.BertEncoderV2(**kwargs)
inputs = new_net.inputs
outputs = new_net(inputs)
network_v2 = tf_keras.Model(inputs=inputs, outputs=outputs)
# Validate that the config can be forced to JSON.
_ = network_v2.to_json()
# Tests model saving/loading with SavedModel.
model_path = self.get_temp_dir() + "/v2_model"
network_v2.save(model_path)
_ = tf_keras.models.load_model(model_path)
# Test model saving/loading with Keras V3.
keras_path = self.get_temp_dir() + "/v2_model.keras"
network_v2.save(keras_path)
_ = tf_keras.models.load_model(keras_path)
def test_network_creation(self):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
self.assertIsInstance(test_network.transformer_layers, list)
self.assertLen(test_network.transformer_layers, 3)
self.assertIsInstance(test_network.pooler_layer, tf_keras.layers.Dense)
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
test_network_dict = bert_encoder.BertEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
dict_outputs=True)
# Create the inputs (note that the first dimension is implicit).
inputs = dict(
input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids)
_ = test_network_dict(inputs)
test_network_dict.set_weights(test_network.get_weights())
batch_size = 2
vocab_size = 100
num_types = 2
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
list_outputs = test_network([word_id_data, mask_data, type_id_data])
dict_outputs = test_network_dict(
dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data))
self.assertAllEqual(list_outputs[0], dict_outputs["sequence_output"])
self.assertAllEqual(list_outputs[1], dict_outputs["pooled_output"])
def test_all_encoder_outputs_network_creation(self):
hidden_size = 32
sequence_length = 21
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
return_all_encoder_outputs=True)
# Create the inputs (note that 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)
all_encoder_outputs, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertLen(all_encoder_outputs, 3)
for data in all_encoder_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
self.assertAllEqual(tf.float32, pooled.dtype)
def test_attention_scores_output_network_creation(self):
hidden_size = 32
sequence_length = 21
num_attention_heads = 5
num_layers = 3
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=num_attention_heads,
num_layers=num_layers,
return_attention_scores=True)
# Create the inputs (note that 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)
_, _, all_attention_outputs = test_network([word_ids, mask, type_ids])
expected_data_shape = [
None, num_attention_heads, sequence_length, sequence_length
]
self.assertLen(all_attention_outputs, num_layers)
for data in all_attention_outputs:
self.assertAllEqual(expected_data_shape, data.shape.as_list())
# The default output dtype is float32.
self.assertAllEqual(tf.float32, all_attention_outputs[-1].dtype)
def test_network_creation_with_float16_dtype(self):
hidden_size = 32
sequence_length = 21
tf_keras.mixed_precision.set_global_policy("mixed_float16")
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoder(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
expected_data_shape = [None, sequence_length, hidden_size]
expected_pooled_shape = [None, hidden_size]
self.assertAllEqual(expected_data_shape, data.shape.as_list())
self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())
# If float_dtype is set to float16, the data output is float32 (from a layer
# norm) and pool output should be float16.
self.assertAllEqual(tf.float32, data.dtype)
self.assertAllEqual(tf.float16, pooled.dtype)
@parameterized.named_parameters(
("all_sequence", None, 21),
("output_range", 1, 1),
)
def test_network_invocation(self, output_range, out_seq_len):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
# Create a small BertEncoder for testing.
test_network = bert_encoder.BertEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=output_range)
# Create the inputs (note that 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)
data, pooled = test_network([word_ids, mask, type_ids])
# Create a model based off of this network:
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
# Invoke the model. We can't validate the output data here (the model is too
# complex) but this will catch structural runtime errors.
batch_size = 3
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[1], out_seq_len)
# Creates a BertEncoder with max_sequence_length != sequence_length
max_sequence_length = 128
test_network = bert_encoder.BertEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types)
data, pooled = test_network([word_ids, mask, type_ids])
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[1], sequence_length)
# Creates a BertEncoder with embedding_width != hidden_size
test_network = bert_encoder.BertEncoder(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
embedding_width=16)
data, pooled = test_network([word_ids, mask, type_ids])
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
outputs = model.predict([word_id_data, mask_data, type_id_data])
self.assertEqual(outputs[0].shape[-1], hidden_size)
self.assertTrue(hasattr(test_network, "_embedding_projection"))
class BertEncoderV2CompatibilityTest(tf.test.TestCase):
def tearDown(self):
super().tearDown()
tf_keras.mixed_precision.set_global_policy("float32")
def test_weights_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types)
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
data = dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
# Create small BertEncoders for testing.
new_net = bert_encoder.BertEncoderV2(**kwargs)
_ = new_net(data)
kwargs["dict_outputs"] = True
old_net = bert_encoder.BertEncoder(**kwargs)
_ = old_net(data)
new_net._embedding_layer.set_weights(old_net._embedding_layer.get_weights())
new_net._position_embedding_layer.set_weights(
old_net._position_embedding_layer.get_weights())
new_net._type_embedding_layer.set_weights(
old_net._type_embedding_layer.get_weights())
new_net._embedding_norm_layer.set_weights(
old_net._embedding_norm_layer.get_weights())
# embedding_dropout has no weights.
if hasattr(old_net, "_embedding_projection"):
new_net._embedding_projection.set_weights(
old_net._embedding_projection.get_weights())
# attention_mask_layer has no weights.
new_net._pooler_layer.set_weights(old_net._pooler_layer.get_weights())
for otl, ntl in zip(old_net._transformer_layers,
new_net._transformer_layers):
ntl.set_weights(otl.get_weights())
def check_output_close(data, net1, net2):
output1 = net1(data)
output2 = net2(data)
for key in output1:
self.assertAllClose(output1[key], output2[key])
check_output_close(data, old_net, new_net)
def test_checkpoint_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types)
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
data = dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
kwargs["dict_outputs"] = True
old_net = bert_encoder.BertEncoder(**kwargs)
old_net_outputs = old_net(data)
ckpt = tf.train.Checkpoint(net=old_net)
path = ckpt.save(self.get_temp_dir())
del kwargs["dict_outputs"]
new_net = bert_encoder.BertEncoderV2(**kwargs)
new_ckpt = tf.train.Checkpoint(net=new_net)
status = new_ckpt.restore(path)
status.assert_existing_objects_matched()
# assert_consumed will fail because the old model has redundant nodes.
new_net_outputs = new_net(data)
self.assertAllEqual(old_net_outputs.keys(), new_net_outputs.keys())
for key in old_net_outputs:
self.assertAllClose(old_net_outputs[key], new_net_outputs[key])
def test_keras_model_checkpoint_forward_compatible(self):
batch_size = 3
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
kwargs = dict(
vocab_size=vocab_size,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3,
type_vocab_size=num_types,
output_range=None)
word_id_data = np.random.randint(
vocab_size, size=(batch_size, sequence_length))
mask_data = np.random.randint(2, size=(batch_size, sequence_length))
type_id_data = np.random.randint(
num_types, size=(batch_size, sequence_length))
data = dict(
input_word_ids=word_id_data,
input_mask=mask_data,
input_type_ids=type_id_data)
kwargs["dict_outputs"] = True
old_net = bert_encoder.BertEncoder(**kwargs)
inputs = old_net.inputs
outputs = old_net(inputs)
old_model = tf_keras.Model(inputs=inputs, outputs=outputs)
old_model_outputs = old_model(data)
ckpt = tf.train.Checkpoint(net=old_model)
path = ckpt.save(self.get_temp_dir())
del kwargs["dict_outputs"]
new_net = bert_encoder.BertEncoderV2(**kwargs)
inputs = new_net.inputs
outputs = new_net(inputs)
new_model = tf_keras.Model(inputs=inputs, outputs=outputs)
new_ckpt = tf.train.Checkpoint(net=new_model)
status = new_ckpt.restore(path)
status.assert_existing_objects_matched()
new_model_outputs = new_model(data)
self.assertAllEqual(old_model_outputs.keys(), new_model_outputs.keys())
for key in old_model_outputs:
self.assertAllClose(old_model_outputs[key], new_model_outputs[key])
if __name__ == "__main__":
tf.test.main()