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# Copyright 2023 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 ALBERT transformer-based text encoder network."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.networks import albert_encoder
class AlbertEncoderTest(tf.test.TestCase, parameterized.TestCase):
def tearDown(self):
super(AlbertEncoderTest, self).tearDown()
tf_keras.mixed_precision.set_global_policy("float32")
@parameterized.named_parameters(
dict(testcase_name="default", expected_dtype=tf.float32),
dict(testcase_name="with_float16_dtype", expected_dtype=tf.float16),
)
def test_network_creation(self, expected_dtype):
hidden_size = 32
sequence_length = 21
kwargs = dict(
vocab_size=100,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=3)
if expected_dtype == tf.float16:
tf_keras.mixed_precision.set_global_policy("mixed_float16")
# Create a small TransformerEncoder for testing.
test_network = albert_encoder.AlbertEncoder(**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)
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.assertEqual(tf.float32, data.dtype)
self.assertEqual(expected_dtype, pooled.dtype)
# ALBERT has additonal 'embedding_hidden_mapping_in' weights and
# it shares transformer weights.
self.assertNotEmpty(
[x for x in test_network.weights if "embedding_projection/" in x.name])
self.assertNotEmpty(
[x for x in test_network.weights if "transformer/" in x.name])
self.assertEmpty(
[x for x in test_network.weights if "transformer/layer" in x.name])
def test_network_invocation(self):
hidden_size = 32
sequence_length = 21
vocab_size = 57
num_types = 7
num_layers = 3
# Create a small TransformerEncoder for testing.
test_network = albert_encoder.AlbertEncoder(
vocab_size=vocab_size,
embedding_width=8,
hidden_size=hidden_size,
num_attention_heads=2,
num_layers=num_layers,
type_vocab_size=num_types)
# 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))
list_outputs = model.predict([word_id_data, mask_data, type_id_data])
# Creates a TransformerEncoder with max_sequence_length != sequence_length
max_sequence_length = 128
test_network = albert_encoder.AlbertEncoder(
vocab_size=vocab_size,
embedding_width=8,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=num_layers,
type_vocab_size=num_types)
model = tf_keras.Model([word_ids, mask, type_ids], [data, pooled])
_ = model.predict([word_id_data, mask_data, type_id_data])
# Tests dictionary outputs.
test_network_dict = albert_encoder.AlbertEncoder(
vocab_size=vocab_size,
embedding_width=8,
hidden_size=hidden_size,
max_sequence_length=max_sequence_length,
num_attention_heads=2,
num_layers=num_layers,
type_vocab_size=num_types,
dict_outputs=True)
_ = test_network_dict([word_ids, mask, type_ids])
test_network_dict.set_weights(test_network.get_weights())
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"])
self.assertLen(dict_outputs["pooled_output"], num_layers)
def test_serialize_deserialize(self):
tf_keras.mixed_precision.set_global_policy("mixed_float16")
# Create a network object that sets all of its config options.
kwargs = dict(
vocab_size=100,
embedding_width=8,
hidden_size=32,
num_layers=3,
num_attention_heads=2,
max_sequence_length=21,
type_vocab_size=12,
intermediate_size=1223,
activation="relu",
dropout_rate=0.05,
attention_dropout_rate=0.22,
initializer="glorot_uniform")
network = albert_encoder.AlbertEncoder(**kwargs)
expected_config = dict(kwargs)
expected_config["activation"] = tf_keras.activations.serialize(
tf_keras.activations.get(expected_config["activation"]))
expected_config["initializer"] = tf_keras.initializers.serialize(
tf_keras.initializers.get(expected_config["initializer"]))
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = (
albert_encoder.AlbertEncoder.from_config(
network.get_config()))
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
if __name__ == "__main__":
tf.test.main()
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