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| # Copyright 2019 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 Keras-based one-hot embedding layer.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.python.keras import keras_parameterized # pylint: disable=g-direct-tensorflow-import | |
| from official.nlp.modeling.layers import on_device_embedding | |
| # This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It | |
| # guarantees forward compatibility of this code for the V2 switchover. | |
| class OnDeviceEmbeddingTest(keras_parameterized.TestCase): | |
| def test_layer_creation(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, embedding_width=embedding_width) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # The output should be the same as the input, save that it has an extra | |
| # embedding_width dimension on the end. | |
| expected_output_shape = [None, sequence_length, embedding_width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| self.assertEqual(output_tensor.dtype, tf.float32) | |
| def test_layer_creation_with_mixed_precision(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16") | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, embedding_width=embedding_width, dtype=policy) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # The output should be the same as the input, save that it has an extra | |
| # embedding_width dimension on the end. | |
| expected_output_shape = [None, sequence_length, embedding_width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| self.assertEqual(output_tensor.dtype, tf.float16) | |
| def test_layer_invocation(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, embedding_width=embedding_width) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # Create a model from the test layer. | |
| model = tf.keras.Model(input_tensor, output_tensor) | |
| # Invoke the model on test data. We can't validate the output data itself | |
| # (the NN is too complex) but this will rule out structural runtime errors. | |
| batch_size = 3 | |
| input_data = np.random.randint( | |
| vocab_size, size=(batch_size, sequence_length)) | |
| output = model.predict(input_data) | |
| self.assertEqual(tf.float32, output.dtype) | |
| def test_layer_invocation_with_mixed_precision(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16") | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, embedding_width=embedding_width, | |
| dtype=policy) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # Create a model from the test layer. | |
| model = tf.keras.Model(input_tensor, output_tensor) | |
| # Invoke the model on test data. We can't validate the output data itself | |
| # (the NN is too complex) but this will rule out structural runtime errors. | |
| batch_size = 3 | |
| input_data = np.random.randint( | |
| vocab_size, size=(batch_size, sequence_length)) | |
| output = model.predict(input_data) | |
| self.assertEqual(tf.float16, output.dtype) | |
| def test_one_hot_layer_creation(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, | |
| embedding_width=embedding_width, | |
| use_one_hot=True) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # The output should be the same as the input, save that it has an extra | |
| # embedding_width dimension on the end. | |
| expected_output_shape = [None, sequence_length, embedding_width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| self.assertEqual(output_tensor.dtype, tf.float32) | |
| def test_one_hot_layer_creation_with_mixed_precision(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16") | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, | |
| embedding_width=embedding_width, | |
| dtype=policy, | |
| use_one_hot=True) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # The output should be the same as the input, save that it has an extra | |
| # embedding_width dimension on the end. | |
| expected_output_shape = [None, sequence_length, embedding_width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| self.assertEqual(output_tensor.dtype, tf.float16) | |
| def test_one_hot_layer_invocation(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, | |
| embedding_width=embedding_width, | |
| use_one_hot=True) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # Create a model from the test layer. | |
| model = tf.keras.Model(input_tensor, output_tensor) | |
| # Invoke the model on test data. We can't validate the output data itself | |
| # (the NN is too complex) but this will rule out structural runtime errors. | |
| batch_size = 3 | |
| input_data = np.random.randint( | |
| vocab_size, size=(batch_size, sequence_length)) | |
| output = model.predict(input_data) | |
| self.assertEqual(tf.float32, output.dtype) | |
| def test_one_hot_layer_invocation_with_mixed_precision(self): | |
| vocab_size = 31 | |
| embedding_width = 27 | |
| policy = tf.keras.mixed_precision.experimental.Policy("mixed_float16") | |
| test_layer = on_device_embedding.OnDeviceEmbedding( | |
| vocab_size=vocab_size, | |
| embedding_width=embedding_width, | |
| dtype=policy, | |
| use_one_hot=True) | |
| # Create a 2-dimensional input (the first dimension is implicit). | |
| sequence_length = 23 | |
| input_tensor = tf.keras.Input(shape=(sequence_length), dtype=tf.int32) | |
| output_tensor = test_layer(input_tensor) | |
| # Create a model from the test layer. | |
| model = tf.keras.Model(input_tensor, output_tensor) | |
| # Invoke the model on test data. We can't validate the output data itself | |
| # (the NN is too complex) but this will rule out structural runtime errors. | |
| batch_size = 3 | |
| input_data = np.random.randint( | |
| vocab_size, size=(batch_size, sequence_length)) | |
| output = model.predict(input_data) | |
| self.assertEqual(tf.float16, output.dtype) | |
| if __name__ == "__main__": | |
| tf.test.main() | |