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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 positional 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 position_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 PositionEmbeddingLayerTest(keras_parameterized.TestCase): | |
| def test_static_layer_output_shape(self): | |
| test_layer = position_embedding.PositionEmbedding() | |
| # Create a 3-dimensional input (the first dimension is implicit). | |
| sequence_length = 21 | |
| width = 30 | |
| input_tensor = tf.keras.Input(shape=(sequence_length, width)) | |
| output_tensor = test_layer(input_tensor) | |
| # When using static positional embedding shapes, the output is expected | |
| # to be the same as the input shape in all dimensions save batch. | |
| expected_output_shape = [None, sequence_length, width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| # The default output dtype for this layer should be tf.float32. | |
| self.assertEqual(tf.float32, output_tensor.dtype) | |
| def test_float16_dtype(self): | |
| test_layer = position_embedding.PositionEmbedding(dtype="float16") | |
| # Create a 3-dimensional input (the first dimension is implicit). | |
| sequence_length = 21 | |
| width = 30 | |
| input_tensor = tf.keras.Input(shape=(sequence_length, width)) | |
| output_tensor = test_layer(input_tensor) | |
| # When using static positional embedding shapes, the output is expected | |
| # to be the same as the input shape in all dimensions save batch. | |
| expected_output_shape = [None, sequence_length, width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| # The default output dtype for this layer should be tf.float32. | |
| self.assertEqual(tf.float16, output_tensor.dtype) | |
| def test_dynamic_layer_output_shape(self): | |
| max_sequence_length = 40 | |
| test_layer = position_embedding.PositionEmbedding( | |
| use_dynamic_slicing=True, max_sequence_length=max_sequence_length) | |
| # Create a 3-dimensional input (the first dimension is implicit). | |
| width = 30 | |
| input_tensor = tf.keras.Input(shape=(None, width)) | |
| output_tensor = test_layer(input_tensor) | |
| # When using dynamic positional embedding shapes, the output is expected | |
| # to be the same as the input shape in all dimensions - but may be None if | |
| # the input shape is None there. | |
| expected_output_shape = [None, None, width] | |
| self.assertEqual(expected_output_shape, output_tensor.shape.as_list()) | |
| def test_dynamic_layer_slicing(self): | |
| max_sequence_length = 40 | |
| test_layer = position_embedding.PositionEmbedding( | |
| use_dynamic_slicing=True, max_sequence_length=max_sequence_length) | |
| # Create a 3-dimensional input (the first dimension is implicit). | |
| width = 30 | |
| input_tensor = tf.keras.Input(shape=(None, width)) | |
| output_tensor = test_layer(input_tensor) | |
| model = tf.keras.Model(input_tensor, output_tensor) | |
| # Create input data that is shorter than max_sequence_length, which should | |
| # trigger a down-slice. | |
| input_length = 17 | |
| # Note: This test explicitly uses a batch size of 1. This is to get around | |
| # Keras' restriction on Model invocations: inputs are expected to have the | |
| # same batch cardinality as outputs. In practice, this layer should be used | |
| # inside a model, where it can be projected when added to another tensor. | |
| input_data = np.ones((1, input_length, width)) | |
| output_data = model.predict(input_data) | |
| self.assertAllEqual([1, input_length, width], output_data.shape) | |
| def test_relative_tensor_input(self): | |
| hidden_size = 8 | |
| test_layer = position_embedding.RelativePositionEmbedding( | |
| hidden_size=hidden_size) | |
| # create a 3-dimensional input for test_layer to infer length as 1. | |
| input_tensor = tf.constant([[[0] * hidden_size]]) | |
| output_tensor = test_layer(input_tensor) | |
| # expected output is the theoretical result of the input based on | |
| # sine cosine relative position embedding formula. | |
| expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]]) | |
| self.assertAllEqual(output_tensor, expected_output_tensor) | |
| def test_relative_length_input(self): | |
| hidden_size = 8 | |
| # When we do not have tensor as input, we explicitly specify length | |
| # value when initializing test_layer. | |
| test_layer = position_embedding.RelativePositionEmbedding( | |
| hidden_size=hidden_size) | |
| input_tensor = None | |
| output_tensor = test_layer(input_tensor, length=1) | |
| # expected output is the theoretical result of the input based on | |
| # sine cosine relative position embedding formula. | |
| expected_output_tensor = tf.constant([[0, 0, 0, 0, 1, 1, 1, 1]]) | |
| self.assertAllEqual(output_tensor, expected_output_tensor) | |
| if __name__ == "__main__": | |
| tf.test.main() | |