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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 mixing.py.""" | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| from official.nlp.modeling.layers import mixing | |
| class MixingTest(tf.test.TestCase): | |
| def test_base_mixing_layer(self): | |
| inputs = tf.random.uniform((3, 8, 16), | |
| minval=0, | |
| maxval=10, | |
| dtype=tf.float32) | |
| with self.assertRaisesRegex(NotImplementedError, "Abstract method"): | |
| _ = mixing.MixingLayer()(query=inputs, value=inputs) | |
| def test_fourier_layer(self): | |
| batch_size = 4 | |
| max_seq_length = 8 | |
| hidden_dim = 16 | |
| inputs = tf.random.uniform((batch_size, max_seq_length, hidden_dim), | |
| minval=0, | |
| maxval=10, | |
| dtype=tf.float32) | |
| outputs = mixing.FourierTransformLayer(use_fft=True)( | |
| query=inputs, value=inputs) | |
| self.assertEqual(outputs.shape, (batch_size, max_seq_length, hidden_dim)) | |
| def test_hartley_layer(self): | |
| batch_size = 3 | |
| max_seq_length = 16 | |
| hidden_dim = 4 | |
| inputs = tf.random.uniform((batch_size, max_seq_length, hidden_dim), | |
| minval=0, | |
| maxval=12, | |
| dtype=tf.float32) | |
| outputs = mixing.HartleyTransformLayer(use_fft=True)( | |
| query=inputs, value=inputs) | |
| self.assertEqual(outputs.shape, (batch_size, max_seq_length, hidden_dim)) | |
| def test_linear_mixing_layer(self): | |
| batch_size = 2 | |
| max_seq_length = 4 | |
| hidden_dim = 3 | |
| inputs = tf.ones((batch_size, max_seq_length, hidden_dim), dtype=tf.float32) | |
| outputs = mixing.LinearTransformLayer( | |
| kernel_initializer=tf_keras.initializers.Ones())( | |
| query=inputs, value=inputs) | |
| # hidden_dim * (max_seq_length * 1) = 12. | |
| expected_outputs = [ | |
| [ | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| ], | |
| [ | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| [12., 12., 12.], | |
| ], | |
| ] | |
| np.testing.assert_allclose(outputs, expected_outputs, rtol=1e-6, atol=1e-6) | |
| def test_pick_fourier_transform(self): | |
| # Ensure we don't hit an edge case which exceeds the fixed numerical error. | |
| tf.random.set_seed(1) | |
| np.random.seed(1) | |
| batch_size = 3 | |
| max_seq_length = 4 | |
| hidden_dim = 8 | |
| fft = mixing._pick_fourier_transform( | |
| use_fft=True, max_seq_length=max_seq_length, hidden_dim=hidden_dim) | |
| dft_matmul = mixing._pick_fourier_transform( | |
| use_fft=False, max_seq_length=max_seq_length, hidden_dim=hidden_dim) | |
| inputs = tf.random.uniform([batch_size, max_seq_length, hidden_dim]) | |
| inputs = tf.cast(inputs, tf.complex64) | |
| np.testing.assert_allclose( | |
| fft(inputs), dft_matmul(inputs), rtol=1e-6, atol=1e-6) | |
| if __name__ == "__main__": | |
| tf.test.main() | |