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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()
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