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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 FactorizedEmbedding layer."""
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import factorized_embedding
class FactorizedEmbeddingTest(tf.test.TestCase):
def test_layer_creation(self):
vocab_size = 31
embedding_width = 27
output_dim = 45
test_layer = factorized_embedding.FactorizedEmbedding(
vocab_size=vocab_size,
embedding_width=embedding_width,
output_dim=output_dim)
# 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, output_dim]
self.assertEqual(expected_output_shape, output_tensor.shape.as_list())
self.assertEqual(output_tensor.dtype, tf.float32)
def test_layer_invocation(self):
vocab_size = 31
embedding_width = 27
output_dim = 45
test_layer = factorized_embedding.FactorizedEmbedding(
vocab_size=vocab_size,
embedding_width=embedding_width,
output_dim=output_dim)
# 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)
if __name__ == "__main__":
tf.test.main()
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