official/nlp/modeling/layers/factorized_embedding_test.py

# 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,
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"""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()