tensorflow/models

# Copyright 2024 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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"""Test Transformer model helper methods."""

import tensorflow as tf, tf_keras

from official.legacy.transformer import model_utils

NEG_INF = -1e9


class ModelUtilsTest(tf.test.TestCase):

  def test_get_padding(self):
    x = tf.constant([[1, 0, 0, 0, 2], [3, 4, 0, 0, 0], [0, 5, 6, 0, 7]])
    padding = model_utils.get_padding(x, padding_value=0)

    self.assertAllEqual([[0, 1, 1, 1, 0], [0, 0, 1, 1, 1], [1, 0, 0, 1, 0]],
                        padding)

  def test_get_padding_bias(self):
    x = tf.constant([[1, 0, 0, 0, 2], [3, 4, 0, 0, 0], [0, 5, 6, 0, 7]])
    bias = model_utils.get_padding_bias(x)
    bias_shape = tf.shape(bias)
    flattened_bias = tf.reshape(bias, [3, 5])

    self.assertAllEqual(
        [[0, NEG_INF, NEG_INF, NEG_INF, 0], [0, 0, NEG_INF, NEG_INF, NEG_INF],
         [NEG_INF, 0, 0, NEG_INF, 0]], flattened_bias)
    self.assertAllEqual([3, 1, 1, 5], bias_shape)

  def test_get_decoder_self_attention_bias(self):
    length = 5
    bias = model_utils.get_decoder_self_attention_bias(length)

    self.assertAllEqual(
        [[[[0, NEG_INF, NEG_INF, NEG_INF, NEG_INF],
           [0, 0, NEG_INF, NEG_INF, NEG_INF], [0, 0, 0, NEG_INF, NEG_INF],
           [0, 0, 0, 0, NEG_INF], [0, 0, 0, 0, 0]]]], bias)


if __name__ == "__main__":
  tf.test.main()