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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. | |
| """Test decoding utility methods.""" | |
| import abc | |
| import tensorflow as tf, tf_keras | |
| from official.nlp.modeling.ops import decoding_module | |
| def length_normalization(length, dtype): | |
| """Return length normalization factor.""" | |
| return tf.pow(((5. + tf.cast(length, dtype)) / 6.), 0.0) | |
| class TestSubclass(decoding_module.DecodingModule, metaclass=abc.ABCMeta): | |
| def __init__(self, | |
| length_normalization_fn=length_normalization, | |
| extra_cache_output=True, | |
| dtype=tf.float32): | |
| super(TestSubclass, self).__init__( | |
| length_normalization_fn=length_normalization, dtype=dtype) | |
| def _create_initial_state(self, initial_ids, initial_cache, batch_size): | |
| pass | |
| def _grow_alive_seq(self, state, batch_size): | |
| pass | |
| def _process_finished_state(self, finished_state): | |
| pass | |
| def _get_new_finished_state(self, state, new_seq, new_log_probs, | |
| new_finished_flags, batch_size): | |
| pass | |
| def _finished_flags(self, topk_ids, state): | |
| pass | |
| def _continue_search(self, state): | |
| pass | |
| def _get_new_alive_state(self, new_seq, new_log_probs, new_finished_flags, | |
| new_cache): | |
| pass | |
| class DecodingModuleTest(tf.test.TestCase): | |
| def test_get_shape_keep_last_dim(self): | |
| y = tf.constant(4.0) | |
| x = tf.ones([7, tf.cast(tf.sqrt(y), tf.int32), 2, 5]) | |
| shape = decoding_module.get_shape_keep_last_dim(x) | |
| self.assertAllEqual([None, None, None, 5], shape.as_list()) | |
| def test_shape_list(self): | |
| x = tf.ones([7, 1]) | |
| shape = decoding_module.shape_list(x) | |
| self.assertAllEqual([7, 1], shape) | |
| def test_inf(self): | |
| d = TestSubclass() | |
| inf_value = d.inf() | |
| self.assertAllEqual(inf_value, tf.constant(10000000., tf.float32)) | |
| def test_length_normalization(self): | |
| d = TestSubclass() | |
| normalized_length = d.length_normalization_fn(32, tf.float32) | |
| self.assertAllEqual(normalized_length, tf.constant(1.0, tf.float32)) | |
| if __name__ == '__main__': | |
| tf.test.main() | |