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| # Lint as: python3 | |
| # Copyright 2020 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 BERT configurations and models instantiation.""" | |
| import tensorflow as tf | |
| from official.nlp.configs import bert | |
| from official.nlp.configs import encoders | |
| class BertModelsTest(tf.test.TestCase): | |
| def test_network_invocation(self): | |
| config = bert.BertPretrainerConfig( | |
| encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1)) | |
| _ = bert.instantiate_bertpretrainer_from_cfg(config) | |
| # Invokes with classification heads. | |
| config = bert.BertPretrainerConfig( | |
| encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1), | |
| cls_heads=[ | |
| bert.ClsHeadConfig( | |
| inner_dim=10, num_classes=2, name="next_sentence") | |
| ]) | |
| _ = bert.instantiate_bertpretrainer_from_cfg(config) | |
| with self.assertRaises(ValueError): | |
| config = bert.BertPretrainerConfig( | |
| encoder=encoders.TransformerEncoderConfig( | |
| vocab_size=10, num_layers=1), | |
| cls_heads=[ | |
| bert.ClsHeadConfig( | |
| inner_dim=10, num_classes=2, name="next_sentence"), | |
| bert.ClsHeadConfig( | |
| inner_dim=10, num_classes=2, name="next_sentence") | |
| ]) | |
| _ = bert.instantiate_bertpretrainer_from_cfg(config) | |
| def test_checkpoint_items(self): | |
| config = bert.BertPretrainerConfig( | |
| encoder=encoders.TransformerEncoderConfig(vocab_size=10, num_layers=1), | |
| cls_heads=[ | |
| bert.ClsHeadConfig( | |
| inner_dim=10, num_classes=2, name="next_sentence") | |
| ]) | |
| encoder = bert.instantiate_bertpretrainer_from_cfg(config) | |
| self.assertSameElements(encoder.checkpoint_items.keys(), | |
| ["encoder", "next_sentence.pooler_dense"]) | |
| if __name__ == "__main__": | |
| tf.test.main() | |