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| # Copyright 2019 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 official.nlp.albert.export_albert_tfhub.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import numpy as np | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| from official.nlp.albert import configs | |
| from official.nlp.albert import export_albert_tfhub | |
| class ExportAlbertTfhubTest(tf.test.TestCase): | |
| def test_export_albert_tfhub(self): | |
| # Exports a savedmodel for TF-Hub | |
| albert_config = configs.AlbertConfig( | |
| vocab_size=100, | |
| embedding_size=8, | |
| hidden_size=16, | |
| intermediate_size=32, | |
| max_position_embeddings=128, | |
| num_attention_heads=2, | |
| num_hidden_layers=1) | |
| bert_model, encoder = export_albert_tfhub.create_albert_model(albert_config) | |
| model_checkpoint_dir = os.path.join(self.get_temp_dir(), "checkpoint") | |
| checkpoint = tf.train.Checkpoint(model=encoder) | |
| checkpoint.save(os.path.join(model_checkpoint_dir, "test")) | |
| model_checkpoint_path = tf.train.latest_checkpoint(model_checkpoint_dir) | |
| sp_model_file = os.path.join(self.get_temp_dir(), "sp_tokenizer.model") | |
| with tf.io.gfile.GFile(sp_model_file, "w") as f: | |
| f.write("dummy content") | |
| hub_destination = os.path.join(self.get_temp_dir(), "hub") | |
| export_albert_tfhub.export_albert_tfhub( | |
| albert_config, | |
| model_checkpoint_path, | |
| hub_destination, | |
| sp_model_file=sp_model_file) | |
| # Restores a hub KerasLayer. | |
| hub_layer = hub.KerasLayer(hub_destination, trainable=True) | |
| if hasattr(hub_layer, "resolved_object"): | |
| with tf.io.gfile.GFile( | |
| hub_layer.resolved_object.sp_model_file.asset_path.numpy()) as f: | |
| self.assertEqual("dummy content", f.read()) | |
| # Checks the hub KerasLayer. | |
| for source_weight, hub_weight in zip(bert_model.trainable_weights, | |
| hub_layer.trainable_weights): | |
| self.assertAllClose(source_weight.numpy(), hub_weight.numpy()) | |
| dummy_ids = np.zeros((2, 10), dtype=np.int32) | |
| hub_outputs = hub_layer([dummy_ids, dummy_ids, dummy_ids]) | |
| source_outputs = bert_model([dummy_ids, dummy_ids, dummy_ids]) | |
| # The outputs of hub module are "pooled_output" and "sequence_output", | |
| # while the outputs of encoder is in reversed order, i.e., | |
| # "sequence_output" and "pooled_output". | |
| encoder_outputs = reversed(encoder([dummy_ids, dummy_ids, dummy_ids])) | |
| self.assertEqual(hub_outputs[0].shape, (2, 16)) | |
| self.assertEqual(hub_outputs[1].shape, (2, 10, 16)) | |
| for source_output, hub_output, encoder_output in zip( | |
| source_outputs, hub_outputs, encoder_outputs): | |
| self.assertAllClose(source_output.numpy(), hub_output.numpy()) | |
| self.assertAllClose(source_output.numpy(), encoder_output.numpy()) | |
| if __name__ == "__main__": | |
| tf.test.main() | |