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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. | |
| """Tests official.nlp.bert.export_tfhub.""" | |
| import os | |
| from absl.testing import parameterized | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| import tensorflow_hub as hub | |
| from official.legacy.bert import configs | |
| from official.legacy.bert import export_tfhub | |
| class ExportTfhubTest(tf.test.TestCase, parameterized.TestCase): | |
| def test_export_tfhub(self, ckpt_key_name): | |
| # Exports a savedmodel for TF-Hub | |
| hidden_size = 16 | |
| bert_config = configs.BertConfig( | |
| vocab_size=100, | |
| hidden_size=hidden_size, | |
| intermediate_size=32, | |
| max_position_embeddings=128, | |
| num_attention_heads=2, | |
| num_hidden_layers=1) | |
| bert_model, encoder = export_tfhub.create_bert_model(bert_config) | |
| model_checkpoint_dir = os.path.join(self.get_temp_dir(), "checkpoint") | |
| checkpoint = tf.train.Checkpoint(**{ckpt_key_name: encoder}) | |
| checkpoint.save(os.path.join(model_checkpoint_dir, "test")) | |
| model_checkpoint_path = tf.train.latest_checkpoint(model_checkpoint_dir) | |
| vocab_file = os.path.join(self.get_temp_dir(), "uncased_vocab.txt") | |
| with tf.io.gfile.GFile(vocab_file, "w") as f: | |
| f.write("dummy content") | |
| hub_destination = os.path.join(self.get_temp_dir(), "hub") | |
| export_tfhub.export_bert_tfhub(bert_config, model_checkpoint_path, | |
| hub_destination, vocab_file) | |
| # Restores a hub KerasLayer. | |
| hub_layer = hub.KerasLayer(hub_destination, trainable=True) | |
| if hasattr(hub_layer, "resolved_object"): | |
| # Checks meta attributes. | |
| self.assertTrue(hub_layer.resolved_object.do_lower_case.numpy()) | |
| with tf.io.gfile.GFile( | |
| hub_layer.resolved_object.vocab_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()) | |
| seq_length = 10 | |
| dummy_ids = np.zeros((2, seq_length), 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, hidden_size)) | |
| self.assertEqual(hub_outputs[1].shape, (2, seq_length, hidden_size)) | |
| 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()) | |
| # Test that training=True makes a difference (activates dropout). | |
| def _dropout_mean_stddev(training, num_runs=20): | |
| input_ids = np.array([[14, 12, 42, 95, 99]], np.int32) | |
| inputs = [input_ids, np.ones_like(input_ids), np.zeros_like(input_ids)] | |
| outputs = np.concatenate( | |
| [hub_layer(inputs, training=training)[0] for _ in range(num_runs)]) | |
| return np.mean(np.std(outputs, axis=0)) | |
| self.assertLess(_dropout_mean_stddev(training=False), 1e-6) | |
| self.assertGreater(_dropout_mean_stddev(training=True), 1e-3) | |
| # Test propagation of seq_length in shape inference. | |
| input_word_ids = tf_keras.layers.Input(shape=(seq_length,), dtype=tf.int32) | |
| input_mask = tf_keras.layers.Input(shape=(seq_length,), dtype=tf.int32) | |
| input_type_ids = tf_keras.layers.Input(shape=(seq_length,), dtype=tf.int32) | |
| pooled_output, sequence_output = hub_layer( | |
| [input_word_ids, input_mask, input_type_ids]) | |
| self.assertEqual(pooled_output.shape.as_list(), [None, hidden_size]) | |
| self.assertEqual(sequence_output.shape.as_list(), | |
| [None, seq_length, hidden_size]) | |
| if __name__ == "__main__": | |
| tf.test.main() | |