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Delete dual_encoder_dataloader_test.py
Browse files- dual_encoder_dataloader_test.py +0 -131
dual_encoder_dataloader_test.py
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# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for official.nlp.data.dual_encoder_dataloader."""
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import os
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from absl.testing import parameterized
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import tensorflow as tf, tf_keras
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from official.nlp.data import dual_encoder_dataloader
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_LEFT_FEATURE_NAME = 'left_input'
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_RIGHT_FEATURE_NAME = 'right_input'
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def _create_fake_dataset(output_path):
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"""Creates a fake dataset contains examples for training a dual encoder model.
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The created dataset contains examples with two byteslist features keyed by
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_LEFT_FEATURE_NAME and _RIGHT_FEATURE_NAME.
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Args:
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output_path: The output path of the fake dataset.
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"""
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def create_str_feature(values):
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return tf.train.Feature(bytes_list=tf.train.BytesList(value=values))
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with tf.io.TFRecordWriter(output_path) as writer:
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for _ in range(100):
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features = {}
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features[_LEFT_FEATURE_NAME] = create_str_feature([b'hello world.'])
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features[_RIGHT_FEATURE_NAME] = create_str_feature([b'world hello.'])
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tf_example = tf.train.Example(
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features=tf.train.Features(feature=features))
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writer.write(tf_example.SerializeToString())
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def _make_vocab_file(vocab, output_path):
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with tf.io.gfile.GFile(output_path, 'w') as f:
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f.write('\n'.join(vocab + ['']))
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class DualEncoderDataTest(tf.test.TestCase, parameterized.TestCase):
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def test_load_dataset(self):
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seq_length = 16
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batch_size = 10
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train_data_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
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vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
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_create_fake_dataset(train_data_path)
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_make_vocab_file(
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['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'], vocab_path)
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data_config = dual_encoder_dataloader.DualEncoderDataConfig(
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input_path=train_data_path,
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seq_length=seq_length,
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vocab_file=vocab_path,
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lower_case=True,
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left_text_fields=(_LEFT_FEATURE_NAME,),
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right_text_fields=(_RIGHT_FEATURE_NAME,),
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global_batch_size=batch_size)
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dataset = dual_encoder_dataloader.DualEncoderDataLoader(
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data_config).load()
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features = next(iter(dataset))
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self.assertCountEqual(
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['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
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'right_mask', 'right_type_ids'],
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features.keys())
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self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
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self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
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@parameterized.parameters(False, True)
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def test_load_tfds(self, use_preprocessing_hub):
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seq_length = 16
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batch_size = 10
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if use_preprocessing_hub:
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vocab_path = ''
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preprocessing_hub = (
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'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3')
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else:
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vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
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_make_vocab_file(
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['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'],
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vocab_path)
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preprocessing_hub = ''
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data_config = dual_encoder_dataloader.DualEncoderDataConfig(
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tfds_name='para_crawl/enmt',
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tfds_split='train',
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seq_length=seq_length,
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vocab_file=vocab_path,
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lower_case=True,
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left_text_fields=('en',),
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right_text_fields=('mt',),
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preprocessing_hub_module_url=preprocessing_hub,
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global_batch_size=batch_size)
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dataset = dual_encoder_dataloader.DualEncoderDataLoader(
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data_config).load()
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features = next(iter(dataset))
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self.assertCountEqual(
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['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
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'right_mask', 'right_type_ids'],
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features.keys())
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self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
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self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
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self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
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if __name__ == '__main__':
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tf.test.main()
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