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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 for official.nlp.data.dual_encoder_dataloader."""
import os
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.nlp.data import dual_encoder_dataloader
_LEFT_FEATURE_NAME = 'left_input'
_RIGHT_FEATURE_NAME = 'right_input'
def _create_fake_dataset(output_path):
"""Creates a fake dataset contains examples for training a dual encoder model.
The created dataset contains examples with two byteslist features keyed by
_LEFT_FEATURE_NAME and _RIGHT_FEATURE_NAME.
Args:
output_path: The output path of the fake dataset.
"""
def create_str_feature(values):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=values))
with tf.io.TFRecordWriter(output_path) as writer:
for _ in range(100):
features = {}
features[_LEFT_FEATURE_NAME] = create_str_feature([b'hello world.'])
features[_RIGHT_FEATURE_NAME] = create_str_feature([b'world hello.'])
tf_example = tf.train.Example(
features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
def _make_vocab_file(vocab, output_path):
with tf.io.gfile.GFile(output_path, 'w') as f:
f.write('\n'.join(vocab + ['']))
class DualEncoderDataTest(tf.test.TestCase, parameterized.TestCase):
def test_load_dataset(self):
seq_length = 16
batch_size = 10
train_data_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
_create_fake_dataset(train_data_path)
_make_vocab_file(
['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'], vocab_path)
data_config = dual_encoder_dataloader.DualEncoderDataConfig(
input_path=train_data_path,
seq_length=seq_length,
vocab_file=vocab_path,
lower_case=True,
left_text_fields=(_LEFT_FEATURE_NAME,),
right_text_fields=(_RIGHT_FEATURE_NAME,),
global_batch_size=batch_size)
dataset = dual_encoder_dataloader.DualEncoderDataLoader(
data_config).load()
features = next(iter(dataset))
self.assertCountEqual(
['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
'right_mask', 'right_type_ids'],
features.keys())
self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
@parameterized.parameters(False, True)
def test_load_tfds(self, use_preprocessing_hub):
seq_length = 16
batch_size = 10
if use_preprocessing_hub:
vocab_path = ''
preprocessing_hub = (
'https://tfhub.dev/tensorflow/bert_multi_cased_preprocess/3')
else:
vocab_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
_make_vocab_file(
['[PAD]', '[UNK]', '[CLS]', '[SEP]', 'he', '#llo', 'world'],
vocab_path)
preprocessing_hub = ''
data_config = dual_encoder_dataloader.DualEncoderDataConfig(
tfds_name='para_crawl/enmt',
tfds_split='train',
seq_length=seq_length,
vocab_file=vocab_path,
lower_case=True,
left_text_fields=('en',),
right_text_fields=('mt',),
preprocessing_hub_module_url=preprocessing_hub,
global_batch_size=batch_size)
dataset = dual_encoder_dataloader.DualEncoderDataLoader(
data_config).load()
features = next(iter(dataset))
self.assertCountEqual(
['left_word_ids', 'left_mask', 'left_type_ids', 'right_word_ids',
'right_mask', 'right_type_ids'],
features.keys())
self.assertEqual(features['left_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['left_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['left_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['right_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['right_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['right_type_ids'].shape, (batch_size, seq_length))
if __name__ == '__main__':
tf.test.main()
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