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# Copyright 2024 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.wmt_dataloader."""
import os
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from sentencepiece import SentencePieceTrainer
from official.nlp.data import wmt_dataloader
def _generate_line_file(filepath, lines):
with tf.io.gfile.GFile(filepath, 'w') as f:
for l in lines:
f.write('{}\n'.format(l))
def _generate_record_file(filepath, src_lines, tgt_lines, unique_id=False):
writer = tf.io.TFRecordWriter(filepath)
for i, (src, tgt) in enumerate(zip(src_lines, tgt_lines)):
features = {
'en': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[src.encode()])),
'reverse_en': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[tgt.encode()])),
}
if unique_id:
features['unique_id'] = tf.train.Feature(
int64_list=tf.train.Int64List(value=[i]))
example = tf.train.Example(
features=tf.train.Features(
feature=features))
writer.write(example.SerializeToString())
writer.close()
def _train_sentencepiece(input_path, vocab_size, model_path, eos_id=1):
argstr = ' '.join([
f'--input={input_path}', f'--vocab_size={vocab_size}',
'--character_coverage=0.995',
f'--model_prefix={model_path}', '--model_type=bpe',
'--bos_id=-1', '--pad_id=0', f'--eos_id={eos_id}', '--unk_id=2'
])
SentencePieceTrainer.Train(argstr)
class WMTDataLoaderTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
super(WMTDataLoaderTest, self).setUp()
self._temp_dir = self.get_temp_dir()
src_lines = [
'abc ede fg',
'bbcd ef a g',
'de f a a g'
]
tgt_lines = [
'dd cc a ef g',
'bcd ef a g',
'gef cd ba'
]
self._record_train_input_path = os.path.join(self._temp_dir, 'train.record')
_generate_record_file(self._record_train_input_path, src_lines, tgt_lines)
self._record_test_input_path = os.path.join(self._temp_dir, 'test.record')
_generate_record_file(self._record_test_input_path, src_lines, tgt_lines,
unique_id=True)
self._sentencepeice_input_path = os.path.join(self._temp_dir, 'inputs.txt')
_generate_line_file(self._sentencepeice_input_path, src_lines + tgt_lines)
sentencepeice_model_prefix = os.path.join(self._temp_dir, 'sp')
_train_sentencepiece(self._sentencepeice_input_path, 20,
sentencepeice_model_prefix)
self._sentencepeice_model_path = '{}.model'.format(
sentencepeice_model_prefix)
@parameterized.named_parameters(
('train_static', True, True, 100, (2, 35)),
('train_non_static', True, False, 100, (12, 7)),
('non_train_static', False, True, 3, (3, 35)),
('non_train_non_static', False, False, 50, (2, 7)),)
def test_load_dataset(
self, is_training, static_batch, batch_size, expected_shape):
data_config = wmt_dataloader.WMTDataConfig(
input_path=self._record_train_input_path
if is_training else self._record_test_input_path,
max_seq_length=35,
global_batch_size=batch_size,
is_training=is_training,
static_batch=static_batch,
src_lang='en',
tgt_lang='reverse_en',
sentencepiece_model_path=self._sentencepeice_model_path)
dataset = wmt_dataloader.WMTDataLoader(data_config).load()
examples = next(iter(dataset))
inputs, targets = examples['inputs'], examples['targets']
self.assertEqual(inputs.shape, expected_shape)
self.assertEqual(targets.shape, expected_shape)
def test_load_dataset_raise_invalid_window(self):
batch_tokens_size = 10 # this is too small to form buckets.
data_config = wmt_dataloader.WMTDataConfig(
input_path=self._record_train_input_path,
max_seq_length=100,
global_batch_size=batch_tokens_size,
is_training=True,
static_batch=False,
src_lang='en',
tgt_lang='reverse_en',
sentencepiece_model_path=self._sentencepeice_model_path)
with self.assertRaisesRegex(
ValueError, 'The token budget, global batch size, is too small.*'):
_ = wmt_dataloader.WMTDataLoader(data_config).load()
if __name__ == '__main__':
tf.test.main()
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