official/nlp/data/sentence_prediction_dataloader_test.py
# 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.sentence_prediction_dataloader."""
import os
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from sentencepiece import SentencePieceTrainer
from official.nlp.data import sentence_prediction_dataloader as loader
def _create_fake_preprocessed_dataset(output_path, seq_length, label_type):
"""Creates a fake dataset."""
writer = tf.io.TFRecordWriter(output_path)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
for _ in range(100):
features = {}
input_ids = np.random.randint(100, size=(seq_length))
features['input_ids'] = create_int_feature(input_ids)
features['input_mask'] = create_int_feature(np.ones_like(input_ids))
features['segment_ids'] = create_int_feature(np.ones_like(input_ids))
if label_type == 'int':
features['label_ids'] = create_int_feature([1])
elif label_type == 'float':
features['label_ids'] = create_float_feature([0.5])
else:
raise ValueError('Unsupported label_type: %s' % label_type)
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def _create_fake_raw_dataset(output_path, text_fields, label_type):
"""Creates a fake tf record file."""
writer = tf.io.TFRecordWriter(output_path)
def create_str_feature(value):
f = tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
return f
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
for _ in range(100):
features = {}
for text_field in text_fields:
features[text_field] = create_str_feature([b'hello world'])
if label_type == 'int':
features['label'] = create_int_feature([0])
elif label_type == 'float':
features['label'] = create_float_feature([0.5])
else:
raise ValueError('Unexpected label_type: %s' % label_type)
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def _create_fake_sentencepiece_model(output_dir):
vocab = ['a', 'b', 'c', 'd', 'e', 'abc', 'def', 'ABC', 'DEF']
model_prefix = os.path.join(output_dir, 'spm_model')
input_text_file_path = os.path.join(output_dir, 'train_input.txt')
with tf.io.gfile.GFile(input_text_file_path, 'w') as f:
f.write(' '.join(vocab + ['\n']))
# Add 7 more tokens: <pad>, <unk>, [CLS], [SEP], [MASK], <s>, </s>.
full_vocab_size = len(vocab) + 7
flags = dict(
model_prefix=model_prefix,
model_type='word',
input=input_text_file_path,
pad_id=0,
unk_id=1,
control_symbols='[CLS],[SEP],[MASK]',
vocab_size=full_vocab_size,
bos_id=full_vocab_size - 2,
eos_id=full_vocab_size - 1)
SentencePieceTrainer.Train(' '.join(
['--{}={}'.format(k, v) for k, v in flags.items()]))
return model_prefix + '.model'
def _create_fake_vocab_file(vocab_file_path):
tokens = ['[PAD]']
for i in range(1, 100):
tokens.append('[unused%d]' % i)
tokens.extend(['[UNK]', '[CLS]', '[SEP]', '[MASK]', 'hello', 'world'])
with tf.io.gfile.GFile(vocab_file_path, 'w') as outfile:
outfile.write('\n'.join(tokens))
class SentencePredictionDataTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(('int', tf.int32), ('float', tf.float32))
def test_load_dataset(self, label_type, expected_label_type):
input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
batch_size = 10
seq_length = 128
_create_fake_preprocessed_dataset(input_path, seq_length, label_type)
data_config = loader.SentencePredictionDataConfig(
input_path=input_path,
seq_length=seq_length,
global_batch_size=batch_size,
label_type=label_type)
dataset = loader.SentencePredictionDataLoader(data_config).load()
features = next(iter(dataset))
self.assertCountEqual(
['input_word_ids', 'input_type_ids', 'input_mask', 'label_ids'],
features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['label_ids'].shape, (batch_size,))
self.assertEqual(features['label_ids'].dtype, expected_label_type)
def test_load_dataset_with_label_mapping(self):
input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
batch_size = 10
seq_length = 128
_create_fake_preprocessed_dataset(input_path, seq_length, 'int')
data_config = loader.SentencePredictionDataConfig(
input_path=input_path,
seq_length=seq_length,
global_batch_size=batch_size,
label_type='int',
label_name=('label_ids', 'next_sentence_labels'))
dataset = loader.SentencePredictionDataLoader(data_config).load()
features = next(iter(dataset))
self.assertCountEqual([
'input_word_ids', 'input_mask', 'input_type_ids',
'next_sentence_labels', 'label_ids'
], features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['label_ids'].shape, (batch_size,))
self.assertEqual(features['label_ids'].dtype, tf.int32)
self.assertEqual(features['next_sentence_labels'].shape, (batch_size,))
self.assertEqual(features['next_sentence_labels'].dtype, tf.int32)
class SentencePredictionTfdsDataLoaderTest(tf.test.TestCase,
parameterized.TestCase):
@parameterized.parameters(True, False)
def test_python_wordpiece_preprocessing(self, use_tfds):
batch_size = 10
seq_length = 256 # Non-default value.
lower_case = True
tf_record_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
text_fields = ['sentence1', 'sentence2']
if not use_tfds:
_create_fake_raw_dataset(tf_record_path, text_fields, label_type='int')
vocab_file_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
_create_fake_vocab_file(vocab_file_path)
data_config = loader.SentencePredictionTextDataConfig(
input_path='' if use_tfds else tf_record_path,
tfds_name='glue/mrpc' if use_tfds else '',
tfds_split='train' if use_tfds else '',
text_fields=text_fields,
global_batch_size=batch_size,
seq_length=seq_length,
is_training=True,
lower_case=lower_case,
vocab_file=vocab_file_path)
dataset = loader.SentencePredictionTextDataLoader(data_config).load()
features = next(iter(dataset))
label_field = data_config.label_field
expected_keys = [
'input_word_ids', 'input_type_ids', 'input_mask', label_field
]
if use_tfds:
expected_keys += ['idx']
self.assertCountEqual(expected_keys, features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features[label_field].shape, (batch_size,))
@parameterized.parameters(True, False)
def test_python_sentencepiece_preprocessing(self, use_tfds):
batch_size = 10
seq_length = 256 # Non-default value.
lower_case = True
tf_record_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
text_fields = ['sentence1', 'sentence2']
if not use_tfds:
_create_fake_raw_dataset(tf_record_path, text_fields, label_type='int')
sp_model_file_path = _create_fake_sentencepiece_model(self.get_temp_dir())
data_config = loader.SentencePredictionTextDataConfig(
input_path='' if use_tfds else tf_record_path,
tfds_name='glue/mrpc' if use_tfds else '',
tfds_split='train' if use_tfds else '',
text_fields=text_fields,
global_batch_size=batch_size,
seq_length=seq_length,
is_training=True,
lower_case=lower_case,
tokenization='SentencePiece',
vocab_file=sp_model_file_path,
)
dataset = loader.SentencePredictionTextDataLoader(data_config).load()
features = next(iter(dataset))
label_field = data_config.label_field
expected_keys = [
'input_word_ids', 'input_type_ids', 'input_mask', label_field
]
if use_tfds:
expected_keys += ['idx']
self.assertCountEqual(expected_keys, features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features[label_field].shape, (batch_size,))
@parameterized.parameters(True, False)
def test_saved_model_preprocessing(self, use_tfds):
batch_size = 10
seq_length = 256 # Non-default value.
tf_record_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
text_fields = ['sentence1', 'sentence2']
if not use_tfds:
_create_fake_raw_dataset(tf_record_path, text_fields, label_type='float')
vocab_file_path = os.path.join(self.get_temp_dir(), 'vocab.txt')
_create_fake_vocab_file(vocab_file_path)
data_config = loader.SentencePredictionTextDataConfig(
input_path='' if use_tfds else tf_record_path,
tfds_name='glue/mrpc' if use_tfds else '',
tfds_split='train' if use_tfds else '',
text_fields=text_fields,
global_batch_size=batch_size,
seq_length=seq_length,
is_training=True,
preprocessing_hub_module_url=(
'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'),
label_type='int' if use_tfds else 'float',
)
dataset = loader.SentencePredictionTextDataLoader(data_config).load()
features = next(iter(dataset))
label_field = data_config.label_field
expected_keys = [
'input_word_ids', 'input_type_ids', 'input_mask', label_field
]
if use_tfds:
expected_keys += ['idx']
self.assertCountEqual(expected_keys, features.keys())
self.assertEqual(features['input_word_ids'].shape, (batch_size, seq_length))
self.assertEqual(features['input_mask'].shape, (batch_size, seq_length))
self.assertEqual(features['input_type_ids'].shape, (batch_size, seq_length))
self.assertEqual(features[label_field].shape, (batch_size,))
if __name__ == '__main__':
tf.test.main()