Delete sentence_prediction_dataloader_test.py

sentence_prediction_dataloader_test.py

@@ -1,290 +0,0 @@
-# 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()