Internal change

PiperOrigin-RevId: 325956876

official/nlp/data/create_pretraining_data_test.py

+# Lint as: python3
+# Copyright 2020 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.create_pretraining_data."""
+import random
+
+import tensorflow as tf
+
+from official.nlp.data import create_pretraining_data as cpd
+
+_VOCAB_WORDS = ["vocab_1", "vocab_2"]
+
+
+class CreatePretrainingDataTest(tf.test.TestCase):
+
+  def assertTokens(self,
+                   input_tokens,
+                   output_tokens,
+                   masked_positions,
+                   masked_labels):
+    # Ensure the masked positions are unique.
+    self.assertCountEqual(masked_positions, set(masked_positions))
+
+    # Ensure we can reconstruct the input from the output.
+    reconstructed_tokens = output_tokens
+    for pos, label in zip(masked_positions, masked_labels):
+      reconstructed_tokens[pos] = label
+    self.assertEqual(input_tokens, reconstructed_tokens)
+
+    # Ensure each label is valid.
+    for pos, label in zip(masked_positions, masked_labels):
+      output_token = output_tokens[pos]
+      if (output_token == "[MASK]" or
+          output_token in _VOCAB_WORDS or
+          output_token == input_tokens[pos]):
+        continue
+      self.fail("invalid mask value: {}".format(output_token))
+
+  def test_wordpieces_to_grams(self):
+    tests = [
+        (["That", "cone"],
+         [(0, 1), (1, 2)]),
+        (["That", "cone", "##s"],
+         [(0, 1), (1, 3)]),
+        (["Swit", "##zer", "##land"],
+         [(0, 3)]),
+        (["[CLS]", "Up", "##dog"],
+         [(1, 3)]),
+        (["[CLS]", "Up", "##dog", "[SEP]", "Down"],
+         [(1, 3), (4, 5)]),
+    ]
+    for inp, expected in tests:
+      output = cpd._wordpieces_to_grams(inp)
+      self.assertEqual(expected, output)
+
+  def test_window(self):
+    input_list = [1, 2, 3, 4]
+    window_outputs = [
+        (1, [[1], [2], [3], [4]]),
+        (2, [[1, 2], [2, 3], [3, 4]]),
+        (3, [[1, 2, 3], [2, 3, 4]]),
+        (4, [[1, 2, 3, 4]]),
+        (5, []),
+    ]
+    for window, expected in window_outputs:
+      output = cpd._window(input_list, window)
+      self.assertEqual(expected, list(output))
+
+  def test_create_masked_lm_predictions(self):
+    tokens = ["[CLS]", "a", "##a", "b", "##b", "c", "##c", "[SEP]"]
+    rng = random.Random(123)
+    for _ in range(0, 5):
+      output_tokens, masked_positions, masked_labels = (
+          cpd.create_masked_lm_predictions(
+              tokens=tokens,
+              masked_lm_prob=1.0,
+              max_predictions_per_seq=3,
+              vocab_words=_VOCAB_WORDS,
+              rng=rng,
+              do_whole_word_mask=False,
+              max_ngram_size=None))
+      self.assertEqual(len(masked_positions), 3)
+      self.assertEqual(len(masked_labels), 3)
+      self.assertTokens(tokens, output_tokens,
+                        masked_positions, masked_labels)
+
+  def test_create_masked_lm_predictions_whole_word(self):
+    tokens = ["[CLS]", "a", "##a", "b", "##b", "c", "##c", "[SEP]"]
+    rng = random.Random(345)
+    for _ in range(0, 5):
+      output_tokens, masked_positions, masked_labels = (
+          cpd.create_masked_lm_predictions(
+              tokens=tokens,
+              masked_lm_prob=1.0,
+              max_predictions_per_seq=3,
+              vocab_words=_VOCAB_WORDS,
+              rng=rng,
+              do_whole_word_mask=True,
+              max_ngram_size=None))
+      # since we can't get exactly three tokens without breaking a word we
+      # only take two.
+      self.assertEqual(len(masked_positions), 2)
+      self.assertEqual(len(masked_labels), 2)
+      self.assertTokens(tokens, output_tokens,
+                        masked_positions, masked_labels)
+      # ensure that we took an entire word.
+      self.assertIn(masked_labels, [["a", "##a"], ["b", "##b"], ["c", "##c"]])
+
+  def test_create_masked_lm_predictions_ngram(self):
+    tokens = ["[CLS]"] + ["tok{}".format(i) for i in range(0, 512)] + ["[SEP]"]
+    rng = random.Random(345)
+    for _ in range(0, 5):
+      output_tokens, masked_positions, masked_labels = (
+          cpd.create_masked_lm_predictions(
+              tokens=tokens,
+              masked_lm_prob=1.0,
+              max_predictions_per_seq=76,
+              vocab_words=_VOCAB_WORDS,
+              rng=rng,
+              do_whole_word_mask=True,
+              max_ngram_size=3))
+      self.assertEqual(len(masked_positions), 76)
+      self.assertEqual(len(masked_labels), 76)
+      self.assertTokens(tokens, output_tokens,
+                        masked_positions, masked_labels)
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/nlp/data/data_loader_factory_test.py

+# Lint as: python3
+# Copyright 2020 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.data_loader_factory."""
+import dataclasses
+import tensorflow as tf
+
+from official.modeling.hyperparams import config_definitions as cfg
+from official.nlp.data import data_loader_factory
+
+
+@dataclasses.dataclass
+class MyDataConfig(cfg.DataConfig):
+  is_training: bool = True
+
+
+@data_loader_factory.register_data_loader_cls(MyDataConfig)
+class MyDataLoader:
+
+  def __init__(self, params):
+    self.params = params
+
+
+class DataLoaderFactoryTest(tf.test.TestCase):
+
+  def test_register_and_load(self):
+    train_config = MyDataConfig()
+    train_loader = data_loader_factory.get_data_loader(train_config)
+    self.assertTrue(train_loader.params.is_training)
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/nlp/data/pretrain_dataloader_test.py

+# Lint as: python3
+# Copyright 2020 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.pretrain_dataloader."""
+import itertools
+import os
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from official.nlp.data import pretrain_dataloader
+
+
+def _create_fake_dataset(output_path, seq_length, max_predictions_per_seq,
+                         use_position_id, use_next_sentence_label):
+  """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))
+
+    features["masked_lm_positions"] = create_int_feature(
+        np.random.randint(100, size=(max_predictions_per_seq)))
+    features["masked_lm_ids"] = create_int_feature(
+        np.random.randint(100, size=(max_predictions_per_seq)))
+    features["masked_lm_weights"] = create_float_feature(
+        [1.0] * max_predictions_per_seq)
+
+    if use_next_sentence_label:
+      features["next_sentence_labels"] = create_int_feature([1])
+
+    if use_position_id:
+      features["position_ids"] = create_int_feature(range(0, seq_length))
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+    writer.write(tf_example.SerializeToString())
+  writer.close()
+
+
+class BertPretrainDataTest(tf.test.TestCase, parameterized.TestCase):
+
+  @parameterized.parameters(itertools.product(
+      (False, True),
+      (False, True),
+  ))
+  def test_load_data(self, use_next_sentence_label, use_position_id):
+    train_data_path = os.path.join(self.get_temp_dir(), "train.tf_record")
+    seq_length = 128
+    max_predictions_per_seq = 20
+    _create_fake_dataset(
+        train_data_path,
+        seq_length,
+        max_predictions_per_seq,
+        use_next_sentence_label=use_next_sentence_label,
+        use_position_id=use_position_id)
+    data_config = pretrain_dataloader.BertPretrainDataConfig(
+        input_path=train_data_path,
+        max_predictions_per_seq=max_predictions_per_seq,
+        seq_length=seq_length,
+        global_batch_size=10,
+        is_training=True,
+        use_next_sentence_label=use_next_sentence_label,
+        use_position_id=use_position_id)
+
+    dataset = pretrain_dataloader.BertPretrainDataLoader(data_config).load()
+    features = next(iter(dataset))
+    self.assertLen(features,
+                   6 + int(use_next_sentence_label) + int(use_position_id))
+    self.assertIn("input_word_ids", features)
+    self.assertIn("input_mask", features)
+    self.assertIn("input_type_ids", features)
+    self.assertIn("masked_lm_positions", features)
+    self.assertIn("masked_lm_ids", features)
+    self.assertIn("masked_lm_weights", features)
+
+    self.assertEqual("next_sentence_labels" in features,
+                     use_next_sentence_label)
+    self.assertEqual("position_ids" in features, use_position_id)
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/nlp/data/question_answering_dataloader_test.py

+# Lint as: python3
+# Copyright 2020 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.question_answering_dataloader."""
+import os
+import numpy as np
+import tensorflow as tf
+
+from official.nlp.data import question_answering_dataloader
+
+
+def _create_fake_dataset(output_path, seq_length):
+  """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
+
+  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))
+    features['start_positions'] = create_int_feature(np.array([0]))
+    features['end_positions'] = create_int_feature(np.array([10]))
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+    writer.write(tf_example.SerializeToString())
+  writer.close()
+
+
+class QuestionAnsweringDataTest(tf.test.TestCase):
+
+  def test_load_dataset(self):
+    seq_length = 128
+    batch_size = 10
+    input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
+    _create_fake_dataset(input_path, seq_length)
+    data_config = question_answering_dataloader.QADataConfig(
+        is_training=True,
+        input_path=input_path,
+        seq_length=seq_length,
+        global_batch_size=batch_size)
+    dataset = question_answering_dataloader.QuestionAnsweringDataLoader(
+        data_config).load()
+    features, labels = next(iter(dataset))
+
+    self.assertCountEqual(['input_word_ids', 'input_mask', 'input_type_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.assertCountEqual(['start_positions', 'end_positions'], labels.keys())
+    self.assertEqual(labels['start_positions'].shape, (batch_size,))
+    self.assertEqual(labels['end_positions'].shape, (batch_size,))
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/nlp/data/sentence_prediction_dataloader_test.py

+# Lint as: python3
+# Copyright 2020 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
+
+from official.nlp.data import sentence_prediction_dataloader
+
+
+def _create_fake_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()
+
+
+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_dataset(input_path, seq_length, label_type)
+    data_config = sentence_prediction_dataloader.SentencePredictionDataConfig(
+        input_path=input_path,
+        seq_length=seq_length,
+        global_batch_size=batch_size,
+        label_type=label_type)
+    dataset = sentence_prediction_dataloader.SentencePredictionDataLoader(
+        data_config).load()
+    features, labels = next(iter(dataset))
+    self.assertCountEqual(['input_word_ids', 'input_mask', 'input_type_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(labels.shape, (batch_size,))
+    self.assertEqual(labels.dtype, expected_label_type)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/nlp/data/tagging_data_lib_test.py

+# Lint as: python3
+# Copyright 2020 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.tagging_data_lib."""
+import os
+import random
+
+from absl.testing import parameterized
+import tensorflow as tf
+
+from official.nlp.bert import tokenization
+from official.nlp.data import tagging_data_lib
+
+
+def _create_fake_file(filename, labels, is_test):
+
+  def write_one_sentence(writer, length):
+    for _ in range(length):
+      line = "hiworld"
+      if not is_test:
+        line += "\t%s" % (labels[random.randint(0, len(labels) - 1)])
+      writer.write(line + "\n")
+
+  # Writes two sentences with length of 3 and 12 respectively.
+  with tf.io.gfile.GFile(filename, "w") as writer:
+    write_one_sentence(writer, 3)
+    writer.write("\n")
+    write_one_sentence(writer, 12)
+
+
+class TaggingDataLibTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    super(TaggingDataLibTest, self).setUp()
+
+    self.processors = {
+        "panx": tagging_data_lib.PanxProcessor,
+        "udpos": tagging_data_lib.UdposProcessor,
+    }
+    self.vocab_file = os.path.join(self.get_temp_dir(), "vocab.txt")
+    with tf.io.gfile.GFile(self.vocab_file, "w") as writer:
+      writer.write("\n".join(["[CLS]", "[SEP]", "hi", "##world", "[UNK]"]))
+
+  @parameterized.parameters(
+      {"task_type": "panx"},
+      {"task_type": "udpos"},
+  )
+  def test_generate_tf_record(self, task_type):
+    processor = self.processors[task_type]()
+    input_data_dir = os.path.join(self.get_temp_dir(), task_type)
+    tf.io.gfile.mkdir(input_data_dir)
+    # Write fake train file.
+    _create_fake_file(
+        os.path.join(input_data_dir, "train-en.tsv"),
+        processor.get_labels(),
+        is_test=False)
+
+    # Write fake dev file.
+    _create_fake_file(
+        os.path.join(input_data_dir, "dev-en.tsv"),
+        processor.get_labels(),
+        is_test=False)
+
+    # Write fake test files.
+    for lang in processor.supported_languages:
+      _create_fake_file(
+          os.path.join(input_data_dir, "test-%s.tsv" % lang),
+          processor.get_labels(),
+          is_test=True)
+
+    output_path = os.path.join(self.get_temp_dir(), task_type, "output")
+    tokenizer = tokenization.FullTokenizer(
+        vocab_file=self.vocab_file, do_lower_case=True)
+    metadata = tagging_data_lib.generate_tf_record_from_data_file(
+        processor,
+        input_data_dir,
+        tokenizer,
+        max_seq_length=8,
+        train_data_output_path=os.path.join(output_path, "train.tfrecord"),
+        eval_data_output_path=os.path.join(output_path, "eval.tfrecord"),
+        test_data_output_path=os.path.join(output_path, "test_{}.tfrecord"),
+        text_preprocessing=tokenization.convert_to_unicode)
+
+    self.assertEqual(metadata["train_data_size"], 5)
+    files = tf.io.gfile.glob(output_path + "/*")
+    expected_files = []
+    expected_files.append(os.path.join(output_path, "train.tfrecord"))
+    expected_files.append(os.path.join(output_path, "eval.tfrecord"))
+    for lang in processor.supported_languages:
+      expected_files.append(
+          os.path.join(output_path, "test_%s.tfrecord" % lang))
+
+    self.assertCountEqual(files, expected_files)
+
+
+if __name__ == "__main__":
+  tf.test.main()

official/nlp/data/tagging_data_loader_test.py

+# Lint as: python3
+# Copyright 2020 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.tagging_data_loader."""
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from official.nlp.data import tagging_data_loader
+
+
+def _create_fake_dataset(output_path, seq_length):
+  """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
+
+  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))
+    features['label_ids'] = create_int_feature(
+        np.random.randint(10, size=(seq_length)))
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
+    writer.write(tf_example.SerializeToString())
+  writer.close()
+
+
+class TaggingDataLoaderTest(tf.test.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')
+    _create_fake_dataset(train_data_path, seq_length)
+    data_config = tagging_data_loader.TaggingDataConfig(
+        input_path=train_data_path,
+        seq_length=seq_length,
+        global_batch_size=batch_size)
+
+    dataset = tagging_data_loader.TaggingDataLoader(data_config).load()
+    features, labels = next(iter(dataset))
+    self.assertCountEqual(['input_word_ids', 'input_mask', 'input_type_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(labels.shape, (batch_size, seq_length))
+
+
+if __name__ == '__main__':
+  tf.test.main()