Delete pretrain_dataloader_test.py

pretrain_dataloader_test.py

@@ -1,242 +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.pretrain_dataloader."""
-import itertools
-import os
-
-from absl.testing import parameterized
-import numpy as np
-import tensorflow as tf, tf_keras
-
-from official.nlp.data import pretrain_dataloader
-
-
-def create_int_feature(values):
-  f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
-  return f
-
-
-def _create_fake_bert_dataset(
-    output_path,
-    seq_length,
-    max_predictions_per_seq,
-    use_position_id,
-    use_next_sentence_label,
-    use_v2_feature_names=False):
-  """Creates a fake dataset."""
-  writer = tf.io.TFRecordWriter(output_path)
-
-  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_mask"] = create_int_feature(np.ones_like(input_ids))
-    if use_v2_feature_names:
-      features["input_word_ids"] = create_int_feature(input_ids)
-      features["input_type_ids"] = create_int_feature(np.ones_like(input_ids))
-    else:
-      features["input_ids"] = create_int_feature(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()
-
-
-def _create_fake_xlnet_dataset(
-    output_path, seq_length, max_predictions_per_seq):
-  """Creates a fake dataset."""
-  writer = tf.io.TFRecordWriter(output_path)
-  for _ in range(100):
-    features = {}
-    input_ids = np.random.randint(100, size=(seq_length))
-    num_boundary_indices = np.random.randint(1, seq_length)
-
-    if max_predictions_per_seq is not None:
-      input_mask = np.zeros_like(input_ids)
-      input_mask[:max_predictions_per_seq] = 1
-      np.random.shuffle(input_mask)
-    else:
-      input_mask = np.ones_like(input_ids)
-
-    features["input_mask"] = create_int_feature(input_mask)
-    features["input_word_ids"] = create_int_feature(input_ids)
-    features["input_type_ids"] = create_int_feature(np.ones_like(input_ids))
-    features["boundary_indices"] = create_int_feature(
-        sorted(np.random.randint(seq_length, size=(num_boundary_indices))))
-    features["target"] = create_int_feature(input_ids + 1)
-    features["label"] = create_int_feature([1])
-    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_bert_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)
-
-  def test_v2_feature_names(self):
-    train_data_path = os.path.join(self.get_temp_dir(), "train.tf_record")
-    seq_length = 128
-    max_predictions_per_seq = 20
-    _create_fake_bert_dataset(
-        train_data_path,
-        seq_length,
-        max_predictions_per_seq,
-        use_next_sentence_label=True,
-        use_position_id=False,
-        use_v2_feature_names=True)
-    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=True,
-        use_position_id=False,
-        use_v2_feature_names=True)
-
-    dataset = pretrain_dataloader.BertPretrainDataLoader(data_config).load()
-    features = next(iter(dataset))
-    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)
-
-
-class XLNetPretrainDataTest(parameterized.TestCase, tf.test.TestCase):
-
-  @parameterized.parameters(itertools.product(
-      ("single_token", "whole_word", "token_span"),
-      (0, 64),
-      (20, None),
-      ))
-  def test_load_data(
-      self, sample_strategy, reuse_length, max_predictions_per_seq):
-    train_data_path = os.path.join(self.get_temp_dir(), "train.tf_record")
-    seq_length = 128
-    batch_size = 5
-
-    _create_fake_xlnet_dataset(
-        train_data_path, seq_length, max_predictions_per_seq)
-
-    data_config = pretrain_dataloader.XLNetPretrainDataConfig(
-        input_path=train_data_path,
-        max_predictions_per_seq=max_predictions_per_seq,
-        seq_length=seq_length,
-        global_batch_size=batch_size,
-        is_training=True,
-        reuse_length=reuse_length,
-        sample_strategy=sample_strategy,
-        min_num_tokens=1,
-        max_num_tokens=2,
-        permutation_size=seq_length // 2,
-        leak_ratio=0.1)
-
-    if max_predictions_per_seq is None:
-      with self.assertRaises(ValueError):
-        dataset = pretrain_dataloader.XLNetPretrainDataLoader(
-            data_config).load()
-        features = next(iter(dataset))
-    else:
-      dataset = pretrain_dataloader.XLNetPretrainDataLoader(data_config).load()
-      features = next(iter(dataset))
-
-      self.assertIn("input_word_ids", features)
-      self.assertIn("input_type_ids", features)
-      self.assertIn("permutation_mask", features)
-      self.assertIn("masked_tokens", features)
-      self.assertIn("target", features)
-      self.assertIn("target_mask", features)
-
-      self.assertAllClose(features["input_word_ids"].shape,
-                          (batch_size, seq_length))
-      self.assertAllClose(features["input_type_ids"].shape,
-                          (batch_size, seq_length))
-      self.assertAllClose(features["permutation_mask"].shape,
-                          (batch_size, seq_length, seq_length))
-      self.assertAllClose(features["masked_tokens"].shape,
-                          (batch_size, seq_length,))
-      if max_predictions_per_seq is not None:
-        self.assertIn("target_mapping", features)
-        self.assertAllClose(features["target_mapping"].shape,
-                            (batch_size, max_predictions_per_seq, seq_length))
-        self.assertAllClose(features["target_mask"].shape,
-                            (batch_size, max_predictions_per_seq))
-        self.assertAllClose(features["target"].shape,
-                            (batch_size, max_predictions_per_seq))
-      else:
-        self.assertAllClose(features["target_mask"].shape,
-                            (batch_size, seq_length))
-        self.assertAllClose(features["target"].shape,
-                            (batch_size, seq_length))
-
-
-if __name__ == "__main__":
-  tf.test.main()