# 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()