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# 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()
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