Skip to content

GitLab

"...git@developer.sourcefind.cn:renzhc/diffusers_dcu.git" did not exist on "6cf8d98ce11867ebe6577d03508e3e06b37ade03"
Commit 8c7d6786 authored by Hongkun Yu's avatar Hongkun Yu Committed by A. Unique TensorFlower
Browse files

Internal change 

PiperOrigin-RevId: 416569827
parent afe1d902
Hide whitespace changes
Inline Side-by-side
Showing with 0 additions and 282 deletions
official/projects/exbert/exbert_pretrain_dataloader.py deleted 100644 → 0
View file @ afe1d902
# Copyright 2021 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.
"""Loads dataset for the ExBERT teacher pretraining task."""
from typing import Mapping, Optional
import tensorflow as tf
from official.core import input_reader
from official.nlp.data import data_loader
class ExbertTeacherPretrainDataLoader(data_loader.DataLoader):
"""Class to load mixed-vocabulary dataset for Exbert teacher pretrain task."""
def __init__(self, params):
"""Inits `ExbertTeacherPretrainDataLoader` class.
Args:
params: A `BertPretrainDataConfig` object.
"""
self._params = params
self._seq_length = params.seq_length
self._max_predictions_per_seq = params.max_predictions_per_seq
self._use_next_sentence_label = params.use_next_sentence_label
self._use_position_id = params.use_position_id
def _name_to_features(self):
name_to_features = {
'input_mask':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_mask_teacher':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_seg_vocab_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'masked_lm_positions':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
'masked_lm_positions_teacher_tvocab':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
'masked_lm_positions_teacher_svocab':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
'masked_lm_ids':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
'masked_lm_ids_teacher_tvocab':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
'masked_lm_ids_teacher_svocab':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
}
if self._params.use_v2_feature_names:
name_to_features.update({
'input_word_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_word_ids_teacher':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_type_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_type_ids_teacher':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
})
else:
name_to_features.update({
'input_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_ids_teacher':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'segment_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'segment_ids_teacher':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
})
if self._use_next_sentence_label:
name_to_features['next_sentence_labels'] = tf.io.FixedLenFeature([1],
tf.int64)
if self._use_position_id:
name_to_features['position_ids'] = tf.io.FixedLenFeature(
[self._seq_length], tf.int64)
return name_to_features
def _decode(self, record: tf.Tensor):
"""Decodes a serialized tf.Example."""
name_to_features = self._name_to_features()
example = tf.io.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def _parse(self, record: Mapping[str, tf.Tensor]):
"""Parses raw tensors into a dict of tensors to be consumed by the model."""
x = {
'input_mask':
record['input_mask'],
'input_mask_teacher':
record['input_mask_teacher'],
'input_seg_vocab_ids':
record['input_seg_vocab_ids'],
'masked_lm_positions':
record['masked_lm_positions'],
'masked_lm_positions_teacher_tvocab':
record['masked_lm_positions_teacher_tvocab'],
'masked_lm_positions_teacher_svocab':
record['masked_lm_positions_teacher_svocab'],
'masked_lm_ids':
record['masked_lm_ids'],
'masked_lm_ids_teacher_tvocab':
record['masked_lm_ids_teacher_tvocab'],
'masked_lm_ids_teacher_svocab':
record['masked_lm_ids_teacher_svocab'],
}
if self._params.use_v2_feature_names:
x['input_word_ids'] = record['input_word_ids']
x['input_word_ids_teacher'] = record['input_word_ids_teacher']
x['input_type_ids'] = record['input_type_ids']
x['input_type_ids_teacher'] = record['input_type_ids_teacher']
else:
x['input_word_ids'] = record['input_ids']
x['input_word_ids_teacher'] = record['input_ids_teacher']
x['input_type_ids'] = record['segment_ids']
x['input_type_ids_teacher'] = record['segment_ids_teacher']
if self._use_next_sentence_label:
x['next_sentence_labels'] = record['next_sentence_labels']
if self._use_position_id:
x['position_ids'] = record['position_ids']
return x
def load(self, input_context: Optional[tf.distribute.InputContext] = None):
"""Returns a tf.dataset.Dataset."""
reader = input_reader.InputReader(
params=self._params, decoder_fn=self._decode, parser_fn=self._parse)
return reader.read(input_context)
official/projects/exbert/exbert_pretrain_dataloader_test.py deleted 100644 → 0
View file @ afe1d902
# Copyright 2021 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.projects.exbert.exbert_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
from official.projects.exbert import exbert_pretrain_dataloader
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def _create_fake_exbert_teacher_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)
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))
features["input_mask_teacher"] = create_int_feature(np.ones_like(input_ids))
features["input_seg_vocab_ids"] = create_int_feature(
np.ones_like(input_ids))
if use_v2_feature_names:
features["input_word_ids"] = create_int_feature(input_ids)
features["input_word_ids_teacher"] = create_int_feature(input_ids)
features["input_type_ids"] = create_int_feature(np.ones_like(input_ids))
features["input_type_ids_teacher"] = create_int_feature(
np.ones_like(input_ids))
else:
features["input_ids"] = create_int_feature(input_ids)
features["input_ids_teacher"] = create_int_feature(input_ids)
features["segment_ids"] = create_int_feature(np.ones_like(input_ids))
features["segment_ids_teacher"] = 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_positions_teacher_tvocab"] = create_int_feature(
np.random.randint(100, size=(max_predictions_per_seq)))
features["masked_lm_positions_teacher_svocab"] = 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_ids_teacher_tvocab"] = create_int_feature(
np.random.randint(100, size=(max_predictions_per_seq)))
features["masked_lm_ids_teacher_svocab"] = create_int_feature(
np.random.randint(100, size=(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 ExbertTeacherPretrainDataTest(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_exbert_teacher_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 = exbert_pretrain_dataloader.ExbertTeacherPretrainDataLoader(
data_config).load()
features = next(iter(dataset))
self.assertLen(features,
13 + int(use_next_sentence_label) + int(use_position_id))
self.assertIn("input_word_ids", features)
self.assertIn("input_word_ids_teacher", features)
self.assertIn("input_mask", features)
self.assertIn("input_mask_teacher", features)
self.assertIn("input_seg_vocab_ids", features)
self.assertIn("input_type_ids", features)
self.assertIn("input_type_ids_teacher", features)
self.assertIn("masked_lm_positions", features)
self.assertIn("masked_lm_positions_teacher_tvocab", features)
self.assertIn("masked_lm_positions_teacher_svocab", features)
self.assertIn("masked_lm_ids", features)
self.assertIn("masked_lm_ids_teacher_tvocab", features)
self.assertIn("masked_lm_ids_teacher_svocab", 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()
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment