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# 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.
# ==============================================================================
"""Loads dataset for the BERT pretraining task."""
from typing import Mapping, Optional
import tensorflow as tf
from official.core import input_reader
class BertPretrainDataLoader:
"""A class to load dataset for bert pretraining task."""
def __init__(self, params):
"""Inits `BertPretrainDataLoader` 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 _decode(self, record: tf.Tensor):
"""Decodes a serialized tf.Example."""
name_to_features = {
'input_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'input_mask':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'segment_ids':
tf.io.FixedLenFeature([self._seq_length], tf.int64),
'masked_lm_positions':
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_weights':
tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.float32),
}
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)
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_word_ids': record['input_ids'],
'input_mask': record['input_mask'],
'input_type_ids': record['segment_ids'],
'masked_lm_positions': record['masked_lm_positions'],
'masked_lm_ids': record['masked_lm_ids'],
'masked_lm_weights': record['masked_lm_weights'],
}
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)
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