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bert-create_pretraining_data代码学习
# coding=utf-8# Copyright 2018 The Google AI Language Team Authors.## 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."""Create masked LM/next sentence masked_lm TF examples for BERT."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport collectionsimport randomimport tokenizationimport tensorflow as tfflags = tf.flagsFLAGS = flags.FLAGSflags.DEFINE_string("input_file", None,"Input raw text file (or comma-separated list of files).")flags.DEFINE_string("output_file", None,"Output TF example file (or comma-separated list of files).")flags.DEFINE_string("vocab_file", None,"The vocabulary file that the BERT model was trained on.")flags.DEFINE_bool("do_lower_case", True,"Whether to lower case the input text. Should be True for uncased ""models and False for cased models.")flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")flags.DEFINE_integer("max_predictions_per_seq", 20,"Maximum number of masked LM predictions per sequence.")flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")flags.DEFINE_integer("dupe_factor", 10,"Number of times to duplicate the input data (with different masks).")flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")flags.DEFINE_float("short_seq_prob", 0.1,"Probability of creating sequences which are shorter than the ""maximum length.")class TrainingInstance(object):
"""A single training instance (sentence pair)."""def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,is_random_next):
self.tokens = tokensself.segment_ids = segment_idsself.is_random_next = is_random_nextself.masked_lm_positions = masked_lm_positionsself.masked_lm_labels = masked_lm_labelsdef __str__(self):
s = ""s += "tokens: %s\n" % (" ".join([tokenization.printable_text(x) for x in self.tokens]))s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))s += "is_random_next: %s\n" % self.is_random_nexts += "masked_lm_positions: %s\n" % (" ".join([str(x) for x in self.masked_lm_positions]))s += "masked_lm_labels: %s\n" % (" ".join([tokenization.printable_text(x) for x in self.masked_lm_labels]))s += "\n"return sdef __repr__(self):
return self.__str__()def write_instance_to_example_files(instances, tokenizer, max_seq_length,max_predictions_per_seq, output_files):
"""Create TF example files from `TrainingInstance`s."""writers = []for output_file in output_files:
writers.append(tf.python_io.TFRecordWriter(output_file))writer_index = 0total_written = 0for (inst_index, instance) in enumerate(instances):input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)input_mask = [1] * len(input_ids)segment_ids = list(instance.segment_ids)assert len(input_ids) <= max_seq_lengthwhile len(input_ids) < max_seq_length:
input_ids.append(0)input_mask.append(0)segment_ids.append(0)assert len(input_ids) == max_seq_lengthassert len(input_mask) == max_seq_lengthassert len(segment_ids) == max_seq_lengthmasked_lm_positions = list(instance.masked_lm_positions)masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)masked_lm_weights = [1.0] * len(masked_lm_ids)while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)masked_lm_ids.append(0)masked_lm_weights.append(0.0)next_sentence_label = 1 if instance.is_random_next else 0features = collections.OrderedDict()features["input_ids"] = create_int_feature(input_ids)features["input_mask"] = create_int_feature(input_mask)features["segment_ids"] = create_int_feature(segment_ids)features["masked_lm_positions"] = create_int_feature(masked_lm_positions)features["masked_lm_ids"] = create_int_feature(masked_lm_ids)features["masked_lm_weights"] = create_float_feature(masked_lm_weights)features["next_sentence_labels"] = create_int_feature([next_sentence_label])tf_example = tf.train.Example(features=tf.train.Features(feature=features))writers[writer_index].write(tf_example.SerializeToString())writer_index = (writer_index + 1) % len(writers)total_written += 1if inst_index < 20:tf.logging.info("*** Example ***")tf.logging.info("tokens: %s" % " ".join([tokenization.printable_text(x) for x in instance.tokens]))for feature_name in features.keys():feature = features[feature_name]values = []if feature.int64_list.value:values = feature.int64_list.valueelif feature.float_list.value:values = feature.float_list.valuetf.logging.info("%s: %s" % (feature_name, " ".join([str(x) for x in values])))for writer in writers:
writer.close()tf.logging.info("Wrote %d total instances", total_written)def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=https://www.it610.com/article/list(values)))return featuredef create_float_feature(values):
feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))return featuredef create_training_instances(input_files, tokenizer, max_seq_length,dupe_factor, short_seq_prob, masked_lm_prob,max_predictions_per_seq, rng):"""Create `TrainingInstance`s from raw text."""all_documents = [[]]# Input file format:# (1) One sentence per line. These should ideally be actual sentences, not# entire paragraphs or arbitrary spans of text. (Because we use the# sentence boundaries for the "next sentence prediction" task).# (2) Blank lines between documents. Document boundaries are needed so# that the "next sentence prediction" task doesn't span between documents.for input_file in input_files:with tf.gfile.GFile(input_file, "r") as reader:while True:line = tokenization.convert_to_unicode(reader.readline())if not line:
breakline = line.strip()# Empty lines are used as document delimitersif not line:
all_documents.append([])tokens = tokenizer.tokenize(line)if tokens:
all_documents[-1].append(tokens)# Remove empty documentsall_documents = [x for x in all_documents if x]rng.shuffle(all_documents)vocab_words = list(tokenizer.vocab.keys())instances = []for _ in range(dupe_factor):for document_index in range(len(all_documents)):
instances.extend(create_instances_from_document(all_documents, document_index, max_seq_length, short_seq_prob,masked_lm_prob, max_predictions_per_seq, vocab_words, rng))rng.shuffle(instances)return instancesdef create_instances_from_document(all_documents, document_index, max_seq_length, short_seq_prob,masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
"""Creates `TrainingInstance`s for a single document."""document = all_documents[document_index]# Account for [CLS], [SEP], [SEP]max_num_tokens = max_seq_length - 3# We *usually* want to fill up the entire sequence since we are padding# to `max_seq_length` anyways, so short sequences are generally wasted# computation. However, we *sometimes*# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter# sequences to minimize the mismatch between pre-training and fine-tuning.# The `target_seq_length` is just a rough target however, whereas# `max_seq_length` is a hard limit.target_seq_length = max_num_tokensif rng.random() < short_seq_prob:
target_seq_length = rng.randint(2, max_num_tokens)# We DON'T just concatenate all of the tokens from a document into a long# sequence and choose an arbitrary split point because this would make the# next sentence prediction task too easy. Instead, we split the input into# segments "A" and "B" based on the actual "sentences" provided by the user# input.instances = []current_chunk = []current_length = 0i = 0while i < len(document):segment = document[i]current_chunk.append(segment)current_length += len(segment)if i == len(document) - 1 or current_length >= target_seq_length:if current_chunk:# `a_end` is how many segments from `current_chunk` go into the `A`# (first) sentence.a_end = 1if len(current_chunk) >= 2:
a_end = rng.randint(1, len(current_chunk) - 1)tokens_a = []for j in range(a_end):
tokens_a.extend(current_chunk[j])tokens_b = []# Random nextis_random_next = Falseif len(current_chunk) == 1 or rng.random() < 0.5:is_random_next = Truetarget_b_length = target_seq_length - len(tokens_a)# This should rarely go for more than one iteration for large# corpora. However, just to be careful, we try to make sure that# the random document is not the same as the document# we're processing.for _ in range(10):random_document_index = rng.randint(0, len(all_documents) - 1)if random_document_index != document_index:
breakrandom_document = all_documents[random_document_index]random_start = rng.randint(0, len(random_document) - 1)for j in range(random_start, len(random_document)):tokens_b.extend(random_document[j])if len(tokens_b) >= target_b_length:
break# We didn't actually use these segments so we "put them back" so# they don't go to waste.num_unused_segments = len(current_chunk) - a_endi -= num_unused_segments# Actual nextelse:is_random_next = Falsefor j in range(a_end, len(current_chunk)):
tokens_b.extend(current_chunk[j])truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)assert len(tokens_a) >= 1assert len(tokens_b) >= 1tokens = []segment_ids = []tokens.append("[CLS]")segment_ids.append(0)for token in tokens_a:
tokens.append(token)segment_ids.append(0)tokens.append("[SEP]")segment_ids.append(0)for token in tokens_b:
tokens.append(token)segment_ids.append(1)tokens.append("[SEP]")segment_ids.append(1)(tokens, masked_lm_positions,masked_lm_labels) = create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)instance = TrainingInstance(tokens=tokens,segment_ids=segment_ids,is_random_next=is_random_next,masked_lm_positions=masked_lm_positions,masked_lm_labels=masked_lm_labels)instances.append(instance)current_chunk = []current_length = 0i += 1return instancesMaskedLmInstance = collections.namedtuple("MaskedLmInstance",["index", "label"])def create_masked_lm_predictions(tokens, masked_lm_prob,max_predictions_per_seq, vocab_words, rng):
"""Creates the predictions for the masked LM objective."""cand_indexes = []for (i, token) in enumerate(tokens):if token == "[CLS]" or token == "[SEP]":
continuecand_indexes.append(i)rng.shuffle(cand_indexes)output_tokens = list(tokens)num_to_predict = min(max_predictions_per_seq,max(1, int(round(len(tokens) * masked_lm_prob))))masked_lms = []covered_indexes = set()for index in cand_indexes:if len(masked_lms) >= num_to_predict:
breakif index in covered_indexes:
continuecovered_indexes.add(index)masked_token = None# 80% of the time, replace with [MASK]if rng.random() < 0.8:masked_token = "[MASK]"else:# 10% of the time, keep originalif rng.random() < 0.5:masked_token = tokens[index]# 10% of the time, replace with random wordelse:masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]output_tokens[index] = masked_tokenmasked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))masked_lms = sorted(masked_lms, key=lambda x: x.index)masked_lm_positions = []masked_lm_labels = []for p in masked_lms:
masked_lm_positions.append(p.index)masked_lm_labels.append(p.label)return (output_tokens, masked_lm_positions, masked_lm_labels)def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
"""Truncates a pair of sequences to a maximum sequence length."""while True:total_length = len(tokens_a) + len(tokens_b)if total_length <= max_num_tokens:
breaktrunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_bassert len(trunc_tokens) >= 1# We want to sometimes truncate from the front and sometimes from the# back to add more randomness and avoid biases.if rng.random() < 0.5:del trunc_tokens[0]else:trunc_tokens.pop()def main(_):
tf.logging.set_verbosity(tf.logging.INFO)tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)input_files = []for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))tf.logging.info("*** Reading from input files ***")for input_file in input_files:
tf.logging.info("%s", input_file)rng = random.Random(FLAGS.random_seed)instances = create_training_instances(input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor,FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq,rng)output_files = FLAGS.output_file.split(",")tf.logging.info("*** Writing to output files ***")for output_file in output_files:
tf.logging.info("%s", output_file)write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,FLAGS.max_predictions_per_seq, output_files)if __name__ == "__main__":
flags.mark_flag_as_required("input_file")flags.mark_flag_as_required("output_file")flags.mark_flag_as_required("vocab_file")tf.app.run()
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