bert/create_pretraining_data.py at master · google-research/bert

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# coding=utf-8

# 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_import

from __future__ import division

from __future__ import print_function

import collections

import random

import tokenization

import tensorflow as tf

flags = tf.flags

FLAGS = flags.FLAGS

flags.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_bool(

"do_whole_word_mask", False,

"Whether to use whole word masking rather than per-WordPiece masking.")

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 = tokens

self.segment_ids = segment_ids

self.is_random_next = is_random_next

self.masked_lm_positions = masked_lm_positions

self.masked_lm_labels = masked_lm_labels

def __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_next

s += "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 s

def __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 = 0

total_written = 0

for (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_length

while len(input_ids) < max_seq_length:

input_ids.append(0)

input_mask.append(0)

segment_ids.append(0)

assert len(input_ids) == max_seq_length

assert len(input_mask) == max_seq_length

assert len(segment_ids) == max_seq_length

masked_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 0

features = 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 += 1

if 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.value

elif feature.float_list.value:

values = feature.float_list.value

tf.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=list(values)))

return feature

def create_float_feature(values):

feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))

return feature

def 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:

break

line = line.strip()

# Empty lines are used as document delimiters

if not line:

all_documents.append([])

tokens = tokenizer.tokenize(line)

if tokens:

all_documents[-1].append(tokens)

# Remove empty documents

all_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 instances

def 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_tokens

if 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 = 0

i = 0

while 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 = 1

if 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 next

is_random_next = False

if len(current_chunk) == 1 or rng.random() < 0.5:

is_random_next = True

target_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:

break

random_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_end

i -= num_unused_segments

# Actual next

else:

is_random_next = False

for 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) >= 1

assert len(tokens_b) >= 1

tokens = []

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 = 0

i += 1

return instances

MaskedLmInstance = 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]":

continue

# Whole Word Masking means that if we mask all of the wordpieces

# corresponding to an original word. When a word has been split into

# WordPieces, the first token does not have any marker and any subsequence

# tokens are prefixed with ##. So whenever we see the ## token, we

# append it to the previous set of word indexes.

# Note that Whole Word Masking does *not* change the training code

# at all -- we still predict each WordPiece independently, softmaxed

# over the entire vocabulary.

if (FLAGS.do_whole_word_mask and len(cand_indexes) >= 1 and

token.startswith("##")):

cand_indexes[-1].append(i)

else:

cand_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_set in cand_indexes:

if len(masked_lms) >= num_to_predict:

break

# If adding a whole-word mask would exceed the maximum number of

# predictions, then just skip this candidate.

if len(masked_lms) + len(index_set) > num_to_predict:

continue

is_any_index_covered = False

for index in index_set:

if index in covered_indexes:

is_any_index_covered = True

break

if is_any_index_covered:

continue

for index in index_set:

covered_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 original

if rng.random() < 0.5:

masked_token = tokens[index]

# 10% of the time, replace with random word

else:

masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]

output_tokens[index] = masked_token

masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))

assert len(masked_lms) <= num_to_predict

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:

break

trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b

assert 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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