new_creat_pretraining_data.py

# 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_import
from __future__ import division
from __future__ import print_function

import collections
import random
import tokenization
# import tensorflow as tf
import tensorflow.compat.v1 as tf
import re

flags = tf.flags
# flags = tf.flags.FLAGS

FLAGS = flags.FLAGS

flags.DEFINE_string("input_file",
                    './home/2018chenshuomei/code/zhou_bert/bert_master/84_train.txt,./home/2018chenshuomei/code/zhou_bert/bert_master/85_train.txt,./home/2018chenshuomei/code/zhou_bert/bert_master/90_train.txt,./home/2018chenshuomei/code/zhou_bert/bert_master/all_train.txt',
                    "Input raw text file (or comma-separated list of files).")  # ./84data.txt

flags.DEFINE_string(
    "output_file", './home/2018chenshuomei/code/zhou_bert/bert_master/tf_examples.tfrecord',
    "Output TF example file (or comma-separated list of files).")

flags.DEFINE_string("vocab_file", './home/2018chenshuomei/code/zhou_bert/bert_master/chinese/vocab.txt',
                    "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", True,
    "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", 5,
    "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)  # 拆分成char
                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())  # vocab.txt集合成list
    instances = []
    for _ in range(dupe_factor):  # 10次循环重复创造数据集
        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)  # 句子长度在2~125之间

    # 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 i % 2 == 1:  # 最后一条或者是奇数条 进入程序
            try:
                assert len(current_chunk) == 2
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A`
                    # (first) sentence.
                    tokens_a = []
                    tokens_a.extend(current_chunk[0])
                    tokens_b = []
                    # Random next
                    is_random_next = False
                    if rng.random() < 0.5:  # 进行预测要素是否替换操作，<0.5是进替换
                        is_random_next = True
                        target_b_length = target_seq_length - len(tokens_a)
                        line = ''.join(current_chunk[1]).split('|')
                        a = rng.randint(1, max(len(line) - 2, 1))
                        label = 0
                        for ia, char in enumerate(current_chunk[1]):
                            if '|' == char:
                                label += 1
                                if label == a:
                                    start = ia
                                elif label == a + 1:
                                    end = ia
                                    break
                                elif label > a + 1:
                                    break

                        new_element = current_chunk[1][start:end]
                        for ii, char in enumerate(current_chunk[1][:]):
                            if ii >= start:
                                for _ in range(end - start):
                                    current_chunk[1].pop(ii)
                                break
                        current_chunk[1].extend(new_element)
                        tokens_b.extend(current_chunk[1])

                        # 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.
                        """
                        # 这里暂时不使用全部文档检索下一句预测，而是只预测下一句话顺序是否改变
                        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
                        tokens_b.extend(current_chunk[1])
                    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
            except:
                print('sth wrong')
        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 = []
    label = 0
    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.
        """
        这里采用10一个要素的全部内容放在一起
        """
        if (FLAGS.do_whole_word_mask and len(cand_indexes) >= 1 and
                token.startswith("##")):
            cand_indexes[-1].append(i)
        else:
            if label == 1 and token != '|':
                cand_indexes[-1].append(i)
            elif label == 1 and token == '|':
                cand_indexes.append([i])
                label = 0
            else:
                cand_indexes.append([i])
        if label == 0 and token == '|' and rng.random() < 0.1:
            label = 1

    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()
    print('finish')