Parallel data creation for BERT in tensorflow 2. Code heavily borrowed from google-research/bert

create_bert_pretraining_data.py

"""Create masked LM/next sentence masked_lm TF examples for BERT."""
import collections
import random
import tokenization
import tensorflow as tf
import argparse
import functools
import os
from utils import create_int_feature
from multiprocessing import Pool

parser = argparse.ArgumentParser('CLI Parser for creating pretraining data '
                                 'for BERT')

parser.add_argument('--input_file', help='Regex for input file(s)')
parser.add_argument('--out_dir', help='Directory for output file(s)')
parser.add_argument("--vocab", default="../../pretrained_models/"
                    "wwm_uncased_L-24_H-1024_A-16/vocab.txt",
                    help="Path to vocab file.")
parser.add_argument("--uppercase", default=False, action="store_true",
                    help="Enable to allow uppercase letters in the input")
parser.add_argument("--mL", help="Sequence length. Shorter ones are padded,"
                    "longer are truncated.", type=int,
                    default=256)
parser.add_argument('--max_predictions', type=int, default=40,
                    help='Maximum number of predictions in one sentence')
parser.add_argument('--dupe_factor', type=int, default=10,
                    help='Number of times to duplicate the input data with '
                    'different masks')
parser.add_argument('--masked_lm_prob', type=float, default=0.15,
                    help="Masked LM probability.")
parser.add_argument('--short_seq_prob', type=float, default=0.0,
                    help='Probability of generating shorter sequence')
parser.add_argument('--num_cpus', default=75, type=int,
                    help='Number of cpus to use for the task')


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_instances_to_example_file(instances, output_file, tokenizer, max_seq_length,
                                    max_predictions_per_seq):
    """Create TF example files from `TrainingInstance`s."""
    writer = tf.io.TFRecordWriter(output_file)

    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))

        writer.write(tf_example.SerializeToString())

    writer.close()


def create_training_instances(input_file, 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.
    with tf.io.gfile.GFile(input_file, "r") as reader:
        while True:
            try:
                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)
            except:
                pass

    # 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)

    output_file = os.path.join(args.out_dir, 'train.tf_record_' + input_file.split('/')[1])
    write_instances_to_example_file(instances, output_file, tokenizer, max_seq_length,
                                    max_predictions_per_seq)


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 (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()


if __name__ == "__main__":
    args = parser.parse_args()
    do_lower_case = not args.uppercase
    tokenizer = tokenization.FullTokenizer(
        vocab_file=args.vocab, do_lower_case=do_lower_case)

    input_files = []
    for input_pattern in args.input_file.split(","):
        input_files.extend(tf.io.gfile.glob(input_pattern))

    rng = random.Random(12345)

    p = Pool(args.num_cpus)

    print('Beginning instances creation')

    _create_training_instances = functools.partial(create_training_instances,
        tokenizer=tokenizer,
        max_seq_length=args.mL,
        dupe_factor=args.dupe_factor,
        short_seq_prob=args.short_seq_prob,
        masked_lm_prob=args.masked_lm_prob,
        max_predictions_per_seq=args.max_predictions,
        rng=rng)

    p.map(_create_training_instances, input_files)