create_pretraining_data.py

# coding=utf-8
# Copyright 2018 The Google AI 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.
# Lint as: python2, python3
# coding=utf-8
"""Create masked LM/next sentence masked_lm TF examples for ALBERT."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import random
from albert import tokenization
import numpy as np
import six
from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 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 ALBERT model was trained on.")

flags.DEFINE_string("spm_model_file", None,
                    "The model file for sentence piece tokenization.")

flags.DEFINE_string("input_file_mode", "r",
                    "The data format of the input file.")

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_bool(
    "do_permutation", False,
    "Whether to do the permutation training.")

flags.DEFINE_bool(
    "favor_shorter_ngram", True,
    "Whether to set higher probabilities for sampling shorter ngrams.")

flags.DEFINE_bool(
    "random_next_sentence", False,
    "Whether to use the sentence that's right before the current sentence "
    "as the negative sample for next sentence prection, rather than using "
    "sentences from other random documents.")

flags.DEFINE_integer("max_seq_length", 512, "Maximum sequence length.")

flags.DEFINE_integer("ngram", 3, "Maximum number of ngrams to mask.")

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", 40,
    "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, token_boundary):
    self.tokens = tokens
    self.segment_ids = segment_ids
    self.is_random_next = is_random_next
    self.token_boundary = token_boundary
    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 += "token_boundary: %s\n" % (" ".join(
        [str(x) for x in self.token_boundary]))
    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)
    token_boundary = list(instance.token_boundary)
    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)
      token_boundary.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)

    multiplier = 1 + int(FLAGS.do_permutation)
    while len(masked_lm_positions) < max_predictions_per_seq * multiplier:
      masked_lm_positions.append(0)
      masked_lm_ids.append(0)
      masked_lm_weights.append(0.0)

    sentence_order_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["token_boundary"] = create_int_feature(token_boundary)
    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)
    # Note: We keep this feature name `next_sentence_labels` to be compatible
    # with the original data created by lanzhzh@. However, in the ALBERT case
    # it does contain sentence_order_label.
    features["next_sentence_labels"] = create_int_feature(
        [sentence_order_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, FLAGS.input_file_mode) as reader:
      while True:
        line = reader.readline()
        if not FLAGS.spm_model_file:
          line = tokenization.convert_to_unicode(line)
        if not line:
          break
        if FLAGS.spm_model_file:
          line = tokenization.preprocess_text(line, lower=FLAGS.do_lower_case)
        else:
          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 \
            (FLAGS.random_next_sentence and 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
        elif not FLAGS.random_next_sentence and rng.random() < 0.5:
          is_random_next = True
          for j in range(a_end, len(current_chunk)):
            tokens_b.extend(current_chunk[j])
          # Note(mingdachen): in this case, we just swap tokens_a and tokens_b
          tokens_a, tokens_b = tokens_b, tokens_a
        # 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, token_boundary) = 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,
            token_boundary=token_boundary,
            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 _is_start_piece_sp(piece):
  """Check if the current word piece is the starting piece (sentence piece)."""
  special_pieces = set(list('!"#$%&\"()*+,-./:;?@[\\]^_`{|}~'))
  special_pieces.add(u"€".encode("utf-8"))
  special_pieces.add(u"£".encode("utf-8"))
  # Note(mingdachen):
  # For foreign characters, we always treat them as a whole piece.
  english_chars = set(list("abcdefghijklmnopqrstuvwxyz"))
  if (six.ensure_str(piece).startswith("▁") or
      six.ensure_str(piece).startswith("<") or piece in special_pieces or
      not all([i.lower() in english_chars.union(special_pieces)
               for i in piece])):
    return True
  else:
    return False


def _is_start_piece_bert(piece):
  """Check if the current word piece is the starting piece (BERT)."""
  # 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.
  return not six.ensure_str(piece).startswith("##")


def is_start_piece(piece):
  if FLAGS.spm_model_file:
    return _is_start_piece_sp(piece)
  else:
    return _is_start_piece_bert(piece)


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 = []
  # Note(mingdachen): We create a list for recording if the piece is
  # the starting piece of current token, where 1 means true, so that
  # on-the-fly whole word masking is possible.
  token_boundary = [0] * len(tokens)

  for (i, token) in enumerate(tokens):
    if token == "[CLS]" or token == "[SEP]":
      token_boundary[i] = 1
      continue
    # Whole Word Masking means that if we mask all of the wordpieces
    # corresponding to an original word.
    #
    # 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
        not is_start_piece(token)):
      cand_indexes[-1].append(i)
    else:
      cand_indexes.append([i])
      if is_start_piece(token):
        token_boundary[i] = 1

  output_tokens = list(tokens)

  masked_lm_positions = []
  masked_lm_labels = []

  if masked_lm_prob == 0:
    return (output_tokens, masked_lm_positions,
            masked_lm_labels, token_boundary)

  num_to_predict = min(max_predictions_per_seq,
                       max(1, int(round(len(tokens) * masked_lm_prob))))

  # Note(mingdachen):
  # By default, we set the probilities to favor shorter ngram sequences.
  ngrams = np.arange(1, FLAGS.ngram + 1, dtype=np.int64)
  pvals = 1. / np.arange(1, FLAGS.ngram + 1)
  pvals /= pvals.sum(keepdims=True)

  if not FLAGS.favor_shorter_ngram:
    pvals = pvals[::-1]

  ngram_indexes = []
  for idx in range(len(cand_indexes)):
    ngram_index = []
    for n in ngrams:
      ngram_index.append(cand_indexes[idx:idx+n])
    ngram_indexes.append(ngram_index)

  rng.shuffle(ngram_indexes)

  masked_lms = []
  covered_indexes = set()
  for cand_index_set in ngram_indexes:
    if len(masked_lms) >= num_to_predict:
      break
    if not cand_index_set:
      continue
    # Note(mingdachen):
    # Skip current piece if they are covered in lm masking or previous ngrams.
    for index_set in cand_index_set[0]:
      for index in index_set:
        if index in covered_indexes:
          continue

    n = np.random.choice(ngrams[:len(cand_index_set)],
                         p=pvals[:len(cand_index_set)] /
                         pvals[:len(cand_index_set)].sum(keepdims=True))
    index_set = sum(cand_index_set[n - 1], [])
    n -= 1
    # Note(mingdachen):
    # Repeatedly looking for a candidate that does not exceed the
    # maximum number of predictions by trying shorter ngrams.
    while len(masked_lms) + len(index_set) > num_to_predict:
      if n == 0:
        break
      index_set = sum(cand_index_set[n - 1], [])
      n -= 1
    # 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

  rng.shuffle(ngram_indexes)

  select_indexes = set()
  if FLAGS.do_permutation:
    for cand_index_set in ngram_indexes:
      if len(select_indexes) >= num_to_predict:
        break
      if not cand_index_set:
        continue
      # Note(mingdachen):
      # Skip current piece if they are covered in lm masking or previous ngrams.
      for index_set in cand_index_set[0]:
        for index in index_set:
          if index in covered_indexes or index in select_indexes:
            continue

      n = np.random.choice(ngrams[:len(cand_index_set)],
                           p=pvals[:len(cand_index_set)] /
                           pvals[:len(cand_index_set)].sum(keepdims=True))
      index_set = sum(cand_index_set[n - 1], [])
      n -= 1

      while len(select_indexes) + len(index_set) > num_to_predict:
        if n == 0:
          break
        index_set = sum(cand_index_set[n - 1], [])
        n -= 1
      # If adding a whole-word mask would exceed the maximum number of
      # predictions, then just skip this candidate.
      if len(select_indexes) + len(index_set) > num_to_predict:
        continue
      is_any_index_covered = False
      for index in index_set:
        if index in covered_indexes or index in select_indexes:
          is_any_index_covered = True
          break
      if is_any_index_covered:
        continue
      for index in index_set:
        select_indexes.add(index)
    assert len(select_indexes) <= num_to_predict

    select_indexes = sorted(select_indexes)
    permute_indexes = list(select_indexes)
    rng.shuffle(permute_indexes)
    orig_token = list(output_tokens)

    for src_i, tgt_i in zip(select_indexes, permute_indexes):
      output_tokens[src_i] = orig_token[tgt_i]
      masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i]))

  masked_lms = sorted(masked_lms, key=lambda x: x.index)

  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, token_boundary)


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,
      spm_model_file=FLAGS.spm_model_file)

  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)

  tf.logging.info("number of instances: %i", len(instances))

  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()