Internal change

PiperOrigin-RevId: 279782659

official/nlp/bert/README.md

@@ -122,9 +122,10 @@ supported by Google Cloud TPU team yet.
 ### Pre-training
 
 There is no change to generate pre-training data. Please use the script
-[`create_pretraining_data.py`](https://github.com/google-research/bert/blob/master/create_pretraining_data.py)
-inside [BERT research repo](https://github.com/google-research/bert) to get
-processed pre-training data.
+[`create_pretraining_data.py`](create_pretraining_data.py)
+which is essentially branched from [BERT research repo](https://github.com/google-research/bert)
+to get processed pre-training data and it adapts to TF2 symbols and python3
+compatibility.
 
 
 ### Fine-tuning

official/nlp/bert/create_pretraining_data.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# 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
+
+from absl import app
+from absl import flags
+from absl import logging
+import tensorflow as tf
+
+from official.nlp.bert import tokenization
+
+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_bool(
+    "gzip_compress", False,
+    "Whether to use `GZIP` compress option to get compressed TFRecord files.")
+
+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.io.TFRecordWriter(
+            output_file, options="GZIP" if FLAGS.gzip_compress else ""))
+
+  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:
+      logging.info("*** Example ***")
+      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
+        logging.info("%s: %s", feature_name, " ".join([str(x) for x in values]))
+
+  for writer in writers:
+    writer.close()
+
+  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.io.gfile.GFile(input_file, "rb") 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(_):
+  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.io.gfile.glob(input_pattern))
+
+  logging.info("*** Reading from input files ***")
+  for input_file in input_files:
+    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(",")
+  logging.info("*** Writing to output files ***")
+  for output_file in output_files:
+    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")
+  app.run(main)