470 lines
16 KiB
Python
470 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Create masked LM/next sentence masked_lm TF examples for BERT."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import random
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import tokenization
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import tensorflow as tf
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flags = tf.flags
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FLAGS = flags.FLAGS
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flags.DEFINE_string("input_file", None,
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"Input raw text file (or comma-separated list of files).")
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flags.DEFINE_string(
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"output_file", None,
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"Output TF example file (or comma-separated list of files).")
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flags.DEFINE_string("vocab_file", None,
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"The vocabulary file that the BERT model was trained on.")
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flags.DEFINE_bool(
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"do_lower_case", True,
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"Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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flags.DEFINE_bool(
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"do_whole_word_mask", False,
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"Whether to use whole word masking rather than per-WordPiece masking.")
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flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")
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flags.DEFINE_integer("max_predictions_per_seq", 20,
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"Maximum number of masked LM predictions per sequence.")
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flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
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flags.DEFINE_integer(
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"dupe_factor", 10,
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"Number of times to duplicate the input data (with different masks).")
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flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
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flags.DEFINE_float(
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"short_seq_prob", 0.1,
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"Probability of creating sequences which are shorter than the "
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"maximum length.")
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class TrainingInstance(object):
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"""A single training instance (sentence pair)."""
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def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
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is_random_next):
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self.tokens = tokens
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self.segment_ids = segment_ids
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self.is_random_next = is_random_next
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self.masked_lm_positions = masked_lm_positions
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self.masked_lm_labels = masked_lm_labels
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def __str__(self):
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s = ""
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s += "tokens: %s\n" % (" ".join(
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[tokenization.printable_text(x) for x in self.tokens]))
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s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
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s += "is_random_next: %s\n" % self.is_random_next
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s += "masked_lm_positions: %s\n" % (" ".join(
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[str(x) for x in self.masked_lm_positions]))
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s += "masked_lm_labels: %s\n" % (" ".join(
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[tokenization.printable_text(x) for x in self.masked_lm_labels]))
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s += "\n"
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return s
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def __repr__(self):
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return self.__str__()
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def write_instance_to_example_files(instances, tokenizer, max_seq_length,
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max_predictions_per_seq, output_files):
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"""Create TF example files from `TrainingInstance`s."""
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writers = []
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for output_file in output_files:
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writers.append(tf.python_io.TFRecordWriter(output_file))
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writer_index = 0
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total_written = 0
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for (inst_index, instance) in enumerate(instances):
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input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
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input_mask = [1] * len(input_ids)
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segment_ids = list(instance.segment_ids)
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assert len(input_ids) <= max_seq_length
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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masked_lm_positions = list(instance.masked_lm_positions)
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masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
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masked_lm_weights = [1.0] * len(masked_lm_ids)
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while len(masked_lm_positions) < max_predictions_per_seq:
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masked_lm_positions.append(0)
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masked_lm_ids.append(0)
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masked_lm_weights.append(0.0)
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next_sentence_label = 1 if instance.is_random_next else 0
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features = collections.OrderedDict()
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features["input_ids"] = create_int_feature(input_ids)
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features["input_mask"] = create_int_feature(input_mask)
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features["segment_ids"] = create_int_feature(segment_ids)
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features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
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features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
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features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
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features["next_sentence_labels"] = create_int_feature([next_sentence_label])
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tf_example = tf.train.Example(features=tf.train.Features(feature=features))
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writers[writer_index].write(tf_example.SerializeToString())
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writer_index = (writer_index + 1) % len(writers)
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total_written += 1
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if inst_index < 20:
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tf.logging.info("*** Example ***")
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tf.logging.info("tokens: %s" % " ".join(
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[tokenization.printable_text(x) for x in instance.tokens]))
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for feature_name in features.keys():
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feature = features[feature_name]
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values = []
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if feature.int64_list.value:
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values = feature.int64_list.value
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elif feature.float_list.value:
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values = feature.float_list.value
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tf.logging.info(
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"%s: %s" % (feature_name, " ".join([str(x) for x in values])))
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for writer in writers:
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writer.close()
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tf.logging.info("Wrote %d total instances", total_written)
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def create_int_feature(values):
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feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
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return feature
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def create_float_feature(values):
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feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
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return feature
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def create_training_instances(input_files, tokenizer, max_seq_length,
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dupe_factor, short_seq_prob, masked_lm_prob,
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max_predictions_per_seq, rng):
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"""Create `TrainingInstance`s from raw text."""
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all_documents = [[]]
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# Input file format:
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# (1) One sentence per line. These should ideally be actual sentences, not
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# entire paragraphs or arbitrary spans of text. (Because we use the
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# sentence boundaries for the "next sentence prediction" task).
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# (2) Blank lines between documents. Document boundaries are needed so
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# that the "next sentence prediction" task doesn't span between documents.
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for input_file in input_files:
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with tf.gfile.GFile(input_file, "r") as reader:
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while True:
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line = tokenization.convert_to_unicode(reader.readline())
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if not line:
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break
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line = line.strip()
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# Empty lines are used as document delimiters
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if not line:
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all_documents.append([])
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tokens = tokenizer.tokenize(line)
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if tokens:
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all_documents[-1].append(tokens)
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# Remove empty documents
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all_documents = [x for x in all_documents if x]
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rng.shuffle(all_documents)
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vocab_words = list(tokenizer.vocab.keys())
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instances = []
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for _ in range(dupe_factor):
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for document_index in range(len(all_documents)):
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instances.extend(
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create_instances_from_document(
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all_documents, document_index, max_seq_length, short_seq_prob,
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masked_lm_prob, max_predictions_per_seq, vocab_words, rng))
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rng.shuffle(instances)
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return instances
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def create_instances_from_document(
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all_documents, document_index, max_seq_length, short_seq_prob,
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masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
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"""Creates `TrainingInstance`s for a single document."""
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document = all_documents[document_index]
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# Account for [CLS], [SEP], [SEP]
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max_num_tokens = max_seq_length - 3
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# We *usually* want to fill up the entire sequence since we are padding
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# to `max_seq_length` anyways, so short sequences are generally wasted
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# computation. However, we *sometimes*
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# (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
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# sequences to minimize the mismatch between pre-training and fine-tuning.
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# The `target_seq_length` is just a rough target however, whereas
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# `max_seq_length` is a hard limit.
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target_seq_length = max_num_tokens
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if rng.random() < short_seq_prob:
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target_seq_length = rng.randint(2, max_num_tokens)
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# We DON'T just concatenate all of the tokens from a document into a long
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# sequence and choose an arbitrary split point because this would make the
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# next sentence prediction task too easy. Instead, we split the input into
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# segments "A" and "B" based on the actual "sentences" provided by the user
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# input.
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instances = []
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current_chunk = []
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current_length = 0
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i = 0
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while i < len(document):
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segment = document[i]
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current_chunk.append(segment)
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current_length += len(segment)
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if i == len(document) - 1 or current_length >= target_seq_length:
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if current_chunk:
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# `a_end` is how many segments from `current_chunk` go into the `A`
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# (first) sentence.
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a_end = 1
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if len(current_chunk) >= 2:
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a_end = rng.randint(1, len(current_chunk) - 1)
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tokens_a = []
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for j in range(a_end):
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tokens_a.extend(current_chunk[j])
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tokens_b = []
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# Random next
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is_random_next = False
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if len(current_chunk) == 1 or rng.random() < 0.5:
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is_random_next = True
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target_b_length = target_seq_length - len(tokens_a)
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# This should rarely go for more than one iteration for large
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# corpora. However, just to be careful, we try to make sure that
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# the random document is not the same as the document
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# we're processing.
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for _ in range(10):
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random_document_index = rng.randint(0, len(all_documents) - 1)
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if random_document_index != document_index:
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break
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random_document = all_documents[random_document_index]
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random_start = rng.randint(0, len(random_document) - 1)
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for j in range(random_start, len(random_document)):
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tokens_b.extend(random_document[j])
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if len(tokens_b) >= target_b_length:
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break
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# We didn't actually use these segments so we "put them back" so
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# they don't go to waste.
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num_unused_segments = len(current_chunk) - a_end
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i -= num_unused_segments
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# Actual next
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else:
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is_random_next = False
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for j in range(a_end, len(current_chunk)):
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tokens_b.extend(current_chunk[j])
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truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
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assert len(tokens_a) >= 1
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assert len(tokens_b) >= 1
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tokens = []
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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in tokens_a:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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for token in tokens_b:
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tokens.append(token)
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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(tokens, masked_lm_positions,
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masked_lm_labels) = create_masked_lm_predictions(
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tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
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instance = TrainingInstance(
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tokens=tokens,
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segment_ids=segment_ids,
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is_random_next=is_random_next,
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masked_lm_positions=masked_lm_positions,
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masked_lm_labels=masked_lm_labels)
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instances.append(instance)
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current_chunk = []
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current_length = 0
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i += 1
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return instances
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MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
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["index", "label"])
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def create_masked_lm_predictions(tokens, masked_lm_prob,
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max_predictions_per_seq, vocab_words, rng):
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"""Creates the predictions for the masked LM objective."""
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cand_indexes = []
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for (i, token) in enumerate(tokens):
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if token == "[CLS]" or token == "[SEP]":
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continue
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# Whole Word Masking means that if we mask all of the wordpieces
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# corresponding to an original word. When a word has been split into
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# WordPieces, the first token does not have any marker and any subsequence
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# tokens are prefixed with ##. So whenever we see the ## token, we
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# append it to the previous set of word indexes.
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#
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# Note that Whole Word Masking does *not* change the training code
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# at all -- we still predict each WordPiece independently, softmaxed
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# over the entire vocabulary.
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||
|
|
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()
|