473 lines
17 KiB
Python
473 lines
17 KiB
Python
# 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, division, print_function, unicode_literals
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import argparse
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import logging
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import os
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import random
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from io import open
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import h5py
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import numpy as np
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from tqdm import tqdm, trange
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from tokenization import BertTokenizer
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import tokenization as tokenization
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import random
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import collections
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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_file(instances, tokenizer, max_seq_length,
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max_predictions_per_seq, output_file):
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"""Create TF example files from `TrainingInstance`s."""
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total_written = 0
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features = collections.OrderedDict()
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num_instances = len(instances)
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features["input_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32")
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features["input_mask"] = np.zeros([num_instances, max_seq_length], dtype="int32")
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features["segment_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32")
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features["masked_lm_positions"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32")
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features["masked_lm_ids"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32")
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features["next_sentence_labels"] = np.zeros(num_instances, dtype="int32")
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for inst_index, instance in enumerate(tqdm(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["input_ids"][inst_index] = input_ids
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features["input_mask"][inst_index] = input_mask
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features["segment_ids"][inst_index] = segment_ids
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features["masked_lm_positions"][inst_index] = masked_lm_positions
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features["masked_lm_ids"][inst_index] = masked_lm_ids
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features["next_sentence_labels"][inst_index] = next_sentence_label
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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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print("saving data")
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f= h5py.File(output_file, 'w')
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f.create_dataset("input_ids", data=features["input_ids"], dtype='i4', compression='gzip')
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f.create_dataset("input_mask", data=features["input_mask"], dtype='i1', compression='gzip')
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f.create_dataset("segment_ids", data=features["segment_ids"], dtype='i1', compression='gzip')
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f.create_dataset("masked_lm_positions", data=features["masked_lm_positions"], dtype='i4', compression='gzip')
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f.create_dataset("masked_lm_ids", data=features["masked_lm_ids"], dtype='i4', compression='gzip')
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f.create_dataset("next_sentence_labels", data=features["next_sentence_labels"], dtype='i1', compression='gzip')
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f.flush()
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f.close()
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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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print("creating instance from {}".format(input_file))
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with open(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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cand_indexes.append(i)
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rng.shuffle(cand_indexes)
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output_tokens = list(tokens)
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num_to_predict = min(max_predictions_per_seq,
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max(1, int(round(len(tokens) * masked_lm_prob))))
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masked_lms = []
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covered_indexes = set()
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for index in cand_indexes:
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if len(masked_lms) >= num_to_predict:
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break
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if index in covered_indexes:
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continue
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covered_indexes.add(index)
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masked_token = None
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# 80% of the time, replace with [MASK]
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if rng.random() < 0.8:
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masked_token = "[MASK]"
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else:
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# 10% of the time, keep original
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if rng.random() < 0.5:
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masked_token = tokens[index]
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# 10% of the time, replace with random word
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else:
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masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
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output_tokens[index] = masked_token
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masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))
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masked_lms = sorted(masked_lms, key=lambda x: x.index)
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masked_lm_positions = []
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masked_lm_labels = []
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for p in masked_lms:
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masked_lm_positions.append(p.index)
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masked_lm_labels.append(p.label)
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return (output_tokens, masked_lm_positions, masked_lm_labels)
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def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
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"""Truncates a pair of sequences to a maximum sequence length."""
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_num_tokens:
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break
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trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
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assert len(trunc_tokens) >= 1
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# We want to sometimes truncate from the front and sometimes from the
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# back to add more randomness and avoid biases.
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if rng.random() < 0.5:
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del trunc_tokens[0]
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else:
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trunc_tokens.pop()
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def main():
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--vocab_file",
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default=None,
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type=str,
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required=True,
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help="The vocabulary the BERT model will train on.")
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parser.add_argument("--input_file",
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default=None,
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type=str,
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required=True,
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help="The input train corpus. can be directory with .txt files or a path to a single file")
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parser.add_argument("--output_file",
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default=None,
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type=str,
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required=True,
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help="The output file where the model checkpoints will be written.")
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## Other parameters
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# str
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parser.add_argument("--bert_model", default="bert-large-uncased", type=str, required=False,
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help="Bert pre-trained model selected in the list: bert-base-uncased, "
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"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
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#int
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parser.add_argument("--max_seq_length",
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default=128,
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type=int,
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help="The maximum total input sequence length after WordPiece tokenization. \n"
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"Sequences longer than this will be truncated, and sequences shorter \n"
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"than this will be padded.")
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parser.add_argument("--dupe_factor",
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default=10,
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type=int,
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help="Number of times to duplicate the input data (with different masks).")
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parser.add_argument("--max_predictions_per_seq",
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default=20,
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type=int,
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help="Maximum sequence length.")
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# floats
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parser.add_argument("--masked_lm_prob",
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default=0.15,
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type=float,
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help="Masked LM probability.")
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parser.add_argument("--short_seq_prob",
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default=0.1,
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type=float,
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help="Probability to create a sequence shorter than maximum sequence length")
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parser.add_argument("--do_lower_case",
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action='store_true',
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default=True,
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help="Whether to lower case the input text. True for uncased models, False for cased models.")
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parser.add_argument('--random_seed',
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type=int,
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default=12345,
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help="random seed for initialization")
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args = parser.parse_args()
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tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
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input_files = []
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if os.path.isfile(args.input_file):
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input_files.append(args.input_file)
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elif os.path.isdir(args.input_file):
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input_files = [os.path.join(args.input_file, f) for f in os.listdir(args.input_file) if (os.path.isfile(os.path.join(args.input_file, f)) and f.endswith('.txt') )]
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else:
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raise ValueError("{} is not a valid path".format(args.input_file))
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rng = random.Random(args.random_seed)
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instances = create_training_instances(
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input_files, tokenizer, args.max_seq_length, args.dupe_factor,
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args.short_seq_prob, args.masked_lm_prob, args.max_predictions_per_seq,
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rng)
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output_file = args.output_file
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write_instance_to_example_file(instances, tokenizer, args.max_seq_length,
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args.max_predictions_per_seq, output_file)
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if __name__ == "__main__":
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main()
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