# coding=utf-8 # Copyright 2018 The Google AI Language 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. """Create masked LM/next sentence masked_lm TF examples for BERT.""" from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging import os import random from io import open import h5py import numpy as np from tqdm import tqdm, trange from tokenization import BertTokenizer import tokenization as tokenization import random import collections 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_file(instances, tokenizer, max_seq_length, max_predictions_per_seq, output_file): """Create TF example files from `TrainingInstance`s.""" total_written = 0 features = collections.OrderedDict() num_instances = len(instances) features["input_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") features["input_mask"] = np.zeros([num_instances, max_seq_length], dtype="int32") features["segment_ids"] = np.zeros([num_instances, max_seq_length], dtype="int32") features["masked_lm_positions"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") features["masked_lm_ids"] = np.zeros([num_instances, max_predictions_per_seq], dtype="int32") features["next_sentence_labels"] = np.zeros(num_instances, dtype="int32") for inst_index, instance in enumerate(tqdm(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["input_ids"][inst_index] = input_ids features["input_mask"][inst_index] = input_mask features["segment_ids"][inst_index] = segment_ids features["masked_lm_positions"][inst_index] = masked_lm_positions features["masked_lm_ids"][inst_index] = masked_lm_ids features["next_sentence_labels"][inst_index] = next_sentence_label 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]))) print("saving data") f= h5py.File(output_file, 'w') f.create_dataset("input_ids", data=features["input_ids"], dtype='i4', compression='gzip') f.create_dataset("input_mask", data=features["input_mask"], dtype='i1', compression='gzip') f.create_dataset("segment_ids", data=features["segment_ids"], dtype='i1', compression='gzip') f.create_dataset("masked_lm_positions", data=features["masked_lm_positions"], dtype='i4', compression='gzip') f.create_dataset("masked_lm_ids", data=features["masked_lm_ids"], dtype='i4', compression='gzip') f.create_dataset("next_sentence_labels", data=features["next_sentence_labels"], dtype='i1', compression='gzip') f.flush() f.close() 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: print("creating instance from {}".format(input_file)) with open(input_file, "r") 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 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 in cand_indexes: if len(masked_lms) >= num_to_predict: break if index in covered_indexes: continue 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])) 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(): parser = argparse.ArgumentParser() ## Required parameters parser.add_argument("--vocab_file", default=None, type=str, required=True, help="The vocabulary the BERT model will train on.") parser.add_argument("--input_file", default=None, type=str, required=True, help="The input train corpus. can be directory with .txt files or a path to a single file") parser.add_argument("--output_file", default=None, type=str, required=True, help="The output file where the model checkpoints will be written.") ## Other parameters # str parser.add_argument("--bert_model", default="bert-large-uncased", type=str, required=False, help="Bert pre-trained model selected in the list: bert-base-uncased, " "bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.") #int parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after WordPiece tokenization. \n" "Sequences longer than this will be truncated, and sequences shorter \n" "than this will be padded.") parser.add_argument("--dupe_factor", default=10, type=int, help="Number of times to duplicate the input data (with different masks).") parser.add_argument("--max_predictions_per_seq", default=20, type=int, help="Maximum sequence length.") # floats parser.add_argument("--masked_lm_prob", default=0.15, type=float, help="Masked LM probability.") parser.add_argument("--short_seq_prob", default=0.1, type=float, help="Probability to create a sequence shorter than maximum sequence length") parser.add_argument("--do_lower_case", action='store_true', default=True, help="Whether to lower case the input text. True for uncased models, False for cased models.") parser.add_argument('--random_seed', type=int, default=12345, help="random seed for initialization") args = parser.parse_args() tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) input_files = [] if os.path.isfile(args.input_file): input_files.append(args.input_file) elif os.path.isdir(args.input_file): 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') )] else: raise ValueError("{} is not a valid path".format(args.input_file)) rng = random.Random(args.random_seed) instances = create_training_instances( input_files, tokenizer, args.max_seq_length, args.dupe_factor, args.short_seq_prob, args.masked_lm_prob, args.max_predictions_per_seq, rng) output_file = args.output_file write_instance_to_example_file(instances, tokenizer, args.max_seq_length, args.max_predictions_per_seq, output_file) if __name__ == "__main__": main()