Convert flags to argparse in 'create_pretraining_data_pytorch.py'
This commit is contained in:
parent
2f49360de9
commit
8627a675cd
|
@ -0,0 +1,429 @@
|
|||
# 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
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import collections
|
||||
import random
|
||||
|
||||
import tokenization
|
||||
import tensorflow as tf
|
||||
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
## Required parameters
|
||||
parser.add_argument("--input_file", default=None, type=str, required=True,
|
||||
help="Input raw text file (or comma-separated list of files).")
|
||||
parser.add_argument("--output_file", default=None, type=str, required=True,
|
||||
help="Output TF example file (or comma-separated list of files).")
|
||||
parser.add_argument("--vocab_file", default=None, type=str, required=True,
|
||||
help="The vocabulary file that the BERT model was trained on.")
|
||||
|
||||
## Other parameters
|
||||
parser.add_argument("--do_lower_case", default=True, type=bool,
|
||||
help="Whether to lower case the input text. Should be True for uncased "
|
||||
"models and False for cased models.")
|
||||
parser.add_argument("--max_seq_length", default=128, type=int, help="Maximum sequence length.")
|
||||
parser.add_argument("--max_predictions_per_seq", default=20, type=int,
|
||||
help="Maximum number of masked LM predictions per sequence.")
|
||||
parser.add_argument("--random_seed", default=12345, type=int, help="Random seed for data generation.")
|
||||
parser.add_argument("--dupe_factor", default=10, type=int,
|
||||
help="Number of times to duplicate the input data (with different masks).")
|
||||
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 of creating sequences which are shorter than the maximum length.")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
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.python_io.TFRecordWriter(output_file))
|
||||
|
||||
writer_index = 0
|
||||
|
||||
total_written = 0
|
||||
for (inst_index, instance) in enumerate(instances):
|
||||
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
|
||||
input_mask = [1] * len(input_ids)
|
||||
segment_ids = list(instance.segment_ids)
|
||||
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:
|
||||
tf.logging.info("*** Example ***")
|
||||
tf.logging.info("tokens: %s" % " ".join(
|
||||
[tokenization.printable_text(x) for x in instance.tokens]))
|
||||
|
||||
for feature_name in features.keys():
|
||||
feature = features[feature_name]
|
||||
values = []
|
||||
if feature.int64_list.value:
|
||||
values = feature.int64_list.value
|
||||
elif feature.float_list.value:
|
||||
values = feature.float_list.value
|
||||
tf.logging.info(
|
||||
"%s: %s" % (feature_name, " ".join([str(x) for x in values])))
|
||||
|
||||
for writer in writers:
|
||||
writer.close()
|
||||
|
||||
tf.logging.info("Wrote %d total instances", total_written)
|
||||
|
||||
|
||||
def create_int_feature(values):
|
||||
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
|
||||
return feature
|
||||
|
||||
|
||||
def create_float_feature(values):
|
||||
feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
|
||||
return feature
|
||||
|
||||
|
||||
def create_training_instances(input_files, tokenizer, max_seq_length,
|
||||
dupe_factor, short_seq_prob, masked_lm_prob,
|
||||
max_predictions_per_seq, rng):
|
||||
"""Create `TrainingInstance`s from raw text."""
|
||||
all_documents = [[]]
|
||||
|
||||
# Input file format:
|
||||
# (1) One sentence per line. These should ideally be actual sentences, not
|
||||
# entire paragraphs or arbitrary spans of text. (Because we use the
|
||||
# sentence boundaries for the "next sentence prediction" task).
|
||||
# (2) Blank lines between documents. Document boundaries are needed so
|
||||
# that the "next sentence prediction" task doesn't span between documents.
|
||||
for input_file in input_files:
|
||||
with tf.gfile.GFile(input_file, "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
|
||||
|
||||
|
||||
def create_masked_lm_predictions(tokens, masked_lm_prob,
|
||||
max_predictions_per_seq, vocab_words, rng):
|
||||
"""Creates the predictis 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)
|
||||
|
||||
masked_lm = collections.namedtuple("masked_lm", ["index", "label"]) # pylint: disable=invalid-name
|
||||
|
||||
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(masked_lm(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(_):
|
||||
tf.logging.set_verbosity(tf.logging.INFO)
|
||||
|
||||
tokenizer = tokenization.FullTokenizer(
|
||||
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
|
||||
|
||||
input_files = []
|
||||
for input_pattern in args.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(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_files = args.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, args.max_seq_length,
|
||||
args.max_predictions_per_seq, output_files)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
tf.app.run()
|
Loading…
Reference in New Issue