official/nlp/data/create_pretraining_data.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Create masked LM/next sentence masked_lm TF examples for BERT."""
import collections
import itertools
import random
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf, tf_keras
from official.nlp.tools import tokenization
FLAGS = flags.FLAGS
flags.DEFINE_string("input_file", None,
"Input raw text file (or comma-separated list of files).")
flags.DEFINE_string(
"output_file", None,
"Output TF example file (or comma-separated list of files).")
flags.DEFINE_enum(
"tokenization",
"WordPiece",
["WordPiece", "SentencePiece"],
"Specifies the tokenizer implementation, i.e., whether to use WordPiece "
"or SentencePiece tokenizer. Canonical BERT uses WordPiece tokenizer, "
"while ALBERT uses SentencePiece tokenizer.",
)
flags.DEFINE_string(
"vocab_file",
None,
"For WordPiece tokenization, the vocabulary file of the tokenizer.",
)
flags.DEFINE_string(
"sp_model_file",
"",
"For SentencePiece tokenization, the path to the model of the tokenizer.",
)
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_bool(
"do_whole_word_mask",
False,
"Whether to use whole word masking rather than per-token masking.",
)
flags.DEFINE_integer(
"max_ngram_size", None,
"Mask contiguous whole words (n-grams) of up to `max_ngram_size` using a "
"weighting scheme to favor shorter n-grams. "
"Note: `--do_whole_word_mask=True` must also be set when n-gram masking.")
flags.DEFINE_bool(
"gzip_compress", False,
"Whether to use `GZIP` compress option to get compressed TFRecord files.")
flags.DEFINE_bool(
"use_v2_feature_names", False,
"Whether to use the feature names consistent with the models.")
flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")
flags.DEFINE_integer("max_predictions_per_seq", 20,
"Maximum number of masked LM predictions per sequence.")
flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
flags.DEFINE_integer(
"dupe_factor", 10,
"Number of times to duplicate the input data (with different masks).")
flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
flags.DEFINE_float(
"short_seq_prob", 0.1,
"Probability of creating sequences which are shorter than the "
"maximum length.")
class TrainingInstance(object):
"""A single training instance (sentence pair)."""
def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
is_random_next):
self.tokens = tokens
self.segment_ids = segment_ids
self.is_random_next = is_random_next
self.masked_lm_positions = masked_lm_positions
self.masked_lm_labels = masked_lm_labels
def __str__(self):
s = ""
s += "tokens: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens]))
s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
s += "is_random_next: %s\n" % self.is_random_next
s += "masked_lm_positions: %s\n" % (" ".join(
[str(x) for x in self.masked_lm_positions]))
s += "masked_lm_labels: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.masked_lm_labels]))
s += "\n"
return s
def __repr__(self):
return self.__str__()
def write_instance_to_example_files(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files,
gzip_compress, use_v2_feature_names):
"""Creates TF example files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(
tf.io.TFRecordWriter(
output_file, options="GZIP" if gzip_compress else ""))
writer_index = 0
total_written = 0
for (inst_index, instance) in enumerate(instances):
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids = list(instance.segment_ids)
assert len(input_ids) <= max_seq_length
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
masked_lm_weights = [1.0] * len(masked_lm_ids)
while len(masked_lm_positions) < max_predictions_per_seq:
masked_lm_positions.append(0)
masked_lm_ids.append(0)
masked_lm_weights.append(0.0)
next_sentence_label = 1 if instance.is_random_next else 0
features = collections.OrderedDict()
if use_v2_feature_names:
features["input_word_ids"] = create_int_feature(input_ids)
features["input_type_ids"] = create_int_feature(segment_ids)
else:
features["input_ids"] = create_int_feature(input_ids)
features["segment_ids"] = create_int_feature(segment_ids)
features["input_mask"] = create_int_feature(input_mask)
features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
features["next_sentence_labels"] = create_int_feature([next_sentence_label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writers[writer_index].write(tf_example.SerializeToString())
writer_index = (writer_index + 1) % len(writers)
total_written += 1
if inst_index < 20:
logging.info("*** Example ***")
logging.info("tokens: %s", " ".join(
[tokenization.printable_text(x) for x in instance.tokens]))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
logging.info("%s: %s", feature_name, " ".join([str(x) for x in values]))
for writer in writers:
writer.close()
logging.info("Wrote %d total instances", total_written)
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return feature
def create_float_feature(values):
feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return feature
def create_training_instances(
input_files,
tokenizer,
processor_text_fn,
max_seq_length,
dupe_factor,
short_seq_prob,
masked_lm_prob,
max_predictions_per_seq,
rng,
do_whole_word_mask=False,
max_ngram_size=None,
):
"""Create `TrainingInstance`s from raw text."""
all_documents = [[]]
# Input file format:
# (1) One sentence per line. These should ideally be actual sentences, not
# entire paragraphs or arbitrary spans of text. (Because we use the
# sentence boundaries for the "next sentence prediction" task).
# (2) Blank lines between documents. Document boundaries are needed so
# that the "next sentence prediction" task doesn't span between documents.
for input_file in input_files:
with tf.io.gfile.GFile(input_file, "rb") as reader:
for line in reader:
line = processor_text_fn(line)
# 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,
do_whole_word_mask, max_ngram_size))
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,
do_whole_word_mask=False,
max_ngram_size=None):
"""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,
do_whole_word_mask, max_ngram_size)
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"])
# A _Gram is a [half-open) interval of token indices which form a word.
# E.g.,
# words: ["The", "doghouse"]
# tokens: ["The", "dog", "##house"]
# grams: [(0,1), (1,3)]
_Gram = collections.namedtuple("_Gram", ["begin", "end"])
def _window(iterable, size):
"""Helper to create a sliding window iterator with a given size.
E.g.,
input = [1, 2, 3, 4]
_window(input, 1) => [1], [2], [3], [4]
_window(input, 2) => [1, 2], [2, 3], [3, 4]
_window(input, 3) => [1, 2, 3], [2, 3, 4]
_window(input, 4) => [1, 2, 3, 4]
_window(input, 5) => None
Args:
iterable: elements to iterate over.
size: size of the window.
Yields:
Elements of `iterable` batched into a sliding window of length `size`.
"""
i = iter(iterable)
window = []
try:
for e in range(0, size):
window.append(next(i))
yield window
except StopIteration:
# handle the case where iterable's length is less than the window size.
return
for e in i:
window = window[1:] + [e]
yield window
def _contiguous(sorted_grams):
"""Test whether a sequence of grams is contiguous.
Args:
sorted_grams: _Grams which are sorted in increasing order.
Returns:
True if `sorted_grams` are touching each other.
E.g.,
_contiguous([(1, 4), (4, 5), (5, 10)]) == True
_contiguous([(1, 2), (4, 5)]) == False
"""
for a, b in _window(sorted_grams, 2):
if a.end != b.begin:
return False
return True
def _masking_ngrams(grams, max_ngram_size, max_masked_tokens, rng):
"""Create a list of masking {1, ..., n}-grams from a list of one-grams.
This is an extension of 'whole word masking' to mask multiple, contiguous
words such as (e.g., "the red boat").
Each input gram represents the token indices of a single word,
words: ["the", "red", "boat"]
tokens: ["the", "red", "boa", "##t"]
grams: [(0,1), (1,2), (2,4)]
For a `max_ngram_size` of three, possible outputs masks include:
1-grams: (0,1), (1,2), (2,4)
2-grams: (0,2), (1,4)
3-grams; (0,4)
Output masks will not overlap and contain less than `max_masked_tokens` total
tokens. E.g., for the example above with `max_masked_tokens` as three,
valid outputs are,
[(0,1), (1,2)] # "the", "red" covering two tokens
[(1,2), (2,4)] # "red", "boa", "##t" covering three tokens
The length of the selected n-gram follows a zipf weighting to
favor shorter n-gram sizes (weight(1)=1, weight(2)=1/2, weight(3)=1/3, ...).
Args:
grams: List of one-grams.
max_ngram_size: Maximum number of contiguous one-grams combined to create
an n-gram.
max_masked_tokens: Maximum total number of tokens to be masked.
rng: `random.Random` generator.
Returns:
A list of n-grams to be used as masks.
"""
if not grams:
return None
grams = sorted(grams)
num_tokens = grams[-1].end
# Ensure our grams are valid (i.e., they don't overlap).
for a, b in _window(grams, 2):
if a.end > b.begin:
raise ValueError("overlapping grams: {}".format(grams))
# Build map from n-gram length to list of n-grams.
ngrams = {i: [] for i in range(1, max_ngram_size+1)}
for gram_size in range(1, max_ngram_size+1):
for g in _window(grams, gram_size):
if _contiguous(g):
# Add an n-gram which spans these one-grams.
ngrams[gram_size].append(_Gram(g[0].begin, g[-1].end))
# Shuffle each list of n-grams.
for v in ngrams.values():
rng.shuffle(v)
# Create the weighting for n-gram length selection.
# Stored cumulatively for `random.choices` below.
cummulative_weights = list(
itertools.accumulate([1./n for n in range(1, max_ngram_size+1)]))
output_ngrams = []
# Keep a bitmask of which tokens have been masked.
masked_tokens = [False] * num_tokens
# Loop until we have enough masked tokens or there are no more candidate
# n-grams of any length.
# Each code path should ensure one or more elements from `ngrams` are removed
# to guarantee this loop terminates.
while (sum(masked_tokens) < max_masked_tokens and
sum(len(s) for s in ngrams.values())):
# Pick an n-gram size based on our weights.
sz = random.choices(range(1, max_ngram_size+1),
cum_weights=cummulative_weights)[0]
# Ensure this size doesn't result in too many masked tokens.
# E.g., a two-gram contains _at least_ two tokens.
if sum(masked_tokens) + sz > max_masked_tokens:
# All n-grams of this length are too long and can be removed from
# consideration.
ngrams[sz].clear()
continue
# All of the n-grams of this size have been used.
if not ngrams[sz]:
continue
# Choose a random n-gram of the given size.
gram = ngrams[sz].pop()
num_gram_tokens = gram.end-gram.begin
# Check if this would add too many tokens.
if num_gram_tokens + sum(masked_tokens) > max_masked_tokens:
continue
# Check if any of the tokens in this gram have already been masked.
if sum(masked_tokens[gram.begin:gram.end]):
continue
# Found a usable n-gram! Mark its tokens as masked and add it to return.
masked_tokens[gram.begin:gram.end] = [True] * (gram.end-gram.begin)
output_ngrams.append(gram)
return output_ngrams
def _tokens_to_grams(tokens):
"""Reconstitue grams (words) from `tokens`.
E.g.,
tokens: ['[CLS]', 'That', 'lit', '##tle', 'blue', 'tru', '##ck', '[SEP]']
grams: [ [1,2), [2, 4), [4,5) , [5, 6)]
Args:
tokens: list of tokens (word pieces or sentence pieces).
Returns:
List of _Grams representing spans of whole words
(without "[CLS]" and "[SEP]").
"""
grams = []
gram_start_pos = None
for i, token in enumerate(tokens):
if gram_start_pos is not None and token.startswith("##"):
continue
if gram_start_pos is not None:
grams.append(_Gram(gram_start_pos, i))
if token not in ["[CLS]", "[SEP]"]:
gram_start_pos = i
else:
gram_start_pos = None
if gram_start_pos is not None:
grams.append(_Gram(gram_start_pos, len(tokens)))
return grams
def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng,
do_whole_word_mask,
max_ngram_size=None):
"""Creates the predictions for the masked LM objective."""
if do_whole_word_mask:
grams = _tokens_to_grams(tokens)
else:
# Here we consider each token to be a word to allow for sub-word masking.
if max_ngram_size:
raise ValueError("cannot use ngram masking without whole word masking")
grams = [_Gram(i, i+1) for i in range(0, len(tokens))
if tokens[i] not in ["[CLS]", "[SEP]"]]
num_to_predict = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
# Generate masks. If `max_ngram_size` in [0, None] it means we're doing
# whole word masking or token level masking. Both of these can be treated
# as the `max_ngram_size=1` case.
masked_grams = _masking_ngrams(grams, max_ngram_size or 1,
num_to_predict, rng)
masked_lms = []
output_tokens = list(tokens)
for gram in masked_grams:
# 80% of the time, replace all n-gram tokens with [MASK]
if rng.random() < 0.8:
replacement_action = lambda idx: "[MASK]"
else:
# 10% of the time, keep all the original n-gram tokens.
if rng.random() < 0.5:
replacement_action = lambda idx: tokens[idx]
# 10% of the time, replace each n-gram token with a random word.
else:
replacement_action = lambda idx: rng.choice(vocab_words)
for idx in range(gram.begin, gram.end):
output_tokens[idx] = replacement_action(idx)
masked_lms.append(MaskedLmInstance(index=idx, label=tokens[idx]))
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 get_processor_text_fn(is_sentence_piece, do_lower_case):
def processor_text_fn(text):
text = tokenization.convert_to_unicode(text)
if is_sentence_piece:
# Additional preprocessing specific to the SentencePiece tokenizer.
text = tokenization.preprocess_text(text, lower=do_lower_case)
return text.strip()
return processor_text_fn
def main(_):
if FLAGS.tokenization == "WordPiece":
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case
)
processor_text_fn = get_processor_text_fn(False, FLAGS.do_lower_case)
else:
assert FLAGS.tokenization == "SentencePiece"
tokenizer = tokenization.FullSentencePieceTokenizer(FLAGS.sp_model_file)
processor_text_fn = get_processor_text_fn(True, FLAGS.do_lower_case)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
logging.info("*** Reading from input files ***")
for input_file in input_files:
logging.info(" %s", input_file)
rng = random.Random(FLAGS.random_seed)
instances = create_training_instances(
input_files,
tokenizer,
processor_text_fn,
FLAGS.max_seq_length,
FLAGS.dupe_factor,
FLAGS.short_seq_prob,
FLAGS.masked_lm_prob,
FLAGS.max_predictions_per_seq,
rng,
FLAGS.do_whole_word_mask,
FLAGS.max_ngram_size,
)
output_files = FLAGS.output_file.split(",")
logging.info("*** Writing to output files ***")
for output_file in output_files:
logging.info(" %s", output_file)
write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
FLAGS.max_predictions_per_seq, output_files,
FLAGS.gzip_compress,
FLAGS.use_v2_feature_names)
if __name__ == "__main__":
flags.mark_flag_as_required("input_file")
flags.mark_flag_as_required("output_file")
app.run(main)