worker.py

"""
    Copyright 2019 Tae-Hwan Jung
    MIT LICENSE
    code reference : https://soooprmx.com/archives/6436,
        https://github.com/google-research/bert/blob/master/create_pretraining_data.py

    ventilator for masked language model preprocessing pipeline
    This code must be executed after wikiextractor script has been **finished**.
    `data` : dump data root folder path, it has to seems like below:
    `vserver` : ventilator server
    `vport` : number of ventilator port
    `sserver` : sink server
    `sport` : number of sink port

    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 io, os
import zmq
import collections
import random
import tokenization
import tensorflow as tf
from bs4 import BeautifulSoup

import boto3
s3 = boto3.resource('s3')

ctx = zmq.Context()

flags = tf.flags

FLAGS = flags.FLAGS

flags.DEFINE_string('vserver', '127.0.0.1', 'ventilator server ip address')
flags.DEFINE_integer('vport', 5557, 'ventilator port')

# flags.DEFINE_string('sserver', '127.0.0.1', 'sink server ip address')
# flags.DEFINE_integer('sport', 5556, 'sink port')

flags.DEFINE_string('vocab_file', './vocab.txt', 'The vocabulary file that '
                                        'the BERT model was trained on.')

flags.DEFINE_string(
    "output_folder", 'output',
    "folder where Output TF example file to be saved")

flags.DEFINE_string("bucket_name", None, "bucket name")
flags.DEFINE_string("bucket_key", 'output', "bucket key")

flags.DEFINE_bool('wiki_data', True, 'Is wiki data?')
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-WordPiece masking.')

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_file,
    ):
    """Create TF example files from `TrainingInstance`s."""

    writer = tf.python_io.TFRecordWriter(output_file)

    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))

        writer.write(tf_example.SerializeToString())

        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])))

    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 html_parser(str):
    bs = BeautifulSoup(str, 'html.parser')
    block = ''
    for doc in bs.find('doc'):
        block += (doc + '\n')
    return block

def create_training_instances(
        input_text,
        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.
    if FLAGS.wiki_data:
        input_text = html_parser(input_text).strip()

    reader = io.StringIO(input_text)
    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
                else:

                    # Actual next

                    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

        # Whole Word Masking means that if we mask all of the wordpieces
        # corresponding to an original word. When a word has been split into
        # WordPieces, the first token does not have any marker and any subsequence
        # tokens are prefixed with ##. So whenever we see the ## token, we
        # append it to the previous set of word indexes.
        #
        # Note that Whole Word Masking does *not* change the training code
        # at all -- we still predict each WordPiece independently, softmaxed
        # over the entire vocabulary.

        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]
                else:
                    # 10% of the time, replace with random word
                    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(_):
    server_pull = FLAGS.vserver
    port_pull = FLAGS.vport

    sock_pull = ctx.socket(zmq.PULL)
    sock_pull.connect("tcp://%s:%d" % (server_pull, port_pull))

    # sock_push = ctx.socket(zmq.PUSH)
    # sock_push.connect("tcp://%s:%d" % (server_push, port_push))

    tf.logging.set_verbosity(tf.logging.INFO)

    tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
                                           do_lower_case=FLAGS.do_lower_case)

    while True:
        data = sock_pull.recv_json()
        key = data['key']
        raw_data = data['text']

        rng = random.Random(FLAGS.random_seed)
        instances = create_training_instances(
            raw_data,
            tokenizer,
            FLAGS.max_seq_length,
            FLAGS.dupe_factor,
            FLAGS.short_seq_prob,
            FLAGS.masked_lm_prob,
            FLAGS.max_predictions_per_seq,
            rng,
        )

        output_file = os.path.join(FLAGS.output_folder, key+'.tfrecord')
        tf.logging.info('*** Writing to data files ***')
        tf.logging.info('  %s', output_file)

        write_instance_to_example_files(instances, tokenizer,
                                        FLAGS.max_seq_length,
                                        FLAGS.max_predictions_per_seq,
                                        output_file)

        # upload tfrecord to s3
        file = open(output_file, 'rb')
        s3.Bucket(FLAGS.bucket_name).put_object(
            Key=FLAGS.bucket_key+'/'+key+'.tfrecord',
            Body=file, ACL='public-read'
        )

        # sock_push.send_string('DONE') # send to sink

if __name__ == '__main__':
    flags.mark_flag_as_required('output_folder')
    flags.mark_flag_as_required('vocab_file')
    flags.mark_flag_as_required('wiki_data')
    flags.mark_flag_as_required('bucket_name')
    flags.mark_flag_as_required('bucket_key')

    flags.mark_flag_as_required('vserver')
    flags.mark_flag_as_required('vport')
    # flags.mark_flag_as_required('sserver')
    # flags.mark_flag_as_required('sport')

    if not os.path.isdir(FLAGS.output_folder):
        os.mkdir(FLAGS.output_folder)
    tf.app.run()