create_pretraining_data.py

# coding=utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
# 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 examples for BERT."""
import argparse
import collections
import os
import random
from io import open

import h5py
import numpy as np
from tqdm import tqdm

from paddlenlp.transformers import BertTokenizer
from paddlenlp.transformers.tokenizer_utils import convert_to_unicode


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 write_instance_to_example_file(instances, tokenizer, max_seq_length, max_predictions_per_seq, output_file):
    """Create 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

    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", encoding="UTF-8") as reader:
            while True:
                line = 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())
    vocab_words = list(tokenizer.vocab.token_to_idx.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

                    # If picked random document is the same as the current document
                    if random_document_index == document_index:
                        is_random_next = False

                    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, target_seq_length, 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()

    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 created hdf5 formatted data will be written.",
    )
    parser.add_argument(
        "--vocab_file",
        default=None,
        type=str,
        required=False,
        help="The vocabulary the BERT model will train on. "
        "Use bert_model argument would ignore this. "
        "The bert_model argument is recommended.",
    )
    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. "
        "Use bert_model argument would ignore this. The bert_model argument is recommended.",
    )
    parser.add_argument(
        "--bert_model",
        default="bert-base-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."
        "If provided, use the pre-trained model used tokenizer to create data "
        "and ignore vocab_file and do_lower_case.",
    )

    # Other parameters
    # 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 number of masked LM predictions per sequence."
    )

    # 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("--random_seed", type=int, default=12345, help="random seed for initialization")

    args = parser.parse_args()
    print(args)

    if args.bert_model:
        tokenizer = BertTokenizer.from_pretrained(args.bert_model)
    else:
        assert args.vocab_file, "vocab_file must be set If bert_model is not provided."
        tokenizer = BertTokenizer(args.vocab_file, 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()