paddlepaddle--paddlenlp/slm/model_zoo/bert/static/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."""
from __future__ import absolute_import, division, print_function, unicode_literals

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, 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()

    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
    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()