bert_dataset.py

# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION.  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
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"""BERT Style dataset."""

import os
import time

import numpy as np
import torch
from torch.utils.data import Dataset

from megatron import get_tokenizer, get_args, print_rank_0
from megatron import mpu
from megatron.data.dataset_utils import get_a_and_b_segments
from megatron.data.dataset_utils import truncate_segments
from megatron.data.dataset_utils import create_tokens_and_tokentypes
from megatron.data.dataset_utils import pad_and_convert_to_numpy
from megatron.data.dataset_utils import create_masked_lm_predictions


class BertDataset(Dataset):

    def __init__(self, name, indexed_dataset, data_prefix,
                 num_epochs, max_num_samples, masked_lm_prob,
                 max_seq_length, short_seq_prob, seed):

        # Params to store.
        self.name = name
        self.seed = seed
        self.masked_lm_prob = masked_lm_prob
        self.max_seq_length = max_seq_length

        # Dataset.
        self.indexed_dataset = indexed_dataset

        # Build the samples mapping.
        self.samples_mapping = get_samples_mapping_(self.indexed_dataset,
                                                    data_prefix,
                                                    num_epochs,
                                                    max_num_samples,
                                                    self.max_seq_length,
                                                    short_seq_prob,
                                                    self.seed,
                                                    self.name)

        # Vocab stuff.
        tokenizer = get_tokenizer()
        self.vocab_id_list = list(tokenizer.inv_vocab.keys())
        self.vocab_id_to_token_dict = tokenizer.inv_vocab
        self.cls_id = tokenizer.cls
        self.sep_id = tokenizer.sep
        self.mask_id = tokenizer.mask
        self.pad_id = tokenizer.pad

    def __len__(self):
        return self.samples_mapping.shape[0]

    def __getitem__(self, idx):
        start_idx, end_idx, seq_length = self.samples_mapping[idx]
        sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
        # Note that this rng state should be numpy and not python since
        # python randint is inclusive whereas the numpy one is exclusive.
        np_rng = np.random.RandomState(seed=(self.seed + idx))
        return build_training_sample(sample, seq_length,
                                     self.max_seq_length,  # needed for padding
                                     self.vocab_id_list,
                                     self.vocab_id_to_token_dict,
                                     self.cls_id, self.sep_id,
                                     self.mask_id, self.pad_id,
                                     self.masked_lm_prob, np_rng)


def get_samples_mapping_(indexed_dataset,
                         data_prefix,
                         num_epochs,
                         max_num_samples,
                         max_seq_length,
                         short_seq_prob,
                         seed,
                         name):
    if not num_epochs:
        if not max_num_samples:
            raise ValueError("Need to specify either max_num_samples "
                             "or num_epochs")
        num_epochs = np.iinfo(np.int32).max - 1
    if not max_num_samples:
        max_num_samples = np.iinfo(np.int64).max - 1

    # Filename of the index mapping
    indexmap_filename = data_prefix
    indexmap_filename += '_{}_indexmap'.format(name)
    if num_epochs != (np.iinfo(np.int32).max - 1):
        indexmap_filename += '_{}ep'.format(num_epochs)
    if max_num_samples != (np.iinfo(np.int64).max - 1):
        indexmap_filename += '_{}mns'.format(max_num_samples)
    indexmap_filename += '_{}msl'.format(max_seq_length)
    indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob)
    indexmap_filename += '_{}s'.format(seed)
    indexmap_filename += '.npy'

    # Build the indexed mapping if not exist.
    if torch.distributed.get_rank() == 0 and \
       not os.path.isfile(indexmap_filename):
        print(' > WARNING: could not find index map file {}, building '
              'the indices on rank 0 ...'.format(indexmap_filename))

        # Make sure the types match the helpers input types.
        assert indexed_dataset.doc_idx.dtype == np.int64
        assert indexed_dataset.sizes.dtype == np.int32

        # Build samples mapping
        verbose = torch.distributed.get_rank() == 0
        start_time = time.time()
        print_rank_0(' > building sapmles index mapping for {} ...'.format(
            name))
        # First compile and then import.
        from megatron.data.dataset_utils import compile_helper
        compile_helper()
        from megatron.data import helpers
        samples_mapping = helpers.build_mapping(
            indexed_dataset.doc_idx,
            indexed_dataset.sizes,
            num_epochs,
            max_num_samples,
            max_seq_length - 3,  # account for added tokens
            short_seq_prob,
            seed,
            verbose)
        print_rank_0(' > done building sapmles index maping')
        np.save(indexmap_filename, samples_mapping, allow_pickle=True)
        print_rank_0(' > saved the index mapping in {}'.format(
            indexmap_filename))
        # Make sure all the ranks have built the mapping
        print_rank_0(' > elasped time to build and save samples mapping '
                     '(seconds): {:4f}'.format(
                         time.time() - start_time))
    # This should be a barrier but nccl barrier assumes
    # device_index=rank which is not the case for model
    # parallel case
    counts = torch.cuda.LongTensor([1])
    torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group())
    assert counts[0].item() == torch.distributed.get_world_size(
        group=mpu.get_data_parallel_group())

    # Load indexed dataset.
    print_rank_0(' > loading indexed mapping from {}'.format(
        indexmap_filename))
    start_time = time.time()
    samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r')
    print_rank_0('    loaded indexed file in {:3.3f} seconds'.format(
        time.time() - start_time))
    print_rank_0('    total number of samples: {}'.format(
        samples_mapping.shape[0]))

    return samples_mapping


def build_training_sample(sample,
                          target_seq_length, max_seq_length,
                          vocab_id_list, vocab_id_to_token_dict,
                          cls_id, sep_id, mask_id, pad_id,
                          masked_lm_prob, np_rng):
    """Biuld training sample.

    Arguments:
        sample: A list of sentences in which each sentence is a list token ids.
        target_seq_length: Desired sequence length.
        max_seq_length: Maximum length of the sequence. All values are padded to
            this length.
        vocab_id_list: List of vocabulary ids. Used to pick a random id.
        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
        cls_id: Start of example id.
        sep_id: Separator id.
        mask_id: Mask token id.
        pad_id: Padding token id.
        masked_lm_prob: Probability to mask tokens.
        np_rng: Random number genenrator. Note that this rng state should be
              numpy and not python since python randint is inclusive for
              the opper bound whereas the numpy one is exclusive.
    """

    # We assume that we have at least two sentences in the sample
    assert len(sample) > 1
    assert target_seq_length <= max_seq_length

    # Divide sample into two segments (A and B).
    tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)

    # Truncate to `target_sequence_length`.
    max_num_tokens = target_seq_length
    truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a),
                                  len(tokens_b), max_num_tokens, np_rng)

    # Build tokens and toketypes.
    tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b,
                                                      cls_id, sep_id)

    # Masking.
    max_predictions_per_seq = masked_lm_prob * max_num_tokens
    (tokens, masked_positions, masked_labels, _) = create_masked_lm_predictions(
        tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob,
        cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng)

    # Padding.
    tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \
        = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions,
                                   masked_labels, pad_id, max_seq_length)

    train_sample = {
        'text': tokens_np,
        'types': tokentypes_np,
        'labels': labels_np,
        'is_random': int(is_next_random),
        'loss_mask': loss_mask_np,
        'padding_mask': padding_mask_np,
        'truncated': int(truncated)}
    return train_sample