bert_dataset.py

# coding=utf-8
# Copyright (c) 2019, 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
# limitations under the License.

"""BERT Style dataset."""

import os
import time

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

from megatron import get_tokenizer
from megatron import mpu
from megatron.data.dataset_utils import build_training_sample
from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
from megatron import print_rank_0


def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
                                    train_valid_test_num_samples,
                                    max_seq_length, masked_lm_prob,
                                    short_seq_prob, seed, skip_warmup):

    # Indexed dataset.
    indexed_dataset = get_indexed_dataset_(data_prefix,
                                           data_impl,
                                           skip_warmup)

    # Get start and end indices of train/valid/train into doc-idx
    # Note that doc-idx is desinged to be num-docs + 1 so we can
    # easily iterate over it.
    total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1
    splits = get_train_valid_test_split_(splits_string, total_num_of_documents)

    # Print stats about the splits.
    print_rank_0(' > dataset split:')

    def print_split_stats(name, index):
        print_rank_0('    {}:'.format(name))
        print_rank_0('     document indices in [{}, {}) total of {} '
                     'documents'.format(splits[index], splits[index + 1],
                                        splits[index + 1] - splits[index]))
        start_index = indexed_dataset.doc_idx[splits[index]]
        end_index = indexed_dataset.doc_idx[splits[index + 1]]
        print_rank_0('     sentence indices in [{}, {}) total of {} '
                     'sentences'.format(start_index, end_index,
                                        end_index - start_index))
    print_split_stats('train', 0)
    print_split_stats('validation', 1)
    print_split_stats('test', 2)

    def build_dataset(index, name):
        dataset = None
        if splits[index + 1] > splits[index]:
            # Get the pointer to the original doc-idx so we can set it later.
            doc_idx_ptr = indexed_dataset.get_doc_idx()
            # Slice the doc-idx
            start_index = splits[index]
            # Add +1 so we can index into the dataset to get the upper bound.
            end_index = splits[index + 1] + 1
            # New doc_idx view.
            indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index])
            # Build the dataset accordingly.
            dataset = BertDataset(
                name=name,
                indexed_dataset=indexed_dataset,
                data_prefix=data_prefix,
                num_epochs=None,
                max_num_samples=train_valid_test_num_samples[index],
                masked_lm_prob=masked_lm_prob,
                max_seq_length=max_seq_length,
                short_seq_prob=short_seq_prob,
                seed=seed)
            # Set the original pointer so dataset remains the main dataset.
            indexed_dataset.set_doc_idx(doc_idx_ptr)
            # Checks.
            assert indexed_dataset.doc_idx[0] == 0
            assert indexed_dataset.doc_idx.shape[0] == \
                (total_num_of_documents + 1)
        return dataset

    train_dataset = build_dataset(0, 'train')
    valid_dataset = build_dataset(1, 'valid')
    test_dataset = build_dataset(2, 'test')

    return (train_dataset, valid_dataset, test_dataset)


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_index, end_index, seq_length = self.samples_mapping[idx]
        sample = []
        for index in range(start_index, end_index):
            sample.append(self.indexed_dataset[index])
        # 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_indexed_dataset_(data_prefix, data_impl, skip_warmup):

    print_rank_0(' > building dataset index ...')

    start_time = time.time()
    indexed_dataset = make_indexed_dataset(data_prefix,
                                           data_impl,
                                           skip_warmup)
    assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1]
    print_rank_0(' > finished creating indexed dataset in {:4f} '
                 'seconds'.format(time.time() - start_time))

    print_rank_0(' > indexed dataset stats:')
    print_rank_0('    number of documents: {}'.format(
        indexed_dataset.doc_idx.shape[0] - 1))
    print_rank_0('    number of sentences: {}'.format(
        indexed_dataset.sizes.shape[0]))

    return indexed_dataset


def get_train_valid_test_split_(splits_string, size):
    """ Get dataset splits from comma or '/' separated string list."""

    splits = []
    if splits_string.find(',') != -1:
        splits = [float(s) for s in splits_string.split(',')]
    elif splits_string.find('/') != -1:
        splits = [float(s) for s in splits_string.split('/')]
    else:
        splits = [float(splits_string)]
    while len(splits) < 3:
        splits.append(0.)
    splits = splits[:3]
    splits_sum = sum(splits)
    assert splits_sum > 0.0
    splits = [split / splits_sum for split in splits]
    splits_index = [0]
    for index, split in enumerate(splits):
        splits_index.append(splits_index[index] +
                            int(round(split * float(size))))
    diff = splits_index[-1] - size
    for index in range(1, len(splits_index)):
        splits_index[index] -= diff
    assert len(splits_index) == 4
    assert splits_index[-1] == size
    return splits_index


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)
    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