ColossalAI/examples/tutorial/sequence_parallel/data/datasets/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 colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger

from ..tokenizer import get_tokenizer
from .dataset_utils import (
    create_masked_lm_predictions,
    create_tokens_and_tokentypes,
    get_a_and_b_segments,
    pad_and_convert_to_numpy,
    truncate_segments,
)

try:
    from . import helpers
except:
    print("helper is not built, ignore this message if you are using synthetic data.")


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, binary_head):

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

        # 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 - 3,    # account for added tokens,
            short_seq_prob,
            self.seed,
            self.name,
            self.binary_head)

        # 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.
        # We % 2**32 since numpy requires the seed to be between 0 and 2**32 - 1
        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
        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,
            self.binary_head)


def get_samples_mapping_(indexed_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, short_seq_prob,
                         seed, name, binary_head):
    logger = get_dist_logger()
    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()
        logger.info('\n > building samples index mapping for {} ...'.format(name), ranks=[0])
        # First compile and then import.
        samples_mapping = helpers.build_mapping(indexed_dataset.doc_idx, indexed_dataset.sizes, num_epochs,
                                                max_num_samples, max_seq_length, short_seq_prob, seed, verbose,
                                                2 if binary_head else 1)
        logger.info('\n > done building samples index maping', ranks=[0])
        np.save(indexmap_filename, samples_mapping, allow_pickle=True)
        logger.info('\n > saved the index mapping in {}'.format(indexmap_filename), ranks=[0])
        # Make sure all the ranks have built the mapping
        logger.info('\n > elapsed time to build and save samples mapping '
                    '(seconds): {:4f}'.format(time.time() - start_time),
                    ranks=[0])
    # 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=gpc.get_group(ParallelMode.DATA))
    if gpc.is_initialized(ParallelMode.PIPELINE):
        torch.distributed.all_reduce(counts, group=gpc.get_group(ParallelMode.PIPELINE))
    assert counts[0].item() == (torch.distributed.get_world_size() //
                                torch.distributed.get_world_size(group=gpc.get_group(ParallelMode.SEQUENCE)))

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

    if binary_head:
        # 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).
    if binary_head:
        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
    else:
        tokens_a = []
        for j in range(len(sample)):
            tokens_a.extend(sample[j])
        tokens_b = []
        is_next_random = False

    # 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