ict_dataset.py

import itertools
import random
import os
import time

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

from megatron import get_tokenizer
from megatron import print_rank_0
from megatron import mpu
from megatron.data import helpers


class InverseClozeDataset(Dataset):
    """Dataset containing sentences and their blocks for an inverse cloze task."""
    def __init__(self, name, block_dataset, title_dataset, data_prefix,
                 num_epochs, max_num_samples, max_seq_length,
                 short_seq_prob, seed):
        self.name = name
        self.seed = seed
        self.max_seq_length = max_seq_length
        self.block_dataset = block_dataset
        self.title_dataset = title_dataset
        self.short_seq_prob = short_seq_prob
        self.rng = random.Random(self.seed)

        self.samples_mapping = get_samples_mapping(self.block_dataset,
                                                   self.title_dataset,
                                                   data_prefix,
                                                   num_epochs,
                                                   max_num_samples,
                                                   self.max_seq_length,
                                                   self.seed,
                                                   self.name)
        tokenizer = get_tokenizer()
        self.vocab_id_list = list(tokenizer.inv_vocab.keys())
        self.vocab_id_to_token_list = 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, doc_idx, block_idx = self.samples_mapping[idx]
        title = list(self.title_dataset[int(doc_idx)])
        block = [list(self.block_dataset[i]) for i in range(start_idx, end_idx)]
        assert len(block) > 1

        # avoid selecting the first or last sentence to be the query.
        if len(block) == 2:
            rand_sent_idx = int(self.rng.random() > 0.5)
        else:
            rand_sent_idx = self.rng.randint(1, len(block) - 2)

        # keep the query in the block 10% of the time.
        if self.rng.random() < 0.1:
            query = block[rand_sent_idx].copy()
        else:
            query = block.pop(rand_sent_idx)

        # still need to truncate because blocks are concluded when
        # the sentence lengths have exceeded max_seq_length.
        query = query[:self.max_seq_length - 2]
        block = list(itertools.chain(*block))[:self.max_seq_length - (3 + len(title))]

        query_tokens, query_token_types, query_pad_mask = self.concat_and_pad_tokens(query)
        block_tokens, block_token_types, block_pad_mask = self.concat_and_pad_tokens(block, title)

        sample = {
            'query_tokens': np.array(query_tokens),
            'query_types': np.array(query_token_types),
            'query_pad_mask': np.array(query_pad_mask),
            'block_tokens': np.array(block_tokens),
            'block_types': np.array(block_token_types),
            'block_pad_mask': np.array(block_pad_mask)
        }

        return sample

    def concat_and_pad_tokens(self, tokens, title=None):
        """concat with special tokens and pad sequence to self.max_seq_length"""
        tokens = [self.cls_id] + tokens + [self.sep_id]
        if title is not None:
            tokens += title + [self.sep_id]
        assert len(tokens) <= self.max_seq_length, len(tokens)

        num_pad = self.max_seq_length - len(tokens)
        pad_mask = [0] * len(tokens) + [1] * num_pad
        tokens += [self.pad_id] * num_pad
        token_types = [0] * self.max_seq_length
        return tokens, token_types, pad_mask


def get_samples_mapping(block_dataset,
                        titles_dataset,
                        data_prefix,
                        num_epochs,
                        max_num_samples,
                        max_seq_length,
                        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 += '_{}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 block_dataset.doc_idx.dtype == np.int64
        assert block_dataset.sizes.dtype == np.int32

        # Build samples mapping
        verbose = torch.distributed.get_rank() == 0
        start_time = time.time()
        print_rank_0(' > building samples index mapping for {} ...'.format(
            name))
        samples_mapping = helpers.build_blocks_mapping(
            block_dataset.doc_idx,
            block_dataset.sizes,
            titles_dataset.sizes,
            num_epochs,
            max_num_samples,
            max_seq_length-3,  # account for added tokens
            seed,
            verbose)
        print_rank_0(' > done building samples index mapping')
        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(' > elapsed 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