realm_dataset_utils.py

import os
import time

import numpy as np
import torch

from megatron import print_rank_0, mpu


def join_str_list(str_list):
    """Join a list of strings, handling spaces appropriately"""
    result = ""
    for s in str_list:
        if s.startswith("##"):
            result += s[2:]
        else:
            result += " " + s
    return result


def get_block_samples_mapping(block_dataset, title_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))
        from megatron.data.dataset_utils import compile_helper
        compile_helper()
        from megatron.data import helpers
        samples_mapping = helpers.build_blocks_mapping(
            block_dataset.doc_idx,
            block_dataset.sizes,
            title_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