mirror of https://github.com/hpcaitech/ColossalAI
171 lines
6.4 KiB
Python
171 lines
6.4 KiB
Python
import json
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import logging
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import os
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import random
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import time
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from concurrent.futures import ProcessPoolExecutor
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import h5py
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import numpy as np
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import torch
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import torch.distributed as dist
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from bert_dataset_provider import BertDatasetProviderInterface
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from torch.utils.data import DataLoader, Dataset
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from torch.utils.data.distributed import DistributedSampler
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from torch.utils.data.sampler import RandomSampler
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import colossalai.utils as utils
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# Workaround because python functions are not picklable
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class WorkerInitObj(object):
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def __init__(self, seed):
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self.seed = seed
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def __call__(self, id):
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np.random.seed(seed=self.seed + id)
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random.seed(self.seed + id)
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def create_pretraining_dataset(input_file, max_predictions_per_seq, num_workers, train_batch_size, worker_init,
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data_sampler):
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train_data = pretraining_dataset(input_file=input_file, max_predictions_per_seq=max_predictions_per_seq)
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train_dataloader = DataLoader(train_data,
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sampler=data_sampler(train_data),
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batch_size=train_batch_size,
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num_workers=num_workers,
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worker_init_fn=worker_init,
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pin_memory=True)
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return train_dataloader, len(train_data)
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class pretraining_dataset(Dataset):
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def __init__(self, input_file, max_predictions_per_seq):
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self.input_file = input_file
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self.max_predictions_per_seq = max_predictions_per_seq
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f = h5py.File(input_file, "r")
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keys = ['input_ids', 'input_mask', 'segment_ids', 'masked_lm_positions']
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self.inputs = [np.asarray(f[key][:]) for key in keys]
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f.close()
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def __len__(self):
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'Denotes the total number of samples'
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return len(self.inputs[0])
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def __getitem__(self, index):
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[input_ids, input_mask, segment_ids, masked_lm_labels] = [
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torch.from_numpy(input[index].astype(np.int64)) if indice < 5 else torch.from_numpy(
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np.asarray(input[index].astype(np.int64))) for indice, input in enumerate(self.inputs)
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]
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return [input_ids, input_mask, segment_ids, masked_lm_labels]
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class NvidiaBertDatasetProvider(BertDatasetProviderInterface):
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def __init__(self, args, evaluate=False):
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self.num_workers = args.num_workers
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self.max_seq_length = args.max_seq_length
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self.max_predictions_per_seq = args.max_predictions_per_seq
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self.gradient_accumulation_steps = args.gradient_accumulation_steps
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if not evaluate:
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self.train_micro_batch_size_per_gpu = args.train_micro_batch_size_per_gpu
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else:
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self.train_micro_batch_size_per_gpu = args.eval_micro_batch_size_per_gpu
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self.logger = args.logger
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self.global_rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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# Initialize dataset files
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if not evaluate:
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self.dataset_files = [
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os.path.join(args.data_path_prefix, f)
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for f in os.listdir(args.data_path_prefix)
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if os.path.isfile(os.path.join(args.data_path_prefix, f)) and 'h5' in f
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]
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else:
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self.dataset_files = [
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os.path.join(args.eval_data_path_prefix, f)
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for f in os.listdir(args.eval_data_path_prefix)
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if os.path.isfile(os.path.join(args.eval_data_path_prefix, f)) and 'h5' in f
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]
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self.dataset_files.sort()
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# random.shuffle(self.dataset_files)
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self.num_files = len(self.dataset_files)
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# self.data_sampler = RandomSampler
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self.data_sampler = DistributedSampler
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self.worker_init = WorkerInitObj(args.seed + args.local_rank)
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self.dataset_future = None
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self.pool = ProcessPoolExecutor(1)
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self.data_file = None
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self.shuffle = True
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if self.global_rank == 0:
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self.logger.info(f"NvidiaBertDatasetProvider - Initialization: num_files = {self.num_files}")
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def get_shard(self, index):
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start = time.time()
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if self.dataset_future is None:
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self.data_file = self._get_shard_file(index)
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self.train_dataloader, sample_count = create_pretraining_dataset(
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input_file=self.data_file,
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max_predictions_per_seq=self.max_predictions_per_seq,
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num_workers=self.num_workers,
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train_batch_size=self.train_micro_batch_size_per_gpu,
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worker_init=self.worker_init,
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data_sampler=self.data_sampler)
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else:
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self.train_dataloader, sample_count = self.dataset_future.result(timeout=None)
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self.logger.info(
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f"Data Loading Completed for Pretraining Data from {self.data_file} with {sample_count} samples took {time.time()-start:.2f}s."
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)
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return self.train_dataloader, sample_count
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def release_shard(self):
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del self.train_dataloader
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self.pool.shutdown()
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def prefetch_shard(self, index):
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self.data_file = self._get_shard_file(index)
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self.dataset_future = self.pool.submit(create_pretraining_dataset, self.data_file, self.max_predictions_per_seq,
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self.num_workers, self.train_micro_batch_size_per_gpu, self.worker_init,
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self.data_sampler)
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def get_batch(self, batch_iter):
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return batch_iter
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def prefetch_batch(self):
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pass
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def _get_shard_file(self, shard_index):
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file_index = self._get_shard_file_index(shard_index, self.global_rank)
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return self.dataset_files[file_index]
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def _get_shard_file_index(self, shard_index, global_rank):
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# if dist.is_initialized() and self.world_size > self.num_files:
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# remainder = self.world_size % self.num_files
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# file_index = (shard_index * self.world_size) + global_rank + (
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# remainder * shard_index)
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# else:
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# file_index = shard_index * self.world_size + global_rank
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return shard_index % self.num_files
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def shuffle_dataset(self, epoch):
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if self.shuffle:
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# deterministically shuffle based on epoch and seed
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g = torch.Generator()
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g.manual_seed(self.epoch)
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indices = torch.randperm(self.num_files, generator=g).tolist()
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new_dataset = [self.dataset_files[i] for i in indices]
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self.dataset_files = new_dataset
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