分布式训练时数据集的分配

pytorch , 评论

@TOC

1. DistributedSampler

torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=None, rank=None, shuffle=True, seed=0, drop_last=False)

  • dataset: Dataset used for sampling
  • num_relicas: 参与分配的进程数，默认自己检索
  • rank: 进程编号，默认自己检索
  • shuffle：default=True
  • seed：如果shuffle=True，对于分布式中所有处理相同，default seed=0
  • drop_last：当数据除GPU无法取整的时候，是否将多余数据舍去，不舍去的话需要从头取数据补全，default=False，即不舍弃

设置dataset的子集用于分布式训练
Sampler that restricts data loading to a subset of the dataset.

注意在分布式的模式中，在每个epoch要调用set.epoch函数，不然进行迭代时每次都是相同的数据集顺序

这个函数看着介绍比较难懂，下面通过例子来解释：

不调用set.epoch

运行：CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_port 29501 exe.py

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import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import random
import numpy as np

seed = 1234
random.seed(seed)  # seed for module random
np.random.seed(seed)  # seed for numpy
torch.manual_seed(seed)  # seed for PyTorch CPU
torch.cuda.manual_seed(seed)  # seed for current PyTorch GPU
torch.cuda.manual_seed_all(seed)  # seed for all PyTorch GPUs

output_size = 2
batch_size = 2
data_size = 16

torch.distributed.init_process_group(backend="nccl")
local_rank = torch.distributed.get_rank()
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
class CustomDataset(Dataset):
    def __init__(self, length, local_rank):
        self.len = length
        self.data = torch.tensor([1,2,3,4,
                                 5,6,7,8,
                                 9,10,11,12,
                                 13,14,15,16]).to('cuda')
        self.local_rank = local_rank
    def __getitem__(self, index):
        return self.data[index]
    def __len__(self):
        return self.len

dataset = CustomDataset(data_size, local_rank)
sampler = DistributedSampler(dataset)
data_loader = DataLoader(dataset=dataset,
                         batch_size=batch_size,
                         sampler=sampler)

for epoch in range(2):
    # sampler.set_epoch(epoch)
    for data in data_loader:
        if local_rank==0:
            print(data)
'''
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
tensor([13, 10], device='cuda:0')
tensor([12, 14], device='cuda:0')
tensor([ 3, 16], device='cuda:0')
tensor([5, 8], device='cuda:0')
tensor([13, 10], device='cuda:0')
tensor([12, 14], device='cuda:0')
tensor([ 3, 16], device='cuda:0')
tensor([5, 8], device='cuda:0')
'''

调用set.epoch，即将上述代码中的sampler.set_epoch(epoch)注释取消掉

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'''
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
tensor([13, 10], device='cuda:0')
tensor([12, 14], device='cuda:0')
tensor([ 3, 16], device='cuda:0')
tensor([5, 8], device='cuda:0')
tensor([6, 7], device='cuda:0')
tensor([12,  8], device='cuda:0')
tensor([ 2, 10], device='cuda:0')
tensor([11, 14], device='cuda:0')
'''

对于上述输出，当不使用set.epoch时，两个epoch的cuda:0中的数据顺序是一致的，而使用set.epoch时，两个epoch的cuda:0中的数据不同，也就是说，set.epoch在每个epoch设置了不同的随机种子
我这里采用了两张卡，最终数据是平均分配的，也就是说数据被随机的分成了两份进行分配
还有一点需要注意的：如果我们在训练过程中加入了torch.utils.data.BatchSampler，原理依旧是不变的

2. BatchSampler

torch.utils.data.BatchSampler(sampler, batch_size, drop_last)

  • sampler: 一个可以迭代对象
  • batch_size: mini-batch的大小
  • drop_last: True的话凑不够batch_size的那部分会被舍弃

产生一个mini-batch的索引

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from torch.utils.data import BatchSampler
sampler = list(BatchSampler(range(10), batch_size=3, drop_last=True))
sampler2 = list(BatchSampler(range(10), batch_size=3, drop_last=False))
print(sampler)
print(sampler2)
'''
[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
'''

一般来说在分布式训练的时候，先执行DistributedSampler，然后执行BatchSampler，将BatchSampler传入到DataLoader即可

harryteaustc

computer vision