使用 PyTorch DistributedDataParallel 在多個節點上訓練時進程卡住

Question

我正在嘗試從mnist-distributed.py的分布式數據並行訓練運行腳本mnist-distributed.py 。 我也在這里粘貼了相同的代碼。 （我已經用abcd替換了我的實際MASTER_ADDR以便在此處發布）。

import os
import argparse
import torch.multiprocessing as mp
import torchvision
import torchvision.transforms as transforms
import torch
import torch.nn as nn
import torch.distributed as dist

class ConvNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc = nn.Linear(7*7*32, num_classes)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('-n', '--nodes', default=1, type=int, metavar='N')
    parser.add_argument('-g', '--gpus', default=1, type=int,
                        help='number of gpus per node')
    parser.add_argument('-nr', '--nr', default=0, type=int,
                        help='ranking within the nodes')
    parser.add_argument('--epochs', default=2, type=int, metavar='N',
                        help='number of total epochs to run')
    args = parser.parse_args()
    args.world_size = args.gpus * args.nodes               
    os.environ['MASTER_ADDR'] = 'a.b.c.d'              
    os.environ['MASTER_PORT'] = '8890'                    
    mp.spawn(train, nprocs=args.gpus, args=(args,))       

def train(gpu, args):
    rank = args.nr * args.gpus + gpu                              
    dist.init_process_group(                                   
        backend='nccl',                                         
        init_method='env://',                                   
        world_size=args.world_size,                              
        rank=rank                                               
    )                                                          
    
    torch.manual_seed(0)
    model = ConvNet()
    torch.cuda.set_device(gpu)
    model.cuda(gpu)
    batch_size = 100
    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(gpu)
    optimizer = torch.optim.SGD(model.parameters(), 1e-4)
    
    # Wrap the model
    model = nn.parallel.DistributedDataParallel(model,
                                                device_ids=[gpu])

    # Data loading code
    train_dataset = torchvision.datasets.MNIST(
        root='./data',
        train=True,
        transform=transforms.ToTensor(),
        download=True
    )                                               
    train_sampler = torch.utils.data.distributed.DistributedSampler(
        train_dataset,
        num_replicas=args.world_size,
        rank=rank
    )

    train_loader = torch.utils.data.DataLoader(
        dataset=train_dataset,
       batch_size=batch_size,
       shuffle=False,            
       num_workers=0,
       pin_memory=True,
      sampler=train_sampler)     

    total_step = len(train_loader)
    for epoch in range(args.epochs):
        for i, (images, labels) in enumerate(train_loader):
            images = images.cuda(non_blocking=True)
            labels = labels.cuda(non_blocking=True)
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)

            # Backward and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if (i + 1) % 100 == 0 and gpu == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(
                    epoch + 1, 
                    args.epochs, 
                    i + 1, 
                    total_step,
                    loss.item())
                   )

if __name__ == '__main__':
    main()

有 2 個節點，每個節點有 2 個 GPU。 我從主節點的終端運行此命令-

python mnist-distributed.py -n 2 -g 2 -nr 0

，然后這是來自另一個節點的終端-

python mnist-distributed.py -n 2 -g 2 -nr 1

但是隨后我的進程卡住了，兩個終端上都沒有輸出。

使用以下命令在單個節點上運行相同的代碼效果很好-

python mnist-distributed.py -n 1 -g 2 -nr 0

Answer 1

我遇到了類似的問題。 問題解決了

sudo vi /etc/default/grub

編輯它：

#GRUB_CMDLINE_LINUX=""                           <----- Original commented
GRUB_CMDLINE_LINUX="iommu=soft"           <------ Change

sudo update-grub

重新啟動以查看更改。

參考： https : //github.com/pytorch/pytorch/issues/1637#issuecomment-338268158