Pytorch:为什么在DDP中记录失败？

import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, Dataset

# 假设我们有一个简单的模型和数据集
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.linear = nn.Linear(10, 1)

    def forward(self, x):
        return self.linear(x)

class SimpleDataset(Dataset):
    def __init__(self):
        self.data = torch.randn(100, 10)
        self.target = torch.randn(100, 1)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.target[idx]

def main(rank, world_size):
    # 初始化进程组
    dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)

    # 创建模型并移动到对应的设备
    model = SimpleModel().to(rank)
    ddp_model = DDP(model, device_ids=[rank])

    # 创建数据加载器
    dataset = SimpleDataset()
    sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank)
    dataloader = DataLoader(dataset, batch_size=32, sampler=sampler)

    # 定义损失函数和优化器
    criterion = nn.MSELoss()
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.01)

    # 训练循环
    for epoch in range(10):
        sampler.set_epoch(epoch)
        for data, target in dataloader:
            data, target = data.to(rank), target.to(rank)
            optimizer.zero_grad()
            output = ddp_model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

    # 保存模型（仅在主进程中）
    if rank == 0:
        torch.save(ddp_model.state_dict(), 'model.pth')

if __name__ == "__main__":
    world_size = 4
    torch.multiprocessing.spawn(main, args=(world_size,), nprocs=world_size, join=True)