梯度计算所需的变量之一已通过就地操作修改：[torch.cuda.FloatTensor [640]] 为第 4 版；

问题描述

我想使用 pytorch distributedDataParallel 进行对抗训练。损失函数是Trades。代码可以在DataParallel 模式下运行。但是在 distributedDataParallel 模式下，我收到了这个错误。 当我将loss改为AT时，它可以成功运行。为什么不能在交易亏损的情况下运行？两个损失函数如下：

-- 进程 1 因以下错误而终止：

Traceback (most recent call last):
  File "/home/lthpc/.conda/envs/bba/lib/python3.7/site-packages/torch/multiprocessing/spawn.py",line 19,in _wrap
    fn(i,*args)
  File "/data/zsd/defense/my_adv_training/muti_gpu_2.py",line 170,in main_worker
    train_loss = train(train_loader,model,optimizer,epoch,local_rank,args)
  File "/data/zsd/defense/my_adv_training/muti_gpu_2.py",line 208,in train
    loss = Trades(model,x,y,args.epsilon,args.step_size,args.num_steps,beta=6.0)
  File "/data/zsd/defense/my_adv_training/loss_functions.py",line 137,in Trades
    loss_kl.backward()
  File "/home/lthpc/.conda/envs/bba/lib/python3.7/site-packages/torch/tensor.py",line 221,in backward
    torch.autograd.backward(self,gradient,retain_graph,create_graph)
  File "/home/lthpc/.conda/envs/bba/lib/python3.7/site-packages/torch/autograd/__init__.py",line 132,in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [640]] is at version 4; expected version 3 instead. Hint: enable anomaly detection to find the operation that Failed to compute its gradient,with torch.autograd.set_detect_anomaly(True).  

.

for i,(x,y) in enumerate(train_loader):
    # measure data loading time
    x,y = x.cuda(local_rank,non_blocking=True),y.cuda(local_rank,non_blocking=True)
    
    loss = Trades(model,beta=6.0)  
    torch.distributed.barrier()      
   
    optimizer.zero_grad()
    loss.backward(retain_graph=True)
    optimizer.step()

def Trades():
    model.eval()
    criterion_kl = nn.KLDivLoss(reduction='sum')
    x_adv = x.detach() + 0.001 * torch.randn_like(x).detach()
    nat_output = model(x)
    for _ in range(num_steps):
        x_adv.requires_grad_()
        with torch.enable_grad():
            loss_kl = criterion_kl(F.log_softmax(model(x_adv),dim=1),F.softmax(nat_output,dim=1))
        loss_kl.backward()
        eta = step_size * x_adv.grad.sign()
        x_adv = x_adv.detach() + eta
        x_adv = torch.min(torch.max(x_adv,x - epsilon),x + epsilon)
        x_adv = torch.clamp(x_adv,0.0,1.0)
    model.train()
    x_adv = Variable(x_adv,requires_grad=False)
    optimizer.zero_grad()
    # calculate robust loss
    logits = model(x)
    loss_natural = nn.CrossEntropyLoss()(logits,y)
    loss_robust = (1.0 / x.size(0)) * criterion_kl(F.log_softmax(model(x_adv),F.softmax(logits,dim=1))
    loss = loss_natural + beta * loss_robust
    return loss

def AT():
    model.eval()
    x_adv = x.detach() + torch.from_numpy(np.random.uniform(-epsilon,epsilon,x.shape)).float().cuda()
    x_adv = torch.clamp(x_adv,1.0)
    for k in range(num_steps):
        x_adv.requires_grad_()
        output = model(x_adv)
        model.zero_grad()
        with torch.enable_grad():
            loss = nn.CrossEntropyLoss()(output,y)
        loss.backward()
        eta = step_size * x_adv.grad.sign()
        x_adv = x_adv.detach() + eta
        x_adv = torch.min(torch.max(x_adv,1.0)
    x_adv = Variable(x_adv,requires_grad=False)

    model.train()
    logits_adv = model(x_adv)
    loss = nn.CrossEntropyLoss()(logits_adv,y)
    return loss

解决方法

我更改了交易代码并解决了这个错误。但我不知道为什么会这样。

def trades():
    model.eval()
    criterion_kl = nn.KLDivLoss(reduction='sum')
    x_adv = x.detach() + 0.001 * torch.randn_like(x).detach()
    nat_output = model(x)
    for _ in range(num_steps):
        x_adv.requires_grad_()
        with torch.enable_grad():
            loss_kl = criterion_kl(F.log_softmax(model(x_adv),dim=1),F.softmax(nat_output,dim=1))
        grad = torch.autograd.grad(loss_kl,[x_adv])[0]
        x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
        x_adv = torch.min(torch.max(x_adv,x - epsilon),x + epsilon)
        x_adv = torch.clamp(x_adv,0.0,1.0)
    model.train()
    x_adv = Variable(x_adv,requires_grad=False)
    optimizer.zero_grad()
    # calculate robust loss
    logits = model(x)
    loss_natural = nn.CrossEntropyLoss()(logits,y)
    loss_robust = (1.0 / x.size(0)) * criterion_kl(F.log_softmax(model(x_adv),F.softmax(logits,dim=1))
    loss = loss_natural + beta * loss_robust
    return loss