**args上运行函数的输出。 torch.utils.checkpoint.checkpoint_sequential(functions,segments,input,**kwargs)[source] 用于检查点顺序模型的辅助函数。顺序模型按顺序(顺序)执行一列模块/功能。因此,我们可以将该模型划分为各个分段和每个分段的检查点。除最后一个段外,所有段都将以torc
如果不需要与非检查点传递相比的确定性输出,则向检查点或checkpoint_sequential提供preserve_rng_state=False,以省略每个检查点期间的RNG状态的存储和恢复。 存储逻辑将当前设备的RNG状态和所有cuda张量参数的设备保存并恢复到run_fn。但是,逻辑无法预测用户是否将张量移动到run_fn本身内的新设备。因此,如果您将张量移动到...
🐛 Bug Using torch.utils.checkpoint.checkpoint_sequential and torch.autograd.grad breaks when used in combination with DistributedDataParallel resulting in the following stacktrace Traceback (most recent call last): File "minimal_buggy_2...
torch.utils.checkpoint.checkpoint_sequential(functions, segments, *inputs) 用于checkpointsequential模型的辅助函数。 sequential模型按顺序执行一系列模块/函数(按顺序)。因此,我们可以将这种模型分为不同的部分和checkpoint。除最后一个段以外的所有段都将以某种torch.no_grad()方式运行 ,即不存储中间活动。将保存每...
fromtorch.utils.checkpointimportcheckpoint_sequential device="cuda"iftorch.cuda.is_available() else"cpu" %matplotlibinline importrandom nvidia_smi.nvmlInit() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
in checkpoint return CheckpointFunction.apply(function, preserve, *args) File "/usr/local/lib/python3.10/site-packages/torch/autograd/function.py", line 553, in apply return super().apply(*args, **kwargs) # type: ignore[misc] File "/usr/local/lib/python3.10/site-packages/torch/utils/check...
例如，如果你想让原来的线性层 torch.nn. linear 是并行的，只需将 torch 变成 ts，并调用带有 dim 参数的子类 nn.ParallelLinear，如下所示：import torchshard as tsts.init_process_group(group_size=2) # init parallel groupsm = torch.nn.Sequential( torch.nn.Linear(20, 30, bias=True), ...
import torch.utils.data.distributed from torchvision import transforms import torch.nn as nn from datetime import datetime 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, stri...
utils.data.DataLoader(dataset,batch_size=256) model = AlexNet(NUM_CLASSES) checkpoint = torch.load(save_path+'modelparams.pth') model.load_state_dict(checkpoint['net']) model.to(DEVICE) train_acc_list=checkpoint['train_acc_list'] val_acc_list=checkpoint['val_acc_list'] cost_list=...
model=models.vgg16()# we do not specify ``weights``, i.e. create untrained modelmodel.load_state_dict(torch.load('model_weights.pth'))model.eval()---VGG((features):Sequential((0):Conv2d(3,64,kernel_size=(3,3),stride=(1,1),padding=(1,1))(1):ReLU(inplace=True)(2):Conv2d...