Each torch.Tensor has a torch.dtype, torch.device, and torch.layout.
torch.dtype
CLASS torch.dtype
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torch.device
CLASS torch.device
通过str 'cuda', 'cuda:0', 'cpu'设置，或str加上设备序号
>>> torch.device('cuda:0')
device(type='cuda', index=0)
>>> torch.device('cpu')
device(type='cpu')
>>> torch.device('cuda') # current cuda device
device(type='cuda')
>>> torch.device('cuda', 0)
device(type='cuda', index=0)
>>> torch.device('cpu', 0)
device(type='cpu', index=0)
>>> # Example of a function that takes in a torch.device
>>> cuda1 = torch.device('cuda:1')
>>> torch.randn((2,3), device=cuda1)
>>> # You can substitute the torch.device with a string
>>> torch.randn((2,3), device='cuda:1')
>>> torch.device(1)
device(type='cuda', index=1)
>>> torch.randn((2,3), device=torch.device('cuda:1'))
>>> torch.randn((2,3), device='cuda:1')
>>> torch.randn((2,3), device=1) # legacy
torch.layout
CLASS torch.layout
torch.layout表示torch.Tensor内存布局的对象。目前，我们支持torch.strided(dense Tensors)并为torch.sparse_coo(sparse COO Tensors)提供实验支持。
torch.strided代表密集张量，是最常用的内存布局。每个strided张量都会关联 一个torch.Storage，它保存着它的数据。
