不同设备上的张量可以相加吗？

发布于 2025-01-14 01:20:19 字数 727 浏览 3 评论 0原文

最近我发现了一件奇怪的事情。据我所知，当你想对两个张量进行一些操作时，你应该确保它们位于同一设备上。但是，当我这样编写代码时，它会意外运行

import torch
a = torch.tensor(1, device='cuda')
print(a.device)
b = torch.tensor(2, device='cpu')
print(b.device)
torch(a+b)


cuda:0
cpu
tensor(3, device='cuda:0')

并且无法在我的代码中工作，如下所示：

pts_1_tile = torch.tensor([[0], [0]], dtype=torch.float32)
torch.add(pred_4pt_shift, pts_1_tile)

这里 pred_4pt_shift 是子网的中间结果,它是 GPU 上的张量。我的问题是，为什么第一个代码可以工作，但第二个代码报告这个不同的设备错误？

原文

I found a curious thing recently. As far as I know, when you want to do some operations on two tensors, you should make sure that they are on the same device. But when I write my code like this, it runs unexpectly

import torch
a = torch.tensor(1, device='cuda')
print(a.device)
b = torch.tensor(2, device='cpu')
print(b.device)
torch(a+b)


cuda:0
cpu
tensor(3, device='cuda:0')

And it can't work in my code like this:

pts_1_tile = torch.tensor([[0], [0]], dtype=torch.float32)
torch.add(pred_4pt_shift, pts_1_tile)

here pred_4pt_shift is an intermediate result of a sub-Net, and it is a tensor on GPU.
My question is that why the first code can work but the second one reports this different device error?

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剧终人散尽 2025-01-21 01:20:19

我猜你的意思是 print(a+b) 而不是 torch(a+b)。

标量张量是一种特殊情况，可以自动移动到目标设备。
如果将 a 和 b 定义为一维张量，则会出错：

import torch
a = torch.tensor([1], device='cuda')
b = torch.tensor([2], device='cpu')
print(a+b)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

I guess you mean print(a+b) rather than torch(a+b).

Scalar tensor is a special case which could be automatically moved to target device.
If you define a and b as 1-d tensor, it will error out:

import torch
a = torch.tensor([1], device='cuda')
b = torch.tensor([2], device='cpu')
print(a+b)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!

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