when copying modules/tensors around, which one should I use? are they interchangable? Thanks a lot

When you use .data, you get a new Tensor with requires_grad=False, so cloning it won’t involve autograd. So both are equivalent, but there might be a (small) speed difference, I am not sure about that. Another use case could is when you want to clone/copy a non-parameter Tensor without autograd. Yo…

Hi @Shisho_Sama , For Tensors in most cases, you should go for clone since this is a PyTorch operation that will be recorded by autograd. >>> t = torch.rand(1, requires_grad=True) >>> t.clone() tensor([0.4847], grad_fn=<CloneBackward>) # <=== as you can see here When it comes to Module, there is n…

Hi, Thanks a lot. So this means, when I do clone() on tensors, their clones will still be on the graph and any operations on them will be reflected in the graph right? for example changing the values or attributes will also change the original tensor as well? or affect the graph computation when d…

Thank you very much. I really appreciate it :slight_smile:

Is there any difference with t.clone().detach()?

Yes there is. Though both methods create same outcomes, however, t.clone().detach() is less efficient. The t.clone() with create a copy that attaches to the graph, then it will create another copy (detach()). So there will be more redundant.

[image] spanev: For Tensor s in most cases, you should go for clone since this is a PyTorch operation that will be recorded by autograd. I never understood this. Why would one ever want to have clone be in the computation graph? It’s just the identity! When I make a copy of something I usual…

Let me see if I understand (it seems the accepted answer here is outdated, .data is not in the library or going to be removed according to what I’ve read in other answers with from albanD). .clone() produces a new tensor instance with a new memory allocation to the tensor data. In addition it remem…

[image] pinocchio: I am still seeking clarification on the history part. Is it a deep copy of that or a pointer copy if we use deep copy? The history will not be copied, as you cannot call copy.deepcopy on a non-leaf tensor: x = torch.randn(1, requires_grad=True) y = x + 1 copy.deepcopy(y) > …

[image] ptrblck: > RuntimeError: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment I always wondered why that error appeared! Whats the choice for that semantics?

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Copy.deepcopy() vs clone()

ptrblck June 17, 2020, 7:38am 9

Answered here and here.

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