Sorry to post again, don’t like to use an auxiliary list to do the autograd, will torch support in-place operation in the future?
I am curious that all AD must “push back” the item, and cannot access it via indexing. In my work, I need an if statement to determine which index I need. Is there really no solution?
The problem in the original code is not really the inplace but the fact that you define f as a “leaf Tensor that requires grad” even though you don’t need to.
You can simply change f to not require gradients anymore and remove the torch.no_grad.
That will give you the result you want 
For reference, the updated code:
import torch
from torch.autograd.functional import jacobian
from torch import tensor
def get_f (x):
f=torch.arange(0,3, requires_grad=False, dtype=torch.float64)
for i in looparray:
f[i] = x[i]**2
return f
looparray=torch.arange(0,3)
x=torch.arange(0,3, requires_grad=True, dtype=torch.float64)
J = jacobian(get_f, x).detach().numpy()
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Thank you, a following quick question. For example, define f requires grad. is df/d something or d something/ df? I remember it is the latter.
Because I will need df/d something for other cases
f requiring grad would be if you want d Loss / df which doesn’t make sense in your example
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and d loss / df can be done by hand right? since normally loss = (f-fo)^2
Yes, d loss / df is just 2 * (f - f0) which can be calculated via the chain rule (assuming that f and f0 are independent of each other, of course)