Error: ValueError: can't optimize a non-leaf Tensor

I get this error ValueError: can’t optimize a non-leaf Tensor. My variable is leaf (torch.Tensor([1.]).requires_grad_().to(device)) however when I pass it in optimizer I get this error.

.to is a differentiable operation and hence is recorded by autograd which makes your tensor as non-leaf.
Please see if this helps:

import torch
a = torch.tensor([1.0], device='cuda', requires_grad=True)
print(a.is_leaf) # True - you might want to use this approach

b = torch.Tensor([1.]).requires_grad_().to('cuda')
print(b.is_leaf) # False

Thanks for the reply, but again I get the same error.
The problem is when
torch.optim.SGD(list(a), lr=0.1). Here I get the error
ValueError: can’t optimize a non-leaf Tensor
If I set
a = torch.Tensor([1.]).requires_grad_()
then I can pass it through the optimizer but I can not optimize the “a”

Use this instead:

opt = torch.optim.SGD([a], lr=0.1)

The reason why list(a) doesn’t work but [a] does:

print(list(a)) # [tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>)]
print([a]) # [tensor([1.], device='cuda:0', requires_grad=True)]

In the first case, autograd was able to track the operation which is evident through the grad_fn attribute of a and so a doesn’t remain a leaf anymore.

1 Like

Great it is working now!
Thank you so much!