Pytorch autograd fails with "RuntimeError: differentiated input is unreachable" after collecting inputs
Pytorch version 0.3.1

EDIT: I am rewriting this question to be simpler, as I've narrowed down the bug.

I have some variables:

x = ag.Variable(torch.ones(1, 1), requires_grad = True)
y = ag.Variable(torch.ones(1, 1), requires_grad = True)
z = ag.Variable(torch.ones(1, 1), requires_grad = True)


I then create a variable representing their concatenation:

w = torch.cat([x, y, z])
f = x + y + z


Then I try to take derivatives:

ag.grad(f, x, retain_graph=True, create_graph=True)


This is fine and returns 1, as expected. Same for y and z.

However,

ag.grad(f, w, retain_graph=True, create_graph=True)


Returns an error: RuntimeError: differentiated input is unreachable

Of course that makes sense - w is not explicitly used in the declaration of f. However, I’d like a behavior where one line of code can generate something like [1; 1; 1] as output.

Let’s say I wanted to conveniently batch my variables together, and then take the gradient of the whole shebang at once, rather than processing variables independently (which can make bookkeeping a nightmare). Is there any way to get the outcome I desire?

Does something like this work or you want to keep f = x + y + z?

w = torch.cat([x, y, z])
f = w[0] + w[1] + w[2]
print (ag.grad(f, w, retain_graph=True, create_graph=True))

# output (tensor([[ 1.],[ 1.],[ 1.]]),)

