Hi @albanD,
Thank you so much for your response.
Is it true that if my learnable parameters are in module1 and module2, and I do:
a1 = func1(input)
b1 = module1(a1)
c1 = func2(b1)

a2 = func1(c1)
b2 = module2(a2)
c2 = func2(b2)

loss = crit(c2)

(1) I will need only critand func2 to be differentiable? How does backward-pass work in the aforementioned scenario?
(2) As I understand, my problem is a kind of disconnected graph since in the backward pass I just want to compute the gradients of loss -> b2 -> b1. Is there any ways to avoid Autograd and do the back-propagation manually?
Best!