Hi,
The functional methods in torch.nn are not all elementary functions for the autograd. So there isn’t a single backward function to call for them.
What you can do though is get the gradient with the regular autograd:
# inp that requires_grad=True and grad_output that match what you want to compute.
# If you want a full jacobian, you will need a for loop to reconstruct it line by line.
out = functional.softmax(inp)
grad = torch.autograd.grad(out, inp, grad_output)

What is the issue you have with functional.jacobian? What do you mean by wrong result?