I suppose PyTorch does use a computational graph in autograd; but indeed the forward pass usually does not make use of it.
I could define by hand new .forward() functions that would do the job and compute only what is needed, if I know which nodes to compute and if the network is simple; but for complex networks, I would have wished to do that automatically: take a network as input, identify the desired subgraph, and run it; for this I need the computational graph of the network.
I guess I can have a look at the implementation of .backward() for inspiration, but that’s going to take a while…