There is no I guess. If you think about it, a list is agnostic to backpropagation. It does not appear in the graph cut. You could, for example, accumulate tensors of the same dimensions on a new dimension using torch.concatenate, however this would create a joint in the graph cut at the time of backpropagating that may be undesirable for you .
I cannot say 100% it’s not possible but according to pytorch working, which follows the flow of data to backpropagate, it’d always create this joint unless developers created a “gpu list” which I unknown at the time of replying.
Anyway I find this topic very interesting. Based on the code i’ve seen everone uses python lists.