Does PyTorch support dict-like objects, through which we can backpropagate gradients, like Tensors in PyTorch?
My goal is to compute gradients with respect to a few (1%) elements of a large matrix. But if I use PyTorch's standard Tensors to store the matrix, I need to keep the whole matrix in my GPU, which causes problems due to limited GPU memory available during training. So I was thinking whether I could store the matrix as a dict instead, indexing only the relevant elements of the matrix, and computing gradients and backpropagating w.r.t those select elements only.
So far, I have tried using Tensors only, but it's causing memory issues for the above reasons. So I searched extensively for alternate options like dicts in PyTorch but couldn't find any such information on Google.