I'm looking to back-propagate gradients through a singular value decomposition for regularisation purposes. PyTorch currently does not support backpropagation through a singular value decomposition.
I know that I could write my own custom function that operates on a Variable; takes its .data tensor, applies the torch.svd to it, wraps a Variable around its singular values and returns it in the forward pass, and in the backward pass applies the appropriate Jacobian matrix to the incoming gradients.
However, I was wondering whether there was a more elegant (and potentially faster) solution, where I could overwrite the "Type Variable doesn't implement stateless method svd" Error directly, call Lapack, etc. ?
If someone could guide me through the appropriate steps and source files I need to look at, I'd be very grateful. I suppose these steps would similarly apply to other linear algebra operations which have no associated backward method currently.
