Is there a canonical way to exploit sparsity in batch operations torch.bmm() and torch.baddmm() yet? Interested mainly in sparse -> dense (dense -> sparse is also interesting).
If I have a batch of sparse input matrices, and a dense batch of matrices :
> mat1 = torch.zeros(4, 3, 5)
> mat1[1][1][1] = 1; mat1[2][2][2] = 1
> mat2 = torch.rand(4, 5, 6)
> torch.bmm(mat1, mat2)

Exploiting sparsity is quite an optimisation. If this isn’t available yet, which users might I liaise with to help out?
Edit: it seems there is a torch.SparseFloatTensor() available in a new release?