torch.Tensor.to_sparse_csc — PyTorch 2.7 documentation (original) (raw)

Tensor.to_sparse_csc() → Tensor¶

Convert a tensor to compressed column storage (CSC) format. Except for strided tensors, only works with 2D tensors. If the selfis strided, then the number of dense dimensions could be specified, and a hybrid CSC tensor will be created, with dense_dim dense dimensions and self.dim() - 2 - dense_dim batch dimension.

Parameters

dense_dim (int, optional) – Number of dense dimensions of the resulting CSC tensor. This argument should be used only ifself is a strided tensor, and must be a value between 0 and dimension of self tensor minus two.

Example:

dense = torch.randn(5, 5) sparse = dense.to_sparse_csc() sparse._nnz() 25

dense = torch.zeros(3, 3, 1, 1) dense[0, 0] = dense[1, 2] = dense[2, 1] = 1 dense.to_sparse_csc(dense_dim=2) tensor(ccol_indices=tensor([0, 1, 2, 3]), row_indices=tensor([0, 2, 1]), values=tensor([[[1.]],

                  [[1.]],

                  [[1.]]]), size=(3, 3, 1, 1), nnz=3,
   layout=torch.sparse_csc)