torch.sparse_csc_tensor

torch.sparse_csc_tensor(ccol_indices, row_indices, values, size=None, *, dtype=None, device=None, requires_grad=False) → Tensor

Constructs a sparse tensor in CSC (Compressed Sparse Column) with specified values at the given ccol_indices and row_indices. Sparse matrix multiplication operations in CSC format are typically faster than that for sparse tensors in COO format. Make you have a look at the note on the data type of the indices.

Parameters:

• ccol_indices (array_like) – (B+1)-dimensional array of size (*batchsize, ncols + 1). The last element of each batch is the number of non-zeros. This tensor encodes the index in values and row_indices depending on where the given column starts. Each successive number in the tensor subtracted by the number before it denotes the number of elements in a given column.

• row_indices (array_like) – Row co-ordinates of each element in values. (B+1)-dimensional tensor with the same length as values.

• values (array_list) – Initial values for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types that represents a (1+K)-dimensonal tensor where K is the number of dense dimensions.

• size (list, tuple, torch.Size, optional) – Size of the sparse tensor: (*batchsize, nrows, ncols, *densesize). If not provided, the size will be inferred as the minimum size big enough to hold all non-zero elements.

Keyword Arguments:

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from values.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example::

>>> ccol_indices = [0, 2, 4]
>>> row_indices = [0, 1, 0, 1]
>>> values = [1, 2, 3, 4]
>>> torch.sparse_csc_tensor(torch.tensor(ccol_indices, dtype=torch.int64),
...                         torch.tensor(row_indices, dtype=torch.int64),
...                         torch.tensor(values), dtype=torch.double)
tensor(ccol_indices=tensor([0, 2, 4]),
       row_indices=tensor([0, 1, 0, 1]),
       values=tensor([1., 2., 3., 4.]), size=(2, 2), nnz=4,
       dtype=torch.float64, layout=torch.sparse_csc)