"""Softmax op for SparseMatrix"""
# pylint: disable=invalid-name, W0622
import torch
from .sparse_matrix import SparseMatrix
__all__ = ["softmax"]
[docs]def softmax(input: SparseMatrix, dim: int = 1) -> SparseMatrix:
"""Applies softmax to the non-zero elements of the sparse matrix on the
dimension :attr:``dim``. dim = 0 or 1 indicates column-wise or row-wise
softmax respectively.
If :attr:`input.val` takes shape ``(nnz, D)``, then the output matrix
:attr:`output` and :attr:`output.val` take the same shape as :attr:`input`
and :attr:`input.val`. :attr:`output.val[:, i]` is calculated based on
:attr:`input.val[:, i]`.
Parameters
----------
input : SparseMatrix
The input sparse matrix
Returns
-------
SparseMatrix
The output sparse matrix
Examples
--------
Case1: row-wise softmax on matrix with values of shape (nnz)
>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
>>> val = torch.tensor([0., 1., 2., 3.])
>>> A = dglsp.spmatrix(indices, val)
>>> dglsp.softmax(A)
SparseMatrix(indices=tensor([[0, 0, 1, 2],
[1, 2, 2, 0]]),
values=tensor([0.2689, 0.7311, 1.0000, 1.0000]),
shape=(3, 3), nnz=4)
Case2: row-wise softmax on matrix with values of shape (nnz, D)
>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
>>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]])
>>> A = dglsp.spmatrix(indices, val)
>>> dglsp.softmax(A)
SparseMatrix(indices=tensor([[0, 0, 1, 2],
[1, 2, 2, 0]]),
values=tensor([[0.2689, 0.9820],
[0.7311, 0.0180],
[1.0000, 1.0000],
[1.0000, 1.0000]]),
shape=(3, 3), nnz=4, val_size=(2,))
Case3: column-wise softmax on matrix with values of shape (nnz)
>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
>>> val = torch.tensor([0., 1., 2., 3.])
>>> A = dglsp.spmatrix(indices, val)
>>> dglsp.softmax(A, 0)
SparseMatrix(indices=tensor([[0, 0, 1, 2],
[1, 2, 2, 0]]),
values=tensor([1.0000, 0.2689, 0.7311, 1.0000]),
shape=(3, 3), nnz=4)
"""
return SparseMatrix(
torch.ops.dgl_sparse.softmax(input.c_sparse_matrix, dim)
)
SparseMatrix.softmax = softmax