dmlc--dgl
Hongzhi (Steve), Chen
0890f56685
* part1 * more * more * spmatrix * code Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>
65 行
2.0 KiB
Python
65 行
2.0 KiB
Python
"""Softmax op for SparseMatrix"""
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# pylint: disable=invalid-name, W0622
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import torch
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from .sparse_matrix import SparseMatrix
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__all__ = ["softmax"]
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def softmax(input: SparseMatrix) -> SparseMatrix:
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"""Applies row-wise softmax to the non-zero elements of the sparse matrix.
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Equivalently, applies softmax to the non-zero elements of the sparse
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matrix along the column (``dim=1``) dimension.
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If :attr:`input.val` takes shape ``(nnz, D)``, then the output matrix
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:attr:`output` and :attr:`output.val` take the same shape as :attr:`input`
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and :attr:`input.val`. :attr:`output.val[:, i]` is calculated based on
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:attr:`input.val[:, i]`.
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Parameters
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----------
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input : SparseMatrix
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The input sparse matrix
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Returns
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-------
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SparseMatrix
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The output sparse matrix
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Examples
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--------
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Case1: matrix with values of shape (nnz)
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>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
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>>> nnz = len(row)
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>>> val = torch.arange(nnz).float()
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>>> A = dglsp.spmatrix(indices, val)
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>>> dglsp.softmax(A)
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SparseMatrix(indices=tensor([[0, 0, 1, 2],
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[1, 2, 2, 0]]),
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values=tensor([0.2689, 0.7311, 1.0000, 1.0000]),
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shape=(3, 3), nnz=4)
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Case2: matrix with values of shape (nnz, D)
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>>> indices = torch.tensor([[0, 0, 1, 2], [1, 2, 2, 0]])
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>>> val = torch.tensor([[0., 7.], [1., 3.], [2., 2.], [3., 1.]])
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>>> A = dglsp.spmatrix(indices, val)
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>>> dglsp.softmax(A)
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SparseMatrix(indices=tensor([[0, 0, 1, 2],
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[1, 2, 2, 0]]),
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values=tensor([[0.2689, 0.9820],
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[0.7311, 0.0180],
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[1.0000, 1.0000],
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[1.0000, 1.0000]]),
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shape=(3, 3), nnz=4, val_size=(2,))
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"""
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return SparseMatrix(torch.ops.dgl_sparse.softmax(input.c_sparse_matrix))
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SparseMatrix.softmax = softmax
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