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dgl/docs/source/api/python/dgl.sparse_v0.rst
2023-12-13 15:33:38 +08:00

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.. _apibackend:
dgl.sparse
=================================
`dgl.sparse` is a library for sparse operators that are commonly used in GNN models.
Sparse matrix class
-------------------------
.. currentmodule:: dgl.sparse
.. class:: SparseMatrix
A SparseMatrix can be created from Coordinate format indices using the
:func:`spmatrix` constructor:
>>> indices = torch.tensor([[1, 1, 2],
>>> [2, 4, 3]])
>>> A = dglsp.spmatrix(indices)
SparseMatrix(indices=tensor([[1, 1, 2],
[2, 4, 3]]),
values=tensor([1., 1., 1.]),
shape=(3, 5), nnz=3)
Creation Ops
````````
.. autosummary::
:toctree: ../../generated/
spmatrix
val_like
from_coo
from_csr
from_csc
diag
identity
Attributes and methods
``````````````````````
.. autosummary::
:toctree: ../../generated/
SparseMatrix.shape
SparseMatrix.nnz
SparseMatrix.dtype
SparseMatrix.device
SparseMatrix.val
SparseMatrix.row
SparseMatrix.col
SparseMatrix.indices
SparseMatrix.coo
SparseMatrix.csr
SparseMatrix.csc
SparseMatrix.coalesce
SparseMatrix.has_duplicate
SparseMatrix.to_dense
SparseMatrix.to
SparseMatrix.cuda
SparseMatrix.cpu
SparseMatrix.float
SparseMatrix.double
SparseMatrix.int
SparseMatrix.long
SparseMatrix.transpose
SparseMatrix.t
SparseMatrix.T
SparseMatrix.neg
SparseMatrix.reduce
SparseMatrix.sum
SparseMatrix.smax
SparseMatrix.smin
SparseMatrix.smean
SparseMatrix.softmax
Operators
---------
.. currentmodule:: dgl.sparse
Elementwise Operators
````````
.. autosummary::
:toctree: ../../generated/
add
sub
mul
div
power
Matrix Multiplication
````````
.. autosummary::
:toctree: ../../generated/
matmul
spmm
bspmm
spspmm
sddmm
bsddmm
Non-linear activation functions
````````
.. autosummary::
:toctree: ../../generated/
softmax
Broadcast operators
````````
.. autosummary::
:toctree: ../../generated/
sp_broadcast_v
sp_add_v
sp_sub_v
sp_mul_v
sp_div_v