"""Matmul ops for SparseMatrix"""
# pylint: disable=invalid-name
from typing import Union
import torch
from .sparse_matrix import SparseMatrix
__all__ = ["spmm", "bspmm", "spspmm", "matmul"]
[docs]def spmm(A: SparseMatrix, X: torch.Tensor) -> torch.Tensor:
"""Multiplies a sparse matrix by a dense matrix, equivalent to ``A @ X``.
Parameters
----------
A : SparseMatrix
Sparse matrix of shape ``(L, M)`` with scalar values
X : torch.Tensor
Dense matrix of shape ``(M, N)`` or ``(M)``
Returns
-------
torch.Tensor
The dense matrix of shape ``(L, N)`` or ``(L)``
Examples
--------
>>> indices = torch.tensor([[0, 1, 1], [1, 0, 1]])
>>> val = torch.randn(indices.shape[1])
>>> A = dglsp.spmatrix(indices, val)
>>> X = torch.randn(2, 3)
>>> result = dglsp.spmm(A, X)
>>> type(result)
<class 'torch.Tensor'>
>>> result.shape
torch.Size([2, 3])
"""
assert isinstance(
A, SparseMatrix
), f"Expect arg1 to be a SparseMatrix object, got {type(A)}."
assert isinstance(
X, torch.Tensor
), f"Expect arg2 to be a torch.Tensor, got {type(X)}."
return torch.ops.dgl_sparse.spmm(A.c_sparse_matrix, X)
[docs]def bspmm(A: SparseMatrix, X: torch.Tensor) -> torch.Tensor:
"""Multiplies a sparse matrix by a dense matrix by batches, equivalent to
``A @ X``.
Parameters
----------
A : SparseMatrix
Sparse matrix of shape ``(L, M)`` with vector values of length ``K``
X : torch.Tensor
Dense matrix of shape ``(M, N, K)``
Returns
-------
torch.Tensor
Dense matrix of shape ``(L, N, K)``
Examples
--------
>>> indices = torch.tensor([[0, 1, 1], [1, 0, 2]])
>>> val = torch.randn(len(row), 2)
>>> A = dglsp.spmatrix(indices, val, shape=(3, 3))
>>> X = torch.randn(3, 3, 2)
>>> result = dglsp.bspmm(A, X)
>>> type(result)
<class 'torch.Tensor'>
>>> result.shape
torch.Size([3, 3, 2])
"""
assert isinstance(
A, SparseMatrix
), f"Expect arg1 to be a SparseMatrix object, got {type(A)}."
assert isinstance(
X, torch.Tensor
), f"Expect arg2 to be a torch.Tensor, got {type(X)}."
return spmm(A, X)
[docs]def spspmm(A: SparseMatrix, B: SparseMatrix) -> SparseMatrix:
"""Multiplies a sparse matrix by a sparse matrix, equivalent to ``A @ B``.
The non-zero values of the two sparse matrices must be 1D.
Parameters
----------
A : SparseMatrix
Sparse matrix of shape ``(L, M)``
B : SparseMatrix
Sparse matrix of shape ``(M, N)``
Returns
-------
SparseMatrix
Sparse matrix of shape ``(L, N)``.
Examples
--------
>>> indices1 = torch.tensor([[0, 1, 1], [1, 0, 1]])
>>> val1 = torch.ones(len(row1))
>>> A = dglsp.spmatrix(indices1, val1)
>>> indices2 = torch.tensor([[0, 1, 1], [0, 2, 1]])
>>> val2 = torch.ones(len(row2))
>>> B = dglsp.spmatrix(indices2, val2)
>>> dglsp.spspmm(A, B)
SparseMatrix(indices=tensor([[0, 0, 1, 1, 1],
[1, 2, 0, 1, 2]]),
values=tensor([1., 1., 1., 1., 1.]),
shape=(2, 3), nnz=5)
"""
assert isinstance(
A, SparseMatrix
), f"Expect A1 to be a SparseMatrix object, got {type(A)}."
assert isinstance(
B, SparseMatrix
), f"Expect A2 to be a SparseMatrix object, got {type(B)}."
return SparseMatrix(
torch.ops.dgl_sparse.spspmm(A.c_sparse_matrix, B.c_sparse_matrix)
)
[docs]def matmul(
A: Union[torch.Tensor, SparseMatrix], B: Union[torch.Tensor, SparseMatrix]
) -> Union[torch.Tensor, SparseMatrix]:
"""Multiplies two dense/sparse matrices, equivalent to ``A @ B``.
This function does not support the case where :attr:`A` is a \
``torch.Tensor`` and :attr:`B` is a ``SparseMatrix``.
* If both matrices are torch.Tensor, it calls \
:func:`torch.matmul()`. The result is a dense matrix.
* If both matrices are sparse, it calls :func:`dgl.sparse.spspmm`. The \
result is a sparse matrix.
* If :attr:`A` is sparse while :attr:`B` is dense, it calls \
:func:`dgl.sparse.spmm`. The result is a dense matrix.
* The operator supports batched sparse-dense matrix multiplication. In \
this case, the sparse matrix :attr:`A` should have shape ``(L, M)``, \
where the non-zero values have a batch dimension ``K``. The dense \
matrix :attr:`B` should have shape ``(M, N, K)``. The output \
is a dense matrix of shape ``(L, N, K)``.
* Sparse-sparse matrix multiplication does not support batched computation.
Parameters
----------
A : torch.Tensor or SparseMatrix
The first matrix.
B : torch.Tensor or SparseMatrix
The second matrix.
Returns
-------
torch.Tensor or SparseMatrix
The result matrix
Examples
--------
Multiplies a diagonal matrix with a dense matrix.
>>> val = torch.randn(3)
>>> A = dglsp.diag(val)
>>> B = torch.randn(3, 2)
>>> result = dglsp.matmul(A, B)
>>> type(result)
<class 'torch.Tensor'>
>>> result.shape
torch.Size([3, 2])
Multiplies a sparse matrix with a dense matrix.
>>> indices = torch.tensor([[0, 1, 1], [1, 0, 1]])
>>> val = torch.randn(indices.shape[1])
>>> A = dglsp.spmatrix(indices, val)
>>> X = torch.randn(2, 3)
>>> result = dglsp.matmul(A, X)
>>> type(result)
<class 'torch.Tensor'>
>>> result.shape
torch.Size([2, 3])
Multiplies a sparse matrix with a sparse matrix.
>>> indices1 = torch.tensor([[0, 1, 1], [1, 0, 1]])
>>> val1 = torch.ones(indices1.shape[1])
>>> A = dglsp.spmatrix(indices1, val1)
>>> indices2 = torch.tensor([[0, 1, 1], [0, 2, 1]])
>>> val2 = torch.ones(indices2.shape[1])
>>> B = dglsp.spmatrix(indices2, val2)
>>> result = dglsp.matmul(A, B)
>>> type(result)
<class 'dgl.sparse.sparse_matrix.SparseMatrix'>
>>> result.shape
(2, 3)
"""
assert isinstance(
A, (torch.Tensor, SparseMatrix)
), f"Expect arg1 to be a torch.Tensor or SparseMatrix, got {type(A)}."
assert isinstance(B, (torch.Tensor, SparseMatrix)), (
f"Expect arg2 to be a torch Tensor or SparseMatrix"
f"object, got {type(B)}."
)
if isinstance(A, torch.Tensor) and isinstance(B, torch.Tensor):
return torch.matmul(A, B)
assert not isinstance(A, torch.Tensor), (
f"Expect arg2 to be a torch Tensor if arg 1 is torch Tensor, "
f"got {type(B)}."
)
if isinstance(B, torch.Tensor):
return spmm(A, B)
return spspmm(A, B)
SparseMatrix.__matmul__ = matmul