"""DGL broadcast operator module."""
import operator
import torch
from .sparse_matrix import SparseMatrix, val_like
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def sp_broadcast_v(A: SparseMatrix, v: torch.Tensor, op: str) -> SparseMatrix:
"""Broadcast operator for sparse matrix and vector.
:attr:`v` is broadcasted to the shape of :attr:`A` and then the operator is
applied on the non-zero values of :attr:`A`.
There are two cases regarding the shape of v:
1. :attr:`v` is a vector of shape ``(1, A.shape[1])`` or ``(A.shape[1])``.
In this case, :attr:`v` is broadcasted on the row dimension of :attr:`A`.
2. :attr:`v` is a vector of shape ``(A.shape[0], 1)``. In this case,
:attr:`v` is broadcasted on the column dimension of :attr:`A`.
If ``A.val`` takes shape ``(nnz, D)``, then :attr:`v` will be broadcasted on
the ``D`` dimension.
Parameters
----------
A: SparseMatrix
Sparse matrix
v: torch.Tensor
Vector
op: str
Operator in ["add", "sub", "mul", "truediv"]
Returns
-------
SparseMatrix
Sparse matrix
Examples
--------
>>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
>>> val = torch.tensor([10, 20, 30])
>>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
>>> v = torch.tensor([1, 2, 3, 4])
>>> dglsp.sp_broadcast_v(A, v, "add")
SparseMatrix(indices=tensor([[1, 0, 2],
[0, 3, 2]]),
values=tensor([11, 24, 33]),
shape=(3, 4), nnz=3)
>>> v = torch.tensor([1, 2, 3]).view(-1, 1)
>>> dglsp.sp_broadcast_v(A, v, "add")
SparseMatrix(indices=tensor([[1, 0, 2],
[0, 3, 2]]),
values=tensor([12, 21, 33]),
shape=(3, 4), nnz=3)
>>> indices = torch.tensor([[1, 0, 2], [0, 3, 2]])
>>> val = torch.tensor([[10, 20], [30, 40], [50, 60]])
>>> A = dglsp.spmatrix(indices, val, shape=(3, 4))
>>> v = torch.tensor([1, 2, 3]).view(-1, 1)
>>> dglsp.sp_broadcast_v(A, v, "sub")
SparseMatrix(indices=tensor([[1, 0, 2],
[0, 3, 2]]),
values=tensor([[ 8, 18],
[29, 39],
[47, 57]]),
shape=(3, 4), nnz=3, val_size=(2,))
"""
op = getattr(operator, op)
if v.dim() == 1:
v = v.view(1, -1)
shape_error_message = (
f"Dimension mismatch for broadcasting. Got A.shape = {A.shape} and"
f"v.shape = {v.shape}."
)
assert v.dim() <= 2 and (1 in v.shape), shape_error_message
broadcast_dim = None
# v can be broadcasted to A if exactly one dimension of v is 1 and the other
# is the same as A.
for d, (dim1, dim2) in enumerate(zip(A.shape, v.shape)):
assert dim2 in (1, dim1), shape_error_message
if dim1 != dim2:
assert broadcast_dim is None, shape_error_message
broadcast_dim = d
# A and v has the same shape of (1, *) or (*, 1).
if broadcast_dim is None:
broadcast_dim = 0 if A.shape[0] == 1 else 1
if broadcast_dim == 0:
v = v.view(-1)[A.col]
else:
v = v.view(-1)[A.row]
if A.val.dim() > 1:
v = v.view(-1, 1)
ret_val = op(A.val, v)
return val_like(A, ret_val)
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def sp_add_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix:
"""Broadcast addition for sparse matrix and vector.
See the definition of :func:`sp_broadcast_v` for details.
"""
return sp_broadcast_v(A, v, "add")
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def sp_sub_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix:
"""Broadcast substraction for sparse matrix and vector.
See the definition of :func:`sp_broadcast_v` for details.
"""
return sp_broadcast_v(A, v, "sub")
[docs]
def sp_mul_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix:
"""Broadcast multiply for sparse matrix and vector.
See the definition of :func:`sp_broadcast_v` for details.
"""
return sp_broadcast_v(A, v, "mul")
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def sp_div_v(A: SparseMatrix, v: torch.Tensor) -> SparseMatrix:
"""Broadcast division for sparse matrix and vector.
See the definition of :func:`sp_broadcast_v` for details.
"""
return sp_broadcast_v(A, v, "truediv")