import typing
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric.typing import SparseTensor
from torch_geometric.utils import coalesce, cumsum
[docs]def dense_to_sparse(
adj: Tensor,
mask: Optional[Tensor] = None,
) -> Tuple[Tensor, Tensor]:
r"""Converts a dense adjacency matrix to a sparse adjacency matrix defined
by edge indices and edge attributes.
Args:
adj (torch.Tensor): The dense adjacency matrix of shape
:obj:`[num_nodes, num_nodes]` or
:obj:`[batch_size, num_nodes, num_nodes]`.
mask (torch.Tensor, optional): A boolean tensor of shape
:obj:`[batch_size, num_nodes]` holding information about which
nodes are in each example are valid. (default: :obj:`None`)
:rtype: (:class:`LongTensor`, :class:`Tensor`)
Examples:
>>> # For a single adjacency matrix:
>>> adj = torch.tensor([[3, 1],
... [2, 0]])
>>> dense_to_sparse(adj)
(tensor([[0, 0, 1],
[0, 1, 0]]),
tensor([3, 1, 2]))
>>> # For two adjacency matrixes:
>>> adj = torch.tensor([[[3, 1],
... [2, 0]],
... [[0, 1],
... [0, 2]]])
>>> dense_to_sparse(adj)
(tensor([[0, 0, 1, 2, 3],
[0, 1, 0, 3, 3]]),
tensor([3, 1, 2, 1, 2]))
>>> # First graph with two nodes, second with three:
>>> adj = torch.tensor([[
... [3, 1, 0],
... [2, 0, 0],
... [0, 0, 0]
... ], [
... [0, 1, 0],
... [0, 2, 3],
... [0, 5, 0]
... ]])
>>> mask = torch.tensor([
... [True, True, False],
... [True, True, True]
... ])
>>> dense_to_sparse(adj, mask)
(tensor([[0, 0, 1, 2, 3, 3, 4],
[0, 1, 0, 3, 3, 4, 3]]),
tensor([3, 1, 2, 1, 2, 3, 5]))
"""
if adj.dim() < 2 or adj.dim() > 3:
raise ValueError(f"Dense adjacency matrix 'adj' must be two- or "
f"three-dimensional (got {adj.dim()} dimensions)")
if mask is not None and adj.dim() == 2:
warnings.warn("Mask should not be provided in case the dense "
"adjacency matrix is two-dimensional")
mask = None
if mask is not None and mask.dim() != 2:
raise ValueError(f"Mask must be two-dimensional "
f"(got {mask.dim()} dimensions)")
if mask is not None and adj.size(-2) != adj.size(-1):
raise ValueError(f"Mask is only supported on quadratic adjacency "
f"matrices (got [*, {adj.size(-2)}, {adj.size(-1)}])")
if adj.dim() == 2:
edge_index = adj.nonzero().t()
edge_attr = adj[edge_index[0], edge_index[1]]
return edge_index, edge_attr
else:
flatten_adj = adj.view(-1, adj.size(-1))
if mask is not None:
flatten_adj = flatten_adj[mask.view(-1)]
edge_index = flatten_adj.nonzero().t()
edge_attr = flatten_adj[edge_index[0], edge_index[1]]
if mask is None:
offset = torch.arange(
start=0,
end=adj.size(0) * adj.size(2),
step=adj.size(2),
device=adj.device,
)
offset = offset.repeat_interleave(adj.size(1))
else:
count = mask.sum(dim=-1)
offset = cumsum(count)[:-1]
offset = offset.repeat_interleave(count)
edge_index[1] += offset[edge_index[0]]
return edge_index, edge_attr
[docs]def is_torch_sparse_tensor(src: Any) -> bool:
r"""Returns :obj:`True` if the input :obj:`src` is a
:class:`torch.sparse.Tensor` (in any sparse layout).
Args:
src (Any): The input object to be checked.
"""
if isinstance(src, Tensor):
if src.layout == torch.sparse_coo:
return True
if src.layout == torch.sparse_csr:
return True
if (torch_geometric.typing.WITH_PT112
and src.layout == torch.sparse_csc):
return True
return False
[docs]def is_sparse(src: Any) -> bool:
r"""Returns :obj:`True` if the input :obj:`src` is of type
:class:`torch.sparse.Tensor` (in any sparse layout) or of type
:class:`torch_sparse.SparseTensor`.
Args:
src (Any): The input object to be checked.
"""
return is_torch_sparse_tensor(src) or isinstance(src, SparseTensor)
[docs]def to_torch_coo_tensor(
edge_index: Tensor,
edge_attr: Optional[Tensor] = None,
size: Optional[Union[int, Tuple[Optional[int], Optional[int]]]] = None,
is_coalesced: bool = False,
) -> Tensor:
r"""Converts a sparse adjacency matrix defined by edge indices and edge
attributes to a :class:`torch.sparse.Tensor` with layout
`torch.sparse_coo`.
See :meth:`~torch_geometric.utils.to_edge_index` for the reverse operation.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): The edge attributes.
(default: :obj:`None`)
size (int or (int, int), optional): The size of the sparse matrix.
If given as an integer, will create a quadratic sparse matrix.
If set to :obj:`None`, will infer a quadratic sparse matrix based
on :obj:`edge_index.max() + 1`. (default: :obj:`None`)
is_coalesced (bool): If set to :obj:`True`, will assume that
:obj:`edge_index` is already coalesced and thus avoids expensive
computation. (default: :obj:`False`)
:rtype: :class:`torch.sparse.Tensor`
Example:
>>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],
... [1, 0, 2, 1, 3, 2]])
>>> to_torch_coo_tensor(edge_index)
tensor(indices=tensor([[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2]]),
values=tensor([1., 1., 1., 1., 1., 1.]),
size=(4, 4), nnz=6, layout=torch.sparse_coo)
"""
if size is None:
size = int(edge_index.max()) + 1
if isinstance(size, (tuple, list)):
num_src_nodes, num_dst_nodes = size
if num_src_nodes is None:
num_src_nodes = int(edge_index[0].max()) + 1
if num_dst_nodes is None:
num_dst_nodes = int(edge_index[1].max()) + 1
size = (num_src_nodes, num_dst_nodes)
else:
size = (size, size)
if not is_coalesced:
edge_index, edge_attr = coalesce(edge_index, edge_attr, max(size))
if edge_attr is None:
# Expanded tensors are not yet supported in all PyTorch code paths :(
# edge_attr = torch.ones(1, device=edge_index.device)
# edge_attr = edge_attr.expand(edge_index.size(1))
edge_attr = torch.ones(edge_index.size(1), device=edge_index.device)
adj = torch.sparse_coo_tensor(
indices=edge_index,
values=edge_attr,
size=tuple(size) + edge_attr.size()[1:],
device=edge_index.device,
)
adj = adj._coalesced_(True)
return adj
[docs]def to_torch_csr_tensor(
edge_index: Tensor,
edge_attr: Optional[Tensor] = None,
size: Optional[Union[int, Tuple[Optional[int], Optional[int]]]] = None,
is_coalesced: bool = False,
) -> Tensor:
r"""Converts a sparse adjacency matrix defined by edge indices and edge
attributes to a :class:`torch.sparse.Tensor` with layout
`torch.sparse_csr`.
See :meth:`~torch_geometric.utils.to_edge_index` for the reverse operation.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): The edge attributes.
(default: :obj:`None`)
size (int or (int, int), optional): The size of the sparse matrix.
If given as an integer, will create a quadratic sparse matrix.
If set to :obj:`None`, will infer a quadratic sparse matrix based
on :obj:`edge_index.max() + 1`. (default: :obj:`None`)
is_coalesced (bool): If set to :obj:`True`, will assume that
:obj:`edge_index` is already coalesced and thus avoids expensive
computation. (default: :obj:`False`)
:rtype: :class:`torch.sparse.Tensor`
Example:
>>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],
... [1, 0, 2, 1, 3, 2]])
>>> to_torch_csr_tensor(edge_index)
tensor(crow_indices=tensor([0, 1, 3, 5, 6]),
col_indices=tensor([1, 0, 2, 1, 3, 2]),
values=tensor([1., 1., 1., 1., 1., 1.]),
size=(4, 4), nnz=6, layout=torch.sparse_csr)
"""
if size is None:
size = int(edge_index.max()) + 1
if isinstance(size, (tuple, list)):
num_src_nodes, num_dst_nodes = size
if num_src_nodes is None:
num_src_nodes = int(edge_index[0].max()) + 1
if num_dst_nodes is None:
num_dst_nodes = int(edge_index[1].max()) + 1
size = (num_src_nodes, num_dst_nodes)
else:
size = (size, size)
if not is_coalesced:
edge_index, edge_attr = coalesce(edge_index, edge_attr, max(size))
if edge_attr is None:
# Expanded tensors are not yet supported in all PyTorch code paths :(
# edge_attr = torch.ones(1, device=edge_index.device)
# edge_attr = edge_attr.expand(edge_index.size(1))
edge_attr = torch.ones(edge_index.size(1), device=edge_index.device)
adj = torch.sparse_csr_tensor(
crow_indices=index2ptr(edge_index[0], size[0]),
col_indices=edge_index[1],
values=edge_attr,
size=tuple(size) + edge_attr.size()[1:],
device=edge_index.device,
)
return adj
[docs]def to_torch_csc_tensor(
edge_index: Tensor,
edge_attr: Optional[Tensor] = None,
size: Optional[Union[int, Tuple[Optional[int], Optional[int]]]] = None,
is_coalesced: bool = False,
) -> Tensor:
r"""Converts a sparse adjacency matrix defined by edge indices and edge
attributes to a :class:`torch.sparse.Tensor` with layout
`torch.sparse_csc`.
See :meth:`~torch_geometric.utils.to_edge_index` for the reverse operation.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): The edge attributes.
(default: :obj:`None`)
size (int or (int, int), optional): The size of the sparse matrix.
If given as an integer, will create a quadratic sparse matrix.
If set to :obj:`None`, will infer a quadratic sparse matrix based
on :obj:`edge_index.max() + 1`. (default: :obj:`None`)
is_coalesced (bool): If set to :obj:`True`, will assume that
:obj:`edge_index` is already coalesced and thus avoids expensive
computation. (default: :obj:`False`)
:rtype: :class:`torch.sparse.Tensor`
Example:
>>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],
... [1, 0, 2, 1, 3, 2]])
>>> to_torch_csc_tensor(edge_index)
tensor(ccol_indices=tensor([0, 1, 3, 5, 6]),
row_indices=tensor([1, 0, 2, 1, 3, 2]),
values=tensor([1., 1., 1., 1., 1., 1.]),
size=(4, 4), nnz=6, layout=torch.sparse_csc)
"""
if not torch_geometric.typing.WITH_PT112:
if typing.TYPE_CHECKING:
raise NotImplementedError
return torch_geometric.typing.MockTorchCSCTensor(
edge_index, edge_attr, size)
if size is None:
size = int(edge_index.max()) + 1
if isinstance(size, (tuple, list)):
num_src_nodes, num_dst_nodes = size
if num_src_nodes is None:
num_src_nodes = int(edge_index[0].max()) + 1
if num_dst_nodes is None:
num_dst_nodes = int(edge_index[1].max()) + 1
size = (num_src_nodes, num_dst_nodes)
else:
size = (size, size)
if not is_coalesced:
edge_index, edge_attr = coalesce(edge_index, edge_attr, max(size),
sort_by_row=False)
if edge_attr is None:
# Expanded tensors are not yet supported in all PyTorch code paths :(
# edge_attr = torch.ones(1, device=edge_index.device)
# edge_attr = edge_attr.expand(edge_index.size(1))
edge_attr = torch.ones(edge_index.size(1), device=edge_index.device)
adj = torch.sparse_csc_tensor(
ccol_indices=index2ptr(edge_index[1], size[1]),
row_indices=edge_index[0],
values=edge_attr,
size=tuple(size) + edge_attr.size()[1:],
device=edge_index.device,
)
return adj
[docs]def to_torch_sparse_tensor(
edge_index: Tensor,
edge_attr: Optional[Tensor] = None,
size: Optional[Union[int, Tuple[Optional[int], Optional[int]]]] = None,
is_coalesced: bool = False,
layout: torch.layout = torch.sparse_coo,
) -> Tensor:
r"""Converts a sparse adjacency matrix defined by edge indices and edge
attributes to a :class:`torch.sparse.Tensor` with custom :obj:`layout`.
See :meth:`~torch_geometric.utils.to_edge_index` for the reverse operation.
Args:
edge_index (LongTensor): The edge indices.
edge_attr (Tensor, optional): The edge attributes.
(default: :obj:`None`)
size (int or (int, int), optional): The size of the sparse matrix.
If given as an integer, will create a quadratic sparse matrix.
If set to :obj:`None`, will infer a quadratic sparse matrix based
on :obj:`edge_index.max() + 1`. (default: :obj:`None`)
is_coalesced (bool): If set to :obj:`True`, will assume that
:obj:`edge_index` is already coalesced and thus avoids expensive
computation. (default: :obj:`False`)
layout (torch.layout, optional): The layout of the output sparse tensor
(:obj:`torch.sparse_coo`, :obj:`torch.sparse_csr`,
:obj:`torch.sparse_csc`). (default: :obj:`torch.sparse_coo`)
:rtype: :class:`torch.sparse.Tensor`
"""
if layout == torch.sparse_coo:
return to_torch_coo_tensor(edge_index, edge_attr, size, is_coalesced)
if layout == torch.sparse_csr:
return to_torch_csr_tensor(edge_index, edge_attr, size, is_coalesced)
if torch_geometric.typing.WITH_PT112 and layout == torch.sparse_csc:
return to_torch_csc_tensor(edge_index, edge_attr, size, is_coalesced)
raise ValueError(f"Unexpected sparse tensor layout (got '{layout}')")
[docs]def to_edge_index(adj: Union[Tensor, SparseTensor]) -> Tuple[Tensor, Tensor]:
r"""Converts a :class:`torch.sparse.Tensor` or a
:class:`torch_sparse.SparseTensor` to edge indices and edge attributes.
Args:
adj (torch.sparse.Tensor or SparseTensor): The adjacency matrix.
:rtype: (:class:`torch.Tensor`, :class:`torch.Tensor`)
Example:
>>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],
... [1, 0, 2, 1, 3, 2]])
>>> adj = to_torch_coo_tensor(edge_index)
>>> to_edge_index(adj)
(tensor([[0, 1, 1, 2, 2, 3],
[1, 0, 2, 1, 3, 2]]),
tensor([1., 1., 1., 1., 1., 1.]))
"""
if isinstance(adj, SparseTensor):
row, col, value = adj.coo()
if value is None:
value = torch.ones(row.size(0), device=row.device)
return torch.stack([row, col], dim=0).long(), value
if adj.layout == torch.sparse_coo:
adj = adj._coalesced_(True)
return adj.indices().detach().long(), adj.values()
if adj.layout == torch.sparse_csr:
row = ptr2index(adj.crow_indices().detach())
col = adj.col_indices().detach()
return torch.stack([row, col], dim=0).long(), adj.values()
if torch_geometric.typing.WITH_PT112 and adj.layout == torch.sparse_csc:
col = ptr2index(adj.ccol_indices().detach())
row = adj.row_indices().detach()
return torch.stack([row, col], dim=0).long(), adj.values()
raise ValueError(f"Unexpected sparse tensor layout (got '{adj.layout}')")
# Helper functions ############################################################
def get_sparse_diag(
size: int,
fill_value: float = 1.0,
layout: Optional[int] = None,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
) -> Tensor:
return torch.sparse.spdiags(
torch.full((1, size), fill_value, dtype=dtype, device=device),
offsets=torch.zeros(1, dtype=torch.long, device=device),
shape=(size, size),
layout=layout,
)
def set_sparse_value(adj: Tensor, value: Tensor) -> Tensor:
if value.dim() > 1:
size = adj.size() + value.size()[1:]
else:
size = adj.size()
if adj.layout == torch.sparse_coo:
return torch.sparse_coo_tensor(
indices=adj.indices(),
values=value,
size=size,
device=value.device,
).coalesce()
if adj.layout == torch.sparse_csr:
return torch.sparse_csr_tensor(
crow_indices=adj.crow_indices(),
col_indices=adj.col_indices(),
values=value,
size=size,
device=value.device,
)
if torch_geometric.typing.WITH_PT112 and adj.layout == torch.sparse_csc:
return torch.sparse_csc_tensor(
ccol_indices=adj.ccol_indices(),
row_indices=adj.row_indices(),
values=value,
size=size,
device=value.device,
)
raise ValueError(f"Unexpected sparse tensor layout (got '{adj.layout}')")
def ptr2index(ptr: Tensor, output_size: Optional[int] = None) -> Tensor:
index = torch.arange(ptr.numel() - 1, dtype=ptr.dtype, device=ptr.device)
return index.repeat_interleave(ptr.diff(), output_size=output_size)
def index2ptr(index: Tensor, size: Optional[int] = None) -> Tensor:
if size is None:
size = int(index.max()) + 1 if index.numel() > 0 else 0
return torch._convert_indices_from_coo_to_csr(
index, size, out_int32=index.dtype == torch.int32)
def cat_coo(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> Tensor:
assert dim in {0, 1, (0, 1)}
assert tensors[0].layout == torch.sparse_coo
indices, values = [], []
num_rows = num_cols = 0
is_coalesced = True
if dim == 0:
for i, tensor in enumerate(tensors):
if i == 0:
indices.append(tensor._indices())
else:
offset = torch.tensor([[num_rows], [0]], device=tensor.device)
indices.append(tensor._indices() + offset)
values.append(tensor._values())
num_rows += tensor.size(0)
num_cols = max(num_cols, tensor.size(1))
if not tensor.is_coalesced():
is_coalesced = False
elif dim == 1:
for i, tensor in enumerate(tensors):
if i == 0:
indices.append(tensor._indices())
else:
offset = torch.tensor([[0], [num_cols]], device=tensor.device)
indices.append(tensor.indices() + offset)
values.append(tensor._values())
num_rows = max(num_rows, tensor.size(0))
num_cols += tensor.size(1)
is_coalesced = False
else:
for i, tensor in enumerate(tensors):
if i == 0:
indices.append(tensor._indices())
else:
offset = torch.tensor([[num_rows], [num_cols]],
device=tensor.device)
indices.append(tensor._indices() + offset)
values.append(tensor._values())
num_rows += tensor.size(0)
num_cols += tensor.size(1)
if not tensor.is_coalesced():
is_coalesced = False
out = torch.sparse_coo_tensor(
indices=torch.cat(indices, dim=-1),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
if is_coalesced:
out = out._coalesced_(True)
return out
def cat_csr(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> Tensor:
assert dim in {0, 1, (0, 1)}
assert tensors[0].layout == torch.sparse_csr
rows, cols, values = [], [], []
num_rows = num_cols = nnz = 0
if dim == 0:
for i, tensor in enumerate(tensors):
if i == 0:
rows.append(tensor.crow_indices())
else:
rows.append(tensor.crow_indices()[1:] + nnz)
cols.append(tensor.col_indices())
values.append(tensor.values())
num_rows += tensor.size(0)
num_cols = max(num_cols, tensor.size(1))
nnz += cols[-1].numel()
return torch.sparse_csr_tensor(
crow_indices=torch.cat(rows),
col_indices=torch.cat(cols),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
elif dim == 1:
for i, tensor in enumerate(tensors):
rows.append(ptr2index(tensor.crow_indices()))
if i == 0:
cols.append(tensor.col_indices())
else:
cols.append(tensor.col_indices() + num_cols)
values.append(tensor.values())
num_rows = max(num_rows, tensor.size(0))
num_cols += tensor.size(1)
return torch.sparse_coo_tensor(
indices=torch.stack((torch.cat(rows), torch.cat(cols)), 0),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
else:
for i, tensor in enumerate(tensors):
if i == 0:
rows.append(tensor.crow_indices())
cols.append(tensor.col_indices())
else:
rows.append(tensor.crow_indices()[1:] + nnz)
cols.append(tensor.col_indices() + num_cols)
values.append(tensor.values())
num_rows += tensor.size(0)
num_cols += tensor.size(1)
nnz += cols[-1].numel()
return torch.sparse_csr_tensor(
crow_indices=torch.cat(rows),
col_indices=torch.cat(cols),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
def cat_csc(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> Tensor:
assert dim in {0, 1, (0, 1)}
assert tensors[0].layout == torch.sparse_csc
rows, cols, values = [], [], []
num_rows = num_cols = nnz = 0
if dim == 0:
for i, tensor in enumerate(tensors):
cols.append(ptr2index(tensor.ccol_indices()))
if i == 0:
rows.append(tensor.row_indices())
else:
rows.append(tensor.row_indices() + num_rows)
values.append(tensor.values())
num_rows += tensor.size(0)
num_cols = max(num_cols, tensor.size(1))
return torch.sparse_coo_tensor(
indices=torch.stack((torch.cat(rows), torch.cat(cols)), 0),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
elif dim == 1:
for i, tensor in enumerate(tensors):
if i == 0:
cols.append(tensor.ccol_indices())
else:
cols.append(tensor.ccol_indices()[1:] + nnz)
rows.append(tensor.row_indices())
values.append(tensor.values())
num_rows = max(num_rows, tensor.size(0))
num_cols += tensor.size(1)
nnz += rows[-1].numel()
return torch.sparse_csc_tensor(
row_indices=torch.cat(rows),
ccol_indices=torch.cat(cols),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
else:
for i, tensor in enumerate(tensors):
if i == 0:
rows.append(tensor.row_indices())
cols.append(tensor.ccol_indices())
else:
rows.append(tensor.row_indices() + num_rows)
cols.append(tensor.ccol_indices()[1:] + nnz)
values.append(tensor.values())
num_rows += tensor.size(0)
num_cols += tensor.size(1)
nnz += rows[-1].numel()
return torch.sparse_csc_tensor(
row_indices=torch.cat(rows),
ccol_indices=torch.cat(cols),
values=torch.cat(values),
size=(num_rows, num_cols) + values[-1].size()[1:],
device=tensor.device,
)
def cat(tensors: List[Tensor], dim: Union[int, Tuple[int, int]]) -> Tensor:
assert is_torch_sparse_tensor(tensors[0])
if tensors[0].layout == torch.sparse_coo:
return cat_coo(tensors, dim)
elif tensors[0].layout == torch.sparse_csr:
return cat_csr(tensors, dim)
else:
return cat_csc(tensors, dim)