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Graph partition based on balance_edge #309

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merged 3 commits into from
Feb 10, 2023
Merged

Graph partition based on balance_edge #309

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a4199c1
add balance_edge for graph partition
ZhengHongming888 Feb 10, 2023
2dd1f2a
add Tensor defition in metis.py
ZhengHongming888 Feb 10, 2023
ff21933
update
rusty1s Feb 10, 2023
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26 changes: 20 additions & 6 deletions torch_sparse/metis.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,13 @@
from typing import Tuple, Optional
from typing import Optional, Tuple

import torch
from torch_sparse.tensor import SparseTensor
from torch import Tensor

from torch_sparse.permute import permute
from torch_sparse.tensor import SparseTensor


def weight2metis(weight: torch.Tensor) -> Optional[torch.Tensor]:
def weight2metis(weight: Tensor) -> Optional[Tensor]:
sorted_weight = weight.sort()[0]
diff = sorted_weight[1:] - sorted_weight[:-1]
if diff.sum() == 0:
Expand All @@ -20,16 +22,24 @@ def weight2metis(weight: torch.Tensor) -> Optional[torch.Tensor]:


def partition(
src: SparseTensor, num_parts: int, recursive: bool = False,
weighted: bool = False, node_weight: Optional[torch.Tensor] = None
) -> Tuple[SparseTensor, torch.Tensor, torch.Tensor]:
src: SparseTensor,
num_parts: int,
recursive: bool = False,
weighted: bool = False,
node_weight: Optional[Tensor] = None,
balance_edge: bool = False,
) -> Tuple[SparseTensor, Tensor, Tensor]:

assert num_parts >= 1
if num_parts == 1:
partptr = torch.tensor([0, src.size(0)], device=src.device())
perm = torch.arange(src.size(0), device=src.device())
return src, partptr, perm

if balance_edge and node_weight:
raise ValueError("Cannot set 'balance_edge' and 'node_weight' at the "
"same time in 'torch_sparse.partition'")

rowptr, col, value = src.csr()
rowptr, col = rowptr.cpu(), col.cpu()

Expand All @@ -41,6 +51,10 @@ def partition(
else:
value = None

if balance_edge:
node_weight = col.new_zeros(col.size(0))
node_weight.scatter_add_(0, col, torch.ones_like(col))

if node_weight is not None:
assert node_weight.numel() == rowptr.numel() - 1
node_weight = node_weight.view(-1).detach().cpu()
Expand Down