cleanup

torch_sparse/metis.py

@@ -22,6 +22,13 @@ def weight2metis(weight: torch.Tensor) -> Optional[torch.Tensor]:
 def partition(src: SparseTensor, num_parts: int, recursive: bool = False,
               weighted=False
               ) -> Tuple[SparseTensor, torch.Tensor, torch.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
+
     rowptr, col, value = src.csr()
     rowptr, col = rowptr.cpu(), col.cpu()
 
@@ -33,13 +40,8 @@ def partition(src: SparseTensor, num_parts: int, recursive: bool = False,
     else:
         value = None
 
-    if num_parts > 1:
-        cluster = torch.ops.torch_sparse.partition(rowptr, col, value,
-                                                   num_parts, recursive)
-    elif num_parts == 1:
-        cluster = torch.zeros((src.size(0)), dtype=torch.long)
-    else:
-        raise ValueError
+    cluster = torch.ops.torch_sparse.partition(rowptr, col, value, num_parts,
+                                               recursive)
     cluster = cluster.to(src.device())
 
     cluster, perm = cluster.sort()