fix for unweighted graph partition

csrc/cpu/metis_cpu.cpp

@@ -7,7 +7,7 @@
 #include "utils.h"
 
 torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,int64_t num_parts,
-                            torch::Tensor edge_wgt,  bool recursive) {
+                            torch::optional<torch::Tensor> edge_wgt,  bool recursive) {
 #ifdef WITH_METIS
   CHECK_CPU(rowptr);
   CHECK_CPU(col);
@@ -17,7 +17,12 @@ torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,int64_t num_
 
   auto *xadj = rowptr.data_ptr<int64_t>();
   auto *adjncy = col.data_ptr<int64_t>();
+  if (edge_wgt==nullptr){
+    adjwgt = nullptr
+  }else{
     auto *adjwgt = edge_wgt.data_ptr<int64_t>();
+  }
+
   int64_t ncon = 1;
   int64_t objval = -1;
   auto part_data = part.data_ptr<int64_t>();

csrc/cpu/metis_cpu.h

@@ -3,4 +3,5 @@
 #include <torch/extension.h>
 
 torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
-                            int64_t num_parts,torch::Tensor adjwgt, bool recursive);
+                            int64_t num_parts,torch::optional<torch::Tensor> edge_wgt,
+                            bool recursive);

csrc/metis.cpp

 #include <Python.h>
 #include <torch/script.h>
-
 #include "cpu/metis_cpu.h"
 
 #ifdef _WIN32
@@ -8,7 +7,7 @@ PyMODINIT_FUNC PyInit__metis(void) { return NULL; }
 #endif
 
 torch::Tensor partition(torch::Tensor rowptr, torch::Tensor col,int64_t num_parts,
-                        torch::Tensor edge_wgt,bool recursive) {
+                        torch::optional<torch::Tensor> edge_wgt,bool recursive) {
   if (rowptr.device().is_cuda()) {
 #ifdef WITH_CUDA
     AT_ERROR("No CUDA version supported");

torch_sparse/metis.py

@@ -7,6 +7,8 @@ from torch_sparse.utils import cartesian1d
 
 
 def metis_wgt(x):
+    if len(x.unique()) == 1:
+        return None
     t1, t2 = cartesian1d(x, x)
     diff = t1 - t2
     diff = diff[diff != 0]
@@ -15,14 +17,14 @@ def metis_wgt(x):
     scale = (res / bod).item()
     tick, arange = scale.as_integer_ratio()
     x_ratio = (x - x.min()) / bod
-    return (x_ratio * arange + tick).long(), tick, arange
+    return (x_ratio * arange + tick).long()
 
 
 def partition(src: SparseTensor, num_parts: int, recursive: bool = False
               ) -> Tuple[SparseTensor, torch.Tensor, torch.Tensor]:
     rowptr, col = src.storage.rowptr().cpu(), src.storage.col().cpu()
     edge_wgt = src.storage.value().cpu()
-    edge_wgt = metis_wgt(edge_wgt)[0]
+    edge_wgt = metis_wgt(edge_wgt)
     cluster = torch.ops.torch_sparse.partition(rowptr, col, num_parts, edge_wgt,
                                                recursive)
     cluster = cluster.to(src.device())