add node weights for metis wrapper

csrc/cpu/metis_cpu.cpp

@@ -12,6 +12,7 @@
 
 torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
                             torch::optional<torch::Tensor> optional_value,
+                            torch::optional<torch::Tensor> vweights,
                             int64_t num_parts, bool recursive) {
 #ifdef WITH_METIS
   CHECK_CPU(rowptr);
@@ -22,6 +23,12 @@ torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
     CHECK_INPUT(optional_value.value().numel() == col.numel());
   }
 
+  if (vweights.has_value()) {
+    CHECK_CPU(vweights.value());
+    CHECK_INPUT(vweights.value().dim() == 1);
+    CHECK_INPUT(vweights.value().numel() == rowptr.numel() - 1);
+  }
+
   int64_t nvtxs = rowptr.numel() - 1;
   int64_t ncon = 1;
   auto *xadj = rowptr.data_ptr<int64_t>();
@@ -29,15 +36,20 @@ torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
   int64_t *adjwgt = NULL;
   if (optional_value.has_value())
     adjwgt = optional_value.value().data_ptr<int64_t>();
+
+  int64_t *vwgt = NULL;
+  if (vweights.has_value())
+    vwgt = vweights.value().data_ptr<int64_t>();
+
   int64_t objval = -1;
   auto part = torch::empty(nvtxs, rowptr.options());
   auto part_data = part.data_ptr<int64_t>();
 
   if (recursive) {
-    METIS_PartGraphRecursive(&nvtxs, &ncon, xadj, adjncy, NULL, NULL, adjwgt,
+    METIS_PartGraphRecursive(&nvtxs, &ncon, xadj, adjncy, vwgt, NULL, adjwgt,
                              &num_parts, NULL, NULL, NULL, &objval, part_data);
   } else {
-    METIS_PartGraphKway(&nvtxs, &ncon, xadj, adjncy, NULL, NULL, adjwgt,
+    METIS_PartGraphKway(&nvtxs, &ncon, xadj, adjncy, vwgt, NULL, adjwgt,
                         &num_parts, NULL, NULL, NULL, &objval, part_data);
   }
 
@@ -52,6 +64,7 @@ torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
 //             --partitions64bit
 torch::Tensor mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
                                torch::optional<torch::Tensor> optional_value,
+                               torch::optional<torch::Tensor> vweights,
                                int64_t num_parts, bool recursive,
                                int64_t num_workers) {
 #ifdef WITH_MTMETIS
@@ -63,6 +76,12 @@ torch::Tensor mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
     CHECK_INPUT(optional_value.value().numel() == col.numel());
   }
 
+  if (vweights.has_value()) {
+    CHECK_CPU(vweights.value());
+    CHECK_INPUT(vweights.value().dim() == 1);
+    CHECK_INPUT(vweights.value().numel() == rowptr.numel() - 1);
+  }
+
   mtmetis_vtx_type nvtxs = rowptr.numel() - 1;
   mtmetis_vtx_type ncon = 1;
   mtmetis_adj_type *xadj = (mtmetis_adj_type *)rowptr.data_ptr<int64_t>();
@@ -70,6 +89,9 @@ torch::Tensor mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
   mtmetis_wgt_type *adjwgt = NULL;
   if (optional_value.has_value())
     adjwgt = optional_value.value().data_ptr<int64_t>();
+  mtmetis_wgt_type *vwgt = NULL;
+  if (vweights.has_value())
+    vwgt = vweights.value().data_ptr<int64_t>();
   mtmetis_pid_type nparts = num_parts;
   mtmetis_wgt_type objval = -1;
   auto part = torch::empty(nvtxs, rowptr.options());
@@ -79,10 +101,10 @@ torch::Tensor mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
   opts[MTMETIS_OPTION_NTHREADS] = num_workers;
 
   if (recursive) {
-    MTMETIS_PartGraphRecursive(&nvtxs, &ncon, xadj, adjncy, NULL, NULL, adjwgt,
+    MTMETIS_PartGraphRecursive(&nvtxs, &ncon, xadj, adjncy, vwgt, NULL, adjwgt,
                                &nparts, NULL, NULL, opts, &objval, part_data);
   } else {
-    MTMETIS_PartGraphKway(&nvtxs, &ncon, xadj, adjncy, NULL, NULL, adjwgt,
+    MTMETIS_PartGraphKway(&nvtxs, &ncon, xadj, adjncy, vwgt, NULL, adjwgt,
                           &nparts, NULL, NULL, opts, &objval, part_data);
   }
 

csrc/cpu/metis_cpu.h

@@ -4,9 +4,11 @@
 
 torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
                             torch::optional<torch::Tensor> optional_value,
+                            torch::optional<torch::Tensor> vweights,
                             int64_t num_parts, bool recursive);
 
 torch::Tensor mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
                                torch::optional<torch::Tensor> optional_value,
+                               torch::optional<torch::Tensor> vweights,
                                int64_t num_parts, bool recursive,
                                int64_t num_workers);

csrc/metis.cpp

@@ -13,6 +13,7 @@ PyMODINIT_FUNC PyInit__metis_cpu(void) { return NULL; }
 
 torch::Tensor partition(torch::Tensor rowptr, torch::Tensor col,
                         torch::optional<torch::Tensor> optional_value,
+                        torch::optional<torch::Tensor> vweights,
                         int64_t num_parts, bool recursive) {
   if (rowptr.device().is_cuda()) {
 #ifdef WITH_CUDA
@@ -21,12 +22,13 @@ torch::Tensor partition(torch::Tensor rowptr, torch::Tensor col,
     AT_ERROR("Not compiled with CUDA support");
 #endif
   } else {
-    return partition_cpu(rowptr, col, optional_value, num_parts, recursive);
+    return partition_cpu(rowptr, col, optional_value, vweights, num_parts, recursive);
   }
 }
 
 torch::Tensor mt_partition(torch::Tensor rowptr, torch::Tensor col,
                            torch::optional<torch::Tensor> optional_value,
+                           torch::optional<torch::Tensor> vweights,
                            int64_t num_parts, bool recursive,
                            int64_t num_workers) {
   if (rowptr.device().is_cuda()) {
@@ -36,7 +38,7 @@ torch::Tensor mt_partition(torch::Tensor rowptr, torch::Tensor col,
     AT_ERROR("Not compiled with CUDA support");
 #endif
   } else {
-    return mt_partition_cpu(rowptr, col, optional_value, num_parts, recursive,
+    return mt_partition_cpu(rowptr, col, optional_value, vweights, num_parts, recursive,
                             num_workers);
   }
 }

test/test_metis.py

@@ -18,20 +18,26 @@ def test_metis(device):
     value2 = torch.arange(6 * 6, dtype=torch.long, device=device).view(6, 6)
     value3 = torch.ones(6 * 6, device=device).view(6, 6)
 
+    vwgts = torch.rand(6, device=device)
+
     for value in [value1, value2, value3]:
-        mat = SparseTensor.from_dense(value)
-
-        _, partptr, perm = mat.partition(num_parts=2, recursive=False,
-                                         weighted=True)
-        assert partptr.numel() == 3
-        assert perm.numel() == 6
-
-        _, partptr, perm = mat.partition(num_parts=2, recursive=False,
-                                         weighted=False)
-        assert partptr.numel() == 3
-        assert perm.numel() == 6
-
-        _, partptr, perm = mat.partition(num_parts=1, recursive=False,
-                                         weighted=True)
-        assert partptr.numel() == 2
-        assert perm.numel() == 6
+        for vwgt in [None, vwgts]:
+            mat = SparseTensor.from_dense(value)
+
+            _, partptr, perm = mat.partition(num_parts=2, recursive=False,
+                                             vweights=vwgt,
+                                             weighted=True)
+            assert partptr.numel() == 3
+            assert perm.numel() == 6
+
+            _, partptr, perm = mat.partition(num_parts=2, recursive=False,
+                                             vweights=vwgt,
+                                             weighted=False)
+            assert partptr.numel() == 3
+            assert perm.numel() == 6
+
+            _, partptr, perm = mat.partition(num_parts=1, recursive=False,
+                                             vweights=vwgt,
+                                             weighted=True)
+            assert partptr.numel() == 2
+            assert perm.numel() == 6

torch_sparse/metis.py

@@ -21,6 +21,7 @@ def weight2metis(weight: torch.Tensor) -> Optional[torch.Tensor]:
 
 def partition(
         src: SparseTensor, num_parts: int, recursive: bool = False,
+        vweights: torch.tensor = None,
         weighted: bool = False
 ) -> Tuple[SparseTensor, torch.Tensor, torch.Tensor]:
 
@@ -41,7 +42,13 @@ def partition(
     else:
         value = None
 
-    cluster = torch.ops.torch_sparse.partition(rowptr, col, value, num_parts,
+    if vweights is not None:
+        assert vweights.numel() == rowptr.numel() - 1
+        vweights = vweights.view(-1).detach().cpu()
+        if vweights.is_floating_point():
+            vweights = weight2metis(vweights)
+
+    cluster = torch.ops.torch_sparse.partition(rowptr, col, value, vweights, num_parts,
                                                recursive)
     cluster = cluster.to(src.device())