fix scatter to handle empty graphs

python/cugraph-equivariant/cugraph_equivariant/tests/conftest.py

@@ -29,3 +29,11 @@ def example_scatter_data():
     }
 
     return src_feat, dst_indices, results
+
+
+@pytest.fixture
+def empty_scatter_data():
+    src_feat = torch.empty((0, 41))
+    dst_indices = torch.empty((0,))
+
+    return src_feat, dst_indices

python/cugraph-equivariant/cugraph_equivariant/tests/test_scatter.py

@@ -18,11 +18,23 @@
 
 @pytest.mark.parametrize("reduce", ["sum", "mean", "prod", "amax", "amin"])
 def test_scatter_reduce(example_scatter_data, reduce):
-    device = torch.device("cuda:0")
+    device = torch.device("cuda")
     src, index, out_true = example_scatter_data
     src = src.to(device)
     index = index.to(device)
 
     out = scatter_reduce(src, index, dim=0, dim_size=None, reduce=reduce)
 
     assert torch.allclose(out.cpu(), out_true[reduce])
+
+
+def test_scatter_reduce_empty(empty_scatter_data):
+    device = torch.device("cuda")
+    src, index = empty_scatter_data
+    src = src.to(device)
+    index = index.to(device)
+
+    out = scatter_reduce(src, index, dim=0, dim_size=None)
+
+    assert out.numel() == 0
+    assert out.size(1) == src.size(1)

python/cugraph-equivariant/cugraph_equivariant/utils/scatter.py

@@ -34,10 +34,9 @@ def scatter_reduce(
     size = list(src.size())
 
     if dim_size is not None:
-        assert dim_size >= int(index.max()) + 1
         size[dim] = dim_size
     else:
-        size[dim] = int(index.max()) + 1
+        size[dim] = 0 if index.numel() == 0 else int(index.max()) + 1
 
     out = torch.zeros(size, dtype=src.dtype, device=src.device)
     return out.scatter_reduce_(dim, index, src, reduce, include_self=False)