Clean Up CuGraph-ops

CHANGELOG.md

@@ -75,6 +75,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Added `edge_attr` in `CuGraphGATConv` ([#10383](https://github.com/pyg-team/pytorch_geometric/pull/10383))
 - Adapt `dgcnn_classification` example to work with `ModelNet` and `MedShapeNet` Datasets ([#9823](https://github.com/pyg-team/pytorch_geometric/pull/9823))
 - Chained exceptions explicitly instead of implicitly ([#10242](https://github.com/pyg-team/pytorch_geometric/pull/10242))
 - Updated cuGraph examples to use buffered sampling which keeps data in memory and is significantly faster than the deprecated buffered sampling ([#10079](https://github.com/pyg-team/pytorch_geometric/pull/10079))

docs/source/install/installation.rst

@@ -187,6 +187,13 @@ If :conda:`null` :obj:`conda` does not pick up the correct CUDA version of :pyg:
 
    conda install pyg=*=*cu* -c pyg
 
+Enabling Accelerated cuGraph GNNs
+---------------------------------
+
+Currently, NVIDIA recommends `NVIDIA PyG Container <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg/tags>_` to use cuGraph integration in PyG.
+This functionality is planned to be enabled through cuDNN which is part of PyTorch builds. We still recommend using the NVIDIA PyG Container regardless to have the fastest and most stable build of the NVIDIA CUDA stack combined with PyTorch and PyG.
+
+
 Frequently Asked Questions
 --------------------------
 

test/nn/conv/cugraph/test_cugraph_gat_conv.py

@@ -11,9 +11,11 @@
 @pytest.mark.parametrize('bias', [True, False])
 @pytest.mark.parametrize('bipartite', [True, False])
 @pytest.mark.parametrize('concat', [True, False])
+@pytest.mark.parametrize('edge_attr', [True, False])
 @pytest.mark.parametrize('heads', [1, 2, 3])
 @pytest.mark.parametrize('max_num_neighbors', [8, None])
-def test_gat_conv_equality(bias, bipartite, concat, heads, max_num_neighbors):
+def test_gat_conv_equality(bias, bipartite, concat, edge_attr, heads,
+                           max_num_neighbors):
     in_channels, out_channels = 5, 2
     kwargs = dict(bias=bias, concat=concat)
 
@@ -32,17 +34,27 @@ def test_gat_conv_equality(bias, bipartite, concat, heads, max_num_neighbors):
         conv2.lin.weight.data[:, :] = conv1.lin.weight.data
         conv2.att.data[:heads * out_channels] = conv1.att_src.data.flatten()
         conv2.att.data[heads * out_channels:] = conv1.att_dst.data.flatten()
+    if edge_attr and not bipartite:
+        e_attrs = torch.randn(size=(edge_index.size(1), 10))
+        out1 = conv1(x, edge_index, edge_attr=e_attrs)
 
-    if bipartite:
-        out1 = conv1((x, x[:size[1]]), edge_index)
+        out2 = conv2(
+            x,
+            EdgeIndex(edge_index, sparse_size=size),
+            max_num_neighbors=max_num_neighbors,
+            edge_attr=e_attrs,
+        )
     else:
-        out1 = conv1(x, edge_index)
+        if bipartite:
+            out1 = conv1((x, x[:size[1]]), edge_index)
+        else:
+            out1 = conv1(x, edge_index)
 
-    out2 = conv2(
-        x,
-        EdgeIndex(edge_index, sparse_size=size),
-        max_num_neighbors=max_num_neighbors,
-    )
+        out2 = conv2(
+            x,
+            EdgeIndex(edge_index, sparse_size=size),
+            max_num_neighbors=max_num_neighbors,
+        )
     assert torch.allclose(out1, out2, atol=1e-3)
 
     grad_output = torch.rand_like(out1)

torch_geometric/nn/conv/cugraph/gat_conv.py

@@ -26,6 +26,9 @@ class CuGraphGATConv(CuGraphModule):  # pragma: no cover
     :class:`~torch_geometric.nn.conv.GATConv` based on the :obj:`cugraph-ops`
     package that fuses message passing computation for accelerated execution
     and lower memory footprint.
+    The current method to enable :obj:`cugraph-ops`
+    is to use `The NVIDIA PyG Container
+    <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg>`_.
     """
     def __init__(
         self,
@@ -67,6 +70,7 @@ def forward(
         self,
         x: Tensor,
         edge_index: EdgeIndex,
+        edge_attr: Tensor,
         max_num_neighbors: Optional[int] = None,
     ) -> Tensor:
         graph = self.get_cugraph(edge_index, max_num_neighbors)
@@ -75,10 +79,12 @@ def forward(
 
         if LEGACY_MODE:
             out = GATConvAgg(x, self.att, graph, self.heads, 'LeakyReLU',
-                             self.negative_slope, False, self.concat)
+                             self.negative_slope, False, self.concat,
+                             edge_feat=edge_attr)
         else:
             out = GATConvAgg(x, self.att, graph, self.heads, 'LeakyReLU',
-                             self.negative_slope, self.concat)
+                             self.negative_slope, self.concat,
+                             edge_feat=edge_attr)
 
         if self.bias is not None:
             out = out + self.bias

torch_geometric/nn/conv/cugraph/rgcn_conv.py

@@ -29,6 +29,9 @@ class CuGraphRGCNConv(CuGraphModule):  # pragma: no cover
     :class:`~torch_geometric.nn.conv.RGCNConv` based on the :obj:`cugraph-ops`
     package that fuses message passing computation for accelerated execution
     and lower memory footprint.
+    The current method to enable :obj:`cugraph-ops`
+    is to use `The NVIDIA PyG Container
+    <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg>`_.
     """
     def __init__(self, in_channels: int, out_channels: int, num_relations: int,
                  num_bases: Optional[int] = None, aggr: str = 'mean',

torch_geometric/nn/conv/cugraph/sage_conv.py

@@ -27,6 +27,9 @@ class CuGraphSAGEConv(CuGraphModule):  # pragma: no cover
     :class:`~torch_geometric.nn.conv.SAGEConv` based on the :obj:`cugraph-ops`
     package that fuses message passing computation for accelerated execution
     and lower memory footprint.
+    The current method to enable :obj:`cugraph-ops`
+    is to use `The NVIDIA PyG Container
+    <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg>`_.
     """
     def __init__(
         self,