Allow optional but untyped tensors in MessagePassing (#9494)

Fixes #9492

CHANGELOG.md

@@ -36,6 +36,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Allow optional but untyped tensors in `MessagePassing` ([#9494](https://github.com/pyg-team/pytorch_geometric/pull/9494))
 - Added support for modifying `filename` of the stored partitioned file in `ClusterLoader` ([#9448](https://github.com/pyg-team/pytorch_geometric/pull/9448))
 - Support other than two-dimensional inputs in `AttentionalAggregation` ([#9433](https://github.com/pyg-team/pytorch_geometric/pull/9433))
 - Improved model performance of the `examples/ogbn_papers_100m.py` script ([#9386](https://github.com/pyg-team/pytorch_geometric/pull/9386), [#9445](https://github.com/pyg-team/pytorch_geometric/pull/9445))

test/nn/conv/test_message_passing.py

@@ -740,3 +740,24 @@ def test_pickle(tmp_path):
 
     model = torch.load(path)
     torch.jit.script(model)
+
+
+class MyOptionalEdgeAttrConv(MessagePassing):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, edge_index, edge_attr=None):
+        return self.propagate(edge_index, x=x, edge_attr=edge_attr)
+
+    def message(self, x_j, edge_attr=None):
+        return x_j if edge_attr is None else x_j * edge_attr.view(-1, 1)
+
+
+def test_my_optional_edge_attr_conv():
+    conv = MyOptionalEdgeAttrConv()
+
+    x = torch.randn(4, 8)
+    edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
+
+    out = conv(x, edge_index)
+    assert out.size() == (4, 8)

torch_geometric/nn/conv/collect.jinja

@@ -98,13 +98,16 @@ def {{collect_name}}(
 
 {%- if 'edge_weight' in collect_param_dict and
     collect_param_dict['edge_weight'].type_repr.endswith('Tensor') %}
-    assert edge_weight is not None
+    if torch.jit.is_scripting():
+        assert edge_weight is not None
 {%- elif 'edge_attr' in collect_param_dict and
     collect_param_dict['edge_attr'].type_repr.endswith('Tensor') %}
-    assert edge_attr is not None
+    if torch.jit.is_scripting():
+        assert edge_attr is not None
 {%- elif 'edge_type' in collect_param_dict and
     collect_param_dict['edge_type'].type_repr.endswith('Tensor') %}
-    assert edge_type is not None
+    if torch.jit.is_scripting():
+        assert edge_type is not None
 {%- endif %}
 
     # Collect user-defined arguments: