Merge branch 'master' into master

Author: rusty1s

CHANGELOG.md

@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [2.0.5] - 2022-MM-DD
 ### Added
 - Added a `normalize` parameter to `dense_diff_pool` ([#4847](https://github.com/pyg-team/pytorch_geometric/pull/4847))
+- Added `size=None` explanation to jittable `MessagePassing` modules in the documentation ([#4850](https://github.com/pyg-team/pytorch_geometric/pull/4850))
 - Added documentation to the `DataLoaderIterator` class ([#4838](https://github.com/pyg-team/pytorch_geometric/pull/4838))
 - Added `GraphStore` support to `Data` and `HeteroData` ([#4816](https://github.com/pyg-team/pytorch_geometric/pull/4816))
 - Added `FeatureStore` support to `Data` and `HeteroData` ([#4807](https://github.com/pyg-team/pytorch_geometric/pull/4807))

docs/source/notes/jit.rst

@@ -99,7 +99,8 @@ However, if you want your own GNN module to be jittable, you need to account for
             def forward(self, x: Tensor, edge_index: Tensor,
                         edge_weight: Optional[Tensor]) -> Tensor:
 
-                return self.propagate(edge_index, x=x, edge_weight=edge_weight)
+                return self.propagate(edge_index, x=x, edge_weight=edge_weight,
+                                      size=None)
 
    2. Declaring the type of propagation arguments as a comment anywhere inside your module:
 
@@ -115,4 +116,10 @@ However, if you want your own GNN module to be jittable, you need to account for
                         edge_weight: Optional[Tensor]) -> Tensor:
 
                 # propagate_type: (x: Tensor, edge_weight: Optional[Tensor])
-                return self.propagate(edge_index, x=x, edge_weight=edge_weight)
+                return self.propagate(edge_index, x=x, edge_weight=edge_weight,
+                                      size=None)
+
+.. warning::
+
+   Importantly, due to TorchScript limitations, one also has to pass in the :obj:`size` attribute to :meth:`~torch_geometric.nn.conv.message_passing.MessagePassing.propagate`.
+   In most cases, this can be simply set to :obj:`None` in which case it will be automatically inferred.