Merge branch 'master' into type_hints_cartesian

CHANGELOG.md

@@ -40,7 +40,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Support `in_channels` with `tuple` in `GENConv` for bipartite message passing ([#5627](https://github.com/pyg-team/pytorch_geometric/pull/5627), [#5641](https://github.com/pyg-team/pytorch_geometric/pull/5641))
 - Handle cases of not having enough possible negative edges in `RandomLinkSplit` ([#5642](https://github.com/pyg-team/pytorch_geometric/pull/5642))
 - Fix `RGCN+pyg-lib` for `LongTensor` input ([#5610](https://github.com/pyg-team/pytorch_geometric/pull/5610))
-- Improved type hint support ([#5603](https://github.com/pyg-team/pytorch_geometric/pull/5603), [#5659](https://github.com/pyg-team/pytorch_geometric/pull/5659), [#5665](https://github.com/pyg-team/pytorch_geometric/pull/5665), [#5666](https://github.com/pyg-team/pytorch_geometric/pull/5666), [#5667](https://github.com/pyg-team/pytorch_geometric/pull/5667), [#5668](https://github.com/pyg-team/pytorch_geometric/pull/5668), [#5669](https://github.com/pyg-team/pytorch_geometric/pull/5669), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5673))
+- Improved type hint support ([#5603](https://github.com/pyg-team/pytorch_geometric/pull/5603), [#5659](https://github.com/pyg-team/pytorch_geometric/pull/5659), [#5664](https://github.com/pyg-team/pytorch_geometric/pull/5664), [#5665](https://github.com/pyg-team/pytorch_geometric/pull/5665), [#5666](https://github.com/pyg-team/pytorch_geometric/pull/5666), [#5667](https://github.com/pyg-team/pytorch_geometric/pull/5667), [#5668](https://github.com/pyg-team/pytorch_geometric/pull/5668), [#5669](https://github.com/pyg-team/pytorch_geometric/pull/5669), [#5673](https://github.com/pyg-team/pytorch_geometric/pull/5673))
 - Avoid modifying `mode_kwargs` in `MultiAggregation` ([#5601](https://github.com/pyg-team/pytorch_geometric/pull/5601))
 - Changed `BatchNorm` to allow for batches of size one during training ([#5530](https://github.com/pyg-team/pytorch_geometric/pull/5530), [#5614](https://github.com/pyg-team/pytorch_geometric/pull/5614))
 - Integrated better temporal sampling support by requiring that local neighborhoods are sorted according to time ([#5516](https://github.com/pyg-team/pytorch_geometric/issues/5516), [#5602](https://github.com/pyg-team/pytorch_geometric/issues/5602))

CONTRIBUTING.md

@@ -96,6 +96,9 @@ Everytime you send a Pull Request, your commit will be built and checked against
 
    (which runs a set of additional but time-consuming tests) dependening on your needs.
 
+3. Add your feature/bugfix to the [`CHANGELOG.md`](https://github.com/pyg-team/pytorch_geometric/blob/master/CHANGELOG.md?plain=1).
+   If multiple PRs move towards integrating a single feature, it is advised to group them together into one bullet point.
+
 ## Building Documentation
 
 To build the documentation:

test/loader/test_link_neighbor_loader.py

@@ -3,6 +3,7 @@
 
 from torch_geometric.data import Data, HeteroData
 from torch_geometric.loader import LinkNeighborLoader
+from torch_geometric.testing import withPackage
 from torch_geometric.testing.feature_store import MyFeatureStore
 from torch_geometric.testing.graph_store import MyGraphStore
 
@@ -181,6 +182,7 @@ def test_link_neighbor_loader_edge_label():
         assert torch.all(batch.edge_label[10:] == 0)
 
 
+@withPackage('pyg_lib')
 def test_temporal_heterogeneous_link_neighbor_loader():
     data = HeteroData()
 

test/loader/test_neighbor_loader.py

@@ -280,6 +280,7 @@ def forward(self, x, edge_index, edge_weight):
     assert torch.allclose(out1, out2, atol=1e-6)
 
 
+@withPackage('pyg_lib')
 def test_temporal_heterogeneous_neighbor_loader_on_cora(get_dataset):
     dataset = get_dataset(name='Cora')
     data = dataset[0]
@@ -380,6 +381,7 @@ def test_custom_neighbor_loader(FeatureStore, GraphStore):
             'author', 'to', 'paper'].edge_index.size())
 
 
+@withPackage('pyg_lib')
 @pytest.mark.parametrize('FeatureStore', [MyFeatureStore, HeteroData])
 @pytest.mark.parametrize('GraphStore', [MyGraphStore, HeteroData])
 def test_temporal_custom_neighbor_loader_on_cora(get_dataset, FeatureStore,

test/nn/dense/test_dense_gcn_conv.py

@@ -1,6 +1,7 @@
 import torch
 
 from torch_geometric.nn import DenseGCNConv, GCNConv
+from torch_geometric.testing import is_full_test
 
 
 def test_dense_gcn_conv():
@@ -38,6 +39,10 @@ def test_dense_gcn_conv():
     dense_out = dense_conv(x, adj, mask)
     assert dense_out.size() == (2, 3, channels)
 
+    if is_full_test():
+        jit = torch.jit.script(dense_conv)
+        assert torch.allclose(jit(x, adj, mask), dense_out)
+
     assert dense_out[1, 2].abs().sum().item() == 0
     dense_out = dense_out.view(6, channels)[:-1]
     assert torch.allclose(sparse_out, dense_out, atol=1e-04)

test/nn/dense/test_dense_sage_conv.py

@@ -1,6 +1,7 @@
 import torch
 
 from torch_geometric.nn import DenseSAGEConv, SAGEConv
+from torch_geometric.testing import is_full_test
 
 
 def test_dense_sage_conv():
@@ -38,6 +39,10 @@ def test_dense_sage_conv():
     dense_out = dense_conv(x, adj, mask)
     assert dense_out.size() == (2, 3, channels)
 
+    if is_full_test():
+        jit = torch.jit.script(dense_conv)
+        assert torch.allclose(jit(x, adj, mask), dense_out)
+
     assert dense_out[1, 2].abs().sum().item() == 0
     dense_out = dense_out.view(6, channels)[:-1]
     assert torch.allclose(sparse_out, dense_out, atol=1e-04)

torch_geometric/nn/dense/dense_gcn_conv.py

@@ -1,14 +1,22 @@
 import torch
+from torch import Tensor
 from torch.nn import Parameter
 
 from torch_geometric.nn.dense.linear import Linear
 from torch_geometric.nn.inits import zeros
+from torch_geometric.typing import OptTensor
 
 
 class DenseGCNConv(torch.nn.Module):
     r"""See :class:`torch_geometric.nn.conv.GCNConv`.
     """
-    def __init__(self, in_channels, out_channels, improved=False, bias=True):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        improved: bool = False,
+        bias: bool = True,
+    ):
         super().__init__()
 
         self.in_channels = in_channels
@@ -29,7 +37,8 @@ def reset_parameters(self):
         self.lin.reset_parameters()
         zeros(self.bias)
 
-    def forward(self, x, adj, mask=None, add_loop=True):
+    def forward(self, x: Tensor, adj: Tensor, mask: OptTensor = None,
+                add_loop: bool = True) -> Tensor:
         r"""
         Args:
             x (Tensor): Node feature tensor :math:`\mathbf{X} \in \mathbb{R}^{B

torch_geometric/nn/dense/dense_sage_conv.py

@@ -1,7 +1,10 @@
 import torch
 import torch.nn.functional as F
+from torch import Tensor
 from torch.nn import Linear
 
+from torch_geometric.typing import OptTensor
+
 
 class DenseSAGEConv(torch.nn.Module):
     r"""See :class:`torch_geometric.nn.conv.SAGEConv`.
@@ -14,7 +17,13 @@ class DenseSAGEConv(torch.nn.Module):
         use :class:`torch_geometric.nn.dense.DenseGraphConv` instead.
 
     """
-    def __init__(self, in_channels, out_channels, normalize=False, bias=True):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        normalize: bool = False,
+        bias: bool = True,
+    ):
         super().__init__()
 
         self.in_channels = in_channels
@@ -30,7 +39,8 @@ def reset_parameters(self):
         self.lin_rel.reset_parameters()
         self.lin_root.reset_parameters()
 
-    def forward(self, x, adj, mask=None):
+    def forward(self, x: Tensor, adj: Tensor,
+                mask: OptTensor = None) -> Tensor:
         r"""
         Args:
             x (Tensor): Node feature tensor :math:`\mathbf{X} \in \mathbb{R}^{B
@@ -54,7 +64,7 @@ def forward(self, x, adj, mask=None):
         out = self.lin_rel(out) + self.lin_root(x)
 
         if self.normalize:
-            out = F.normalize(out, p=2, dim=-1)
+            out = F.normalize(out, p=2.0, dim=-1)
 
         if mask is not None:
             out = out * mask.view(B, N, 1).to(x.dtype)