Confirm that to_hetero() works with custom functions (dropout_adj)

CHANGELOG.md

@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [2.0.5] - 2022-MM-DD
 ### Added
+- Confirm that `to_hetero()` works with custom functions, *e.g.*, `dropout_adj` ([4653](https://github.com/pyg-team/pytorch_geometric/pull/4653))
 - Added the `MLP.plain_last=False` option ([4652](https://github.com/pyg-team/pytorch_geometric/pull/4652))
 - Added a check in `HeteroConv` and `to_hetero()` to ensure that `MessagePassing.add_self_loops` is disabled ([4647](https://github.com/pyg-team/pytorch_geometric/pull/4647))
 - Added `HeteroData.subgraph()` support ([#4635](https://github.com/pyg-team/pytorch_geometric/pull/4635))

test/nn/test_to_hetero_transformer.py

@@ -2,13 +2,17 @@
 
 import pytest
 import torch
+import torch.nn.functional as F
 from torch import Tensor
 from torch.nn import Linear, ReLU, Sequential
 from torch_sparse import SparseTensor
 
 from torch_geometric.nn import BatchNorm, GCNConv, GINEConv, GlobalPooling
 from torch_geometric.nn import Linear as LazyLinear
 from torch_geometric.nn import MessagePassing, RGCNConv, SAGEConv, to_hetero
+from torch_geometric.utils import dropout_adj
+
+torch.fx.wrap('dropout_adj')
 
 
 class Net1(torch.nn.Module):
@@ -123,6 +127,17 @@ def forward(self, x: Tensor) -> Tensor:
         return self.batch_norm(x)
 
 
+class Net10(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = SAGEConv(16, 32)
+
+    def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
+        x = F.dropout(x, p=0.5, training=self.training)
+        edge_index, _ = dropout_adj(edge_index, p=0.5, training=self.training)
+        return self.conv(x, edge_index)
+
+
 def test_to_hetero():
     x_dict = {
         'paper': torch.randn(100, 16),
@@ -213,6 +228,13 @@ def test_to_hetero():
     assert out['paper'].size() == (4, 16)
     assert out['author'].size() == (8, 16)
 
+    model = Net10()
+    model = to_hetero(model, metadata, debug=False)
+    out = model(x_dict, edge_index_dict)
+    assert isinstance(out, dict) and len(out) == 2
+    assert out['paper'].size() == (100, 32)
+    assert out['author'].size() == (100, 32)
+
 
 class GCN(torch.nn.Module):
     def __init__(self):