Fix dtype conversion for Index|EdgeIndex in multi-process `DataLo…

…ader` (#9295)

test/loader/test_dataloader.py

@@ -206,6 +206,7 @@ def test_index_dataloader(num_workers):
 
     for batch in loader:
         assert isinstance(batch.index, Index)
+        assert batch.index.dtype == torch.long
         assert batch.index.dim_size == 7
         assert batch.index.is_sorted
 
@@ -236,6 +237,7 @@ def test_edge_index_dataloader(num_workers, sort_order):
 
     for batch in loader:
         assert isinstance(batch.edge_index, EdgeIndex)
+        assert batch.edge_index.dtype == torch.long
         assert batch.edge_index.sparse_size() == (6, 6)
         assert batch.edge_index.sort_order == sort_order
         assert batch.edge_index.is_undirected

torch_geometric/edge_index.py

@@ -478,6 +478,11 @@ def is_undirected(self) -> bool:
         r"""Returns whether indices are bidirectional."""
         return self._is_undirected
 
+    @property
+    def dtype(self) -> torch.dtype:  # type: ignore
+        # TODO Remove once PyTorch does not override `dtype` in `DataLoader`.
+        return self._data.dtype
+
     # Cache Interface #########################################################
 
     @overload

torch_geometric/index.py

@@ -199,6 +199,11 @@ def is_sorted(self) -> bool:
         r"""TODO."""
         return self._is_sorted
 
+    @property
+    def dtype(self) -> torch.dtype:  # type: ignore
+        # TODO Remove once PyTorch does not override `dtype` in `DataLoader`.
+        return self._data.dtype
+
     # Cache Interface #########################################################
 
     def get_dim_size(self) -> int: