torch.compile benchmark for HeteroConv; Allow device conversions …

…of datasets (#8402)

```
+----------+-----------+------------+---------+
| Name     | Forward   | Backward   | Total   |
|----------+-----------+------------+---------|
| Vanilla  | 2.0331s   | 0.5175s    | 2.5505s |
| Compiled | 0.6899s   | 0.6946s    | 1.3844s |
+----------+-----------+------------+---------+
```

CHANGELOG.md

@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added support for device conversions of `InMemoryDataset` ([#8402] (https://github.com/pyg-team/pytorch_geometric/pull/8402))
 - Added support for edge-level temporal sampling in `NeighborLoader` and `LinkNeighborLoader` ([#8372] (https://github.com/pyg-team/pytorch_geometric/pull/8372))
 - Added support for `torch.compile` in `ModuleDict` and `ParameterDict` ([#8363](https://github.com/pyg-team/pytorch_geometric/pull/8363))
 - Added `force_reload` option to `Dataset` and `InMemoryDataset` to reload datasets ([#8352](https://github.com/pyg-team/pytorch_geometric/pull/8352), [#8357](https://github.com/pyg-team/pytorch_geometric/pull/8357))

test/nn/conv/test_hetero_conv.py

@@ -1,8 +1,11 @@
+import random
+
 import pytest
 import torch
 
 import torch_geometric
 from torch_geometric.data import HeteroData
+from torch_geometric.datasets import FakeHeteroDataset
 from torch_geometric.nn import (
     GATConv,
     GCN2Conv,
@@ -12,6 +15,7 @@
     MessagePassing,
     SAGEConv,
 )
+from torch_geometric.profile import benchmark
 from torch_geometric.testing import (
     disableExtensions,
     get_random_edge_index,
@@ -205,3 +209,64 @@ def test_compile_hetero_conv_graph_breaks(device):
     assert len(out) == len(expected)
     for key in expected.keys():
         assert torch.allclose(out[key], expected[key], atol=1e-6)
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default='cuda')
+    parser.add_argument('--backward', action='store_true')
+    args = parser.parse_args()
+
+    dataset = FakeHeteroDataset(num_graphs=10).to(args.device)
+
+    def gen_args():
+        data = dataset[random.randrange(len(dataset))]
+        return data.x_dict, data.edge_index_dict
+
+    class HeteroGNN(torch.nn.Module):
+        def __init__(self, channels: int = 32, num_layers: int = 2):
+            super().__init__()
+            self.convs = torch.nn.ModuleList()
+
+            conv = HeteroConv({
+                edge_type:
+                SAGEConv(
+                    in_channels=(
+                        dataset.num_features[edge_type[0]],
+                        dataset.num_features[edge_type[-1]],
+                    ),
+                    out_channels=channels,
+                )
+                for edge_type in dataset[0].edge_types
+            })
+            self.convs.append(conv)
+
+            for _ in range(num_layers - 1):
+                conv = HeteroConv({
+                    edge_type:
+                    SAGEConv((channels, channels), channels)
+                    for edge_type in dataset[0].edge_types
+                })
+                self.convs.append(conv)
+
+            self.lin = Linear(channels, 1)
+
+        def forward(self, x_dict, edge_index_dict):
+            for conv in self.convs:
+                x_dict = conv(x_dict, edge_index_dict)
+                x_dict = {key: x.relu() for key, x in x_dict.items()}
+            return self.lin(x_dict['v0'])
+
+    model = HeteroGNN().to(args.device)
+    compiled_model = torch_geometric.compile(model)
+
+    benchmark(
+        funcs=[model, compiled_model],
+        func_names=['Vanilla', 'Compiled'],
+        args=gen_args,
+        num_steps=50 if args.device == 'cpu' else 500,
+        num_warmups=10 if args.device == 'cpu' else 100,
+        backward=args.backward,
+    )

torch_geometric/data/in_memory_dataset.py

@@ -307,6 +307,31 @@ def __getattr__(self, key: str) -> Any:
         raise AttributeError(f"'{self.__class__.__name__}' object has no "
                              f"attribute '{key}'")
 
+    def to(self, device: Union[int, str]) -> 'InMemoryDataset':
+        r"""Performs device conversion of the whole dataset."""
+        if self._indices is not None:
+            raise ValueError("The given 'InMemoryDataset' only references a "
+                             "subset of examples of the full dataset")
+        if self._data_list is not None:
+            raise ValueError("The data of the dataset is already cached")
+        self._data.to(device)
+        return self
+
+    def cpu(self, *args: str) -> 'InMemoryDataset':
+        r"""Moves the dataset to CPU memory."""
+        return self.to(torch.device('cpu'))
+
+    def cuda(
+        self,
+        device: Optional[Union[int, str]] = None,
+    ) -> 'InMemoryDataset':
+        r"""Moves the dataset toto CUDA memory."""
+        if isinstance(device, int):
+            device = f'cuda:{int}'
+        elif device is None:
+            device = 'cuda'
+        return self.to(device)
+
 
 def nested_iter(node: Union[Mapping, Sequence]) -> Iterable:
     if isinstance(node, Mapping):