Add XPU support to basic GNN examples

CHANGELOG.md

@@ -29,7 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added a `ogbn-mag240m` example ([#8249](https://github.com/pyg-team/pytorch_geometric/pull/8249))
 - Added `EdgeIndex.sparse_resize_` functionality ([#8983](https://github.com/pyg-team/pytorch_geometric/pull/8983))
 - Added approximate `faiss`-based KNN-search ([#8952](https://github.com/pyg-team/pytorch_geometric/pull/8952))
-- Added documentation on Environment setup on XPU device ([#9407](https://github.com/pyg-team/pytorch_geometric/pull/9407))
+- Added documentation on environment setup on XPU device ([#9407](https://github.com/pyg-team/pytorch_geometric/pull/9407))
 
 ### Changed
 
@@ -48,6 +48,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Made `MessagePassing` interface thread-safe ([#9001](https://github.com/pyg-team/pytorch_geometric/pull/9001))
 - Breaking Change: Added support for `EdgeIndex` in `cugraph` GNN layers ([#8938](https://github.com/pyg-team/pytorch_geometric/pull/8937))
 - Added the `dim` arg to `torch.cross` calls ([#8918](https://github.com/pyg-team/pytorch_geometric/pull/8918))
+- Added XPU support to basic GNN examples ([#9421](https://github.com/pyg-team/pytorch_geometric/pull/9001))
 
 ### Deprecated
 

examples/gat.py

@@ -5,6 +5,7 @@
 import torch
 import torch.nn.functional as F
 
+import torch_geometric
 import torch_geometric.transforms as T
 from torch_geometric.datasets import Planetoid
 from torch_geometric.logging import init_wandb, log
@@ -19,7 +20,13 @@
 parser.add_argument('--wandb', action='store_true', help='Track experiment')
 args = parser.parse_args()
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+elif torch_geometric.is_xpu_available():
+    device = torch.device('xpu')
+else:
+    device = torch.device('cpu')
+
 init_wandb(name=f'GAT-{args.dataset}', heads=args.heads, epochs=args.epochs,
            hidden_channels=args.hidden_channels, lr=args.lr, device=device)
 

examples/gcn.py

@@ -5,6 +5,7 @@
 import torch
 import torch.nn.functional as F
 
+import torch_geometric
 import torch_geometric.transforms as T
 from torch_geometric.datasets import Planetoid
 from torch_geometric.logging import init_wandb, log
@@ -19,12 +20,7 @@
 parser.add_argument('--wandb', action='store_true', help='Track experiment')
 args = parser.parse_args()
 
-if torch.cuda.is_available():
-    device = torch.device('cuda')
-elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-    device = torch.device('mps')
-else:
-    device = torch.device('cpu')
+device = torch_geometric.device('auto')
 
 init_wandb(
     name=f'GCN-{args.dataset}',

examples/graph_sage_unsup.py

@@ -5,6 +5,7 @@
 import torch.nn.functional as F
 from sklearn.linear_model import LogisticRegression
 
+import torch_geometric
 import torch_geometric.transforms as T
 from torch_geometric.datasets import Planetoid
 from torch_geometric.loader import LinkNeighborLoader
@@ -22,8 +23,12 @@
     neg_sampling_ratio=1.0,
     num_neighbors=[10, 10],
 )
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+elif torch_geometric.is_xpu_available():
+    device = torch.device('xpu')
+else:
+    device = torch.device('cpu')
 data = data.to(device, 'x', 'edge_index')
 
 model = GraphSAGE(

examples/graph_sage_unsup_ppi.py

@@ -8,6 +8,7 @@
 from sklearn.metrics import f1_score
 from sklearn.multioutput import MultiOutputClassifier
 
+import torch_geometric
 from torch_geometric.data import Batch
 from torch_geometric.datasets import PPI
 from torch_geometric.loader import DataLoader, LinkNeighborLoader
@@ -29,7 +30,12 @@
 val_loader = DataLoader(val_dataset, batch_size=2)
 test_loader = DataLoader(test_dataset, batch_size=2)
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+elif torch_geometric.is_xpu_available():
+    device = torch.device('xpu')
+else:
+    device = torch.device('cpu')
 model = GraphSAGE(
     in_channels=train_dataset.num_features,
     hidden_channels=64,

examples/pna.py

@@ -5,6 +5,7 @@
 from torch.nn import Embedding, Linear, ModuleList, ReLU, Sequential
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 
+import torch_geometric
 from torch_geometric.datasets import ZINC
 from torch_geometric.loader import DataLoader
 from torch_geometric.nn import BatchNorm, PNAConv, global_add_pool
@@ -66,7 +67,12 @@ def forward(self, x, edge_index, edge_attr, batch):
         return self.mlp(x)
 
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+elif torch_geometric.is_xpu_available():
+    device = torch.device('xpu')
+else:
+    device = torch.device('cpu')
 model = Net().to(device)
 optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
 scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20,

torch_geometric/__init__.py

@@ -3,6 +3,7 @@
 from .edge_index import EdgeIndex
 from .seed import seed_everything
 from .home import get_home_dir, set_home_dir
+from .device import is_mps_available, is_xpu_available, device
 from .isinstance import is_torch_instance
 from .debug import is_debug_enabled, debug, set_debug
 
@@ -33,6 +34,9 @@
     'set_home_dir',
     'compile',
     'is_compiling',
+    'is_mps_available',
+    'is_xpu_available',
+    'device',
     'is_torch_instance',
     'is_debug_enabled',
     'debug',

torch_geometric/device.py

@@ -0,0 +1,42 @@
+from typing import Any
+
+import torch
+
+
+def is_mps_available() -> bool:
+    r"""Returns a bool indicating if MPS is currently available."""
+    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        try:  # Github CI may not have access to MPS hardware. Confirm:
+            torch.empty(1, device='mps')
+            return True
+        except Exception:
+            return False
+    return False
+
+
+def is_xpu_available() -> bool:
+    r"""Returns a bool indicating if XPU is currently available."""
+    if hasattr(torch, 'xpu') and torch.xpu.is_available():
+        return True
+    try:
+        import intel_extension_for_pytorch as ipex
+        return ipex.xpu.is_available()
+    except ImportError:
+        return False
+
+
+def device(device: Any) -> torch.device:
+    r"""Returns a :class:`torch.device`.
+
+    If :obj:`"auto"` is specified, returns the optimal device depending on
+    available hardware.
+    """
+    if device != 'auto':
+        return torch.device(device)
+    if torch.cuda.is_available():
+        return torch.device('cuda')
+    if is_mps_available():
+        return torch.device('mps')
+    if is_xpu_available():
+        return torch.device('xpu')
+    return torch.device('cpu')

torch_geometric/testing/decorators.py

@@ -9,6 +9,7 @@
 from packaging.requirements import Requirement
 from packaging.version import Version
 
+import torch_geometric
 from torch_geometric.typing import WITH_METIS, WITH_PYG_LIB, WITH_TORCH_SPARSE
 from torch_geometric.visualization.graph import has_graphviz
 
@@ -113,13 +114,8 @@ def onlyCUDA(func: Callable) -> Callable:
 def onlyXPU(func: Callable) -> Callable:
     r"""A decorator to skip tests if XPU is not found."""
     import pytest
-    try:
-        import intel_extension_for_pytorch as ipex
-        xpu_available = ipex.xpu.is_available()
-    except ImportError:
-        xpu_available = False
     return pytest.mark.skipif(
-        not xpu_available,
+        not torch_geometric.is_xpu_available(),
         reason="XPU not available",
     )(func)
 
@@ -227,23 +223,10 @@ def withDevice(func: Callable) -> Callable:
     if torch.cuda.is_available():
         devices.append(pytest.param(torch.device('cuda:0'), id='cuda:0'))
 
-    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-        try:  # Github CI may not have access to MPS hardware. Confirm:
-            torch.empty(1, device='mps')
-            devices.append(pytest.param(torch.device('mps:0'), id='mps'))
-        except RuntimeError:
-            pass
-
-    if not hasattr(torch, 'xpu'):
-        try:
-            import intel_extension_for_pytorch as ipex
-            xpu_available = ipex.xpu.is_available()
-        except ImportError:
-            xpu_available = False
-    else:
-        xpu_available = torch.xpu.is_available()
+    if torch_geometric.is_mps_available():
+        devices.append(pytest.param(torch.device('mps:0'), id='mps'))
 
-    if xpu_available:
+    if torch_geometric.is_xpu_available():
         devices.append(pytest.param(torch.device('xpu:0'), id='xpu'))
 
     # Additional devices can be registered through environment variables: