Add NodeFormer model and example

CHANGELOG.md

@@ -14,6 +14,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added `NodeFormer` model and example ([#10409](https://github.com/pyg-team/pytorch_geometric/pull/10409))
 - Added `torch_geometric.llm` and its examples ([#10436](https://github.com/pyg-team/pytorch_geometric/pull/10436))
 - Added support for negative weights in `sparse_cross_entropy` ([#10432](https://github.com/pyg-team/pytorch_geometric/pull/10432))
 - Added `connected_components()` method to `Data` and `HeterData` ([#10388](https://github.com/pyg-team/pytorch_geometric/pull/10388))

examples/ogbn_train.py

@@ -11,7 +11,13 @@
 
 from torch_geometric import seed_everything
 from torch_geometric.loader import NeighborLoader
-from torch_geometric.nn.models import GAT, GraphSAGE, Polynormer, SGFormer
+from torch_geometric.nn.models import (
+    GAT,
+    GraphSAGE,
+    NodeFormer,
+    Polynormer,
+    SGFormer,
+)
 from torch_geometric.utils import (
     add_self_loops,
     remove_self_loops,
@@ -37,7 +43,7 @@
     "--model",
     type=str.lower,
     default='SGFormer',
-    choices=['sage', 'gat', 'sgformer', 'polynormer'],
+    choices=['sage', 'gat', 'sgformer', 'polynormer', 'nodeformer'],
     help="Model used for training",
 )
 
@@ -55,6 +61,8 @@
 parser.add_argument('--lr', type=float, default=0.003)
 parser.add_argument('--wd', type=float, default=0.0)
 parser.add_argument('--dropout', type=float, default=0.5)
+parser.add_argument('--lamda', type=float, default=0.1,
+                    help='weight for edge reg loss of nodeformer')
 parser.add_argument(
     '--use_directed_graph',
     action='store_true',
@@ -132,18 +140,23 @@ def train(epoch: int) -> tuple[Tensor, float]:
 
     total_loss = total_correct = 0
     for batch in train_loader:
+        batch = batch.to(device)
         optimizer.zero_grad()
         if args.model in ['sgformer', 'polynormer']:
             if args.model == 'polynormer' and epoch == args.local_epochs:
                 print('start global attention')
                 model._global = True
-            out = model(batch.x, batch.edge_index.to(device),
-                        batch.batch.to(device))[:batch.batch_size]
+            out = model(batch.x, batch.edge_index,
+                        batch.batch)[:batch.batch_size]
+        elif args.model in ['nodeformer']:
+            out, link_loss = model(batch.x, batch.edge_index)
+            out = out[:batch.batch_size]
         else:
-            out = model(batch.x,
-                        batch.edge_index.to(device))[:batch.batch_size]
+            out = model(batch.x, batch.edge_index)[:batch.batch_size]
         y = batch.y[:batch.batch_size].squeeze().to(torch.long)
         loss = F.cross_entropy(out, y)
+        if args.model in ['nodeformer']:
+            loss -= args.lamda * sum(link_loss) / len(link_loss)
         loss.backward()
         optimizer.step()
 
@@ -168,6 +181,9 @@ def test(loader: NeighborLoader) -> float:
         if args.model in ['sgformer', 'polynormer']:
             out = model(batch.x, batch.edge_index,
                         batch.batch)[:batch.batch_size]
+        elif args.model in ['nodeformer']:
+            out, _ = model(batch.x, batch.edge_index)
+            out = out[:batch.batch_size]
         else:
             out = model(batch.x, batch.edge_index)[:batch_size]
         pred = out.argmax(dim=-1)
@@ -214,6 +230,24 @@ def get_model(model_name: str) -> torch.nn.Module:
             out_channels=dataset.num_classes,
             local_layers=num_layers,
         )
+    elif model_name == 'nodeformer':
+        model = NodeFormer(
+            in_channels=dataset.num_features,
+            hidden_channels=num_hidden_channels,
+            out_channels=dataset.num_classes,
+            num_layers=num_layers,
+            num_heads=args.num_heads,
+            use_bn=True,
+            nb_random_features=100,
+            use_gumbel=True,
+            use_residual=True,
+            use_act=True,
+            use_jk=True,
+            nb_gumbel_sample=5,
+            rb_order=1,
+            rb_trans='identity',
+            tau=1.0,
+        )
     else:
         raise ValueError(f'Unsupported model type: {model_name}')
 
@@ -227,8 +261,13 @@ def get_model(model_name: str) -> torch.nn.Module:
     lr=args.lr,
     weight_decay=args.wd,
 )
-scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max',
-                                                       patience=5)
+if args.model == 'nodeformer':
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
+                                                     milestones=[100, 200],
+                                                     gamma=0.5)
+else:
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer, mode='max', patience=5)
 
 print(f'Total time before training begins took '
       f'{time.perf_counter() - wall_clock_start:.4f}s')

test/nn/models/test_nodeformer.py

@@ -0,0 +1,21 @@
+import torch
+
+from torch_geometric.nn.models import NodeFormer
+
+
+def test_nodeformer():
+    x = torch.randn(10, 16)
+    edge_index = torch.tensor([
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+        [1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
+    ])
+
+    model = NodeFormer(
+        in_channels=16,
+        hidden_channels=128,
+        out_channels=40,
+        num_layers=3,
+    )
+    out, link_loss = model(x, edge_index)
+    assert out.size() == (10, 40)
+    assert len(link_loss) == 3

torch_geometric/nn/models/__init__.py

@@ -33,6 +33,7 @@
 from .sgformer import SGFormer
 
 from .polynormer import Polynormer
+from .nodeformer import NodeFormer
 # Deprecated:
 from torch_geometric.explain.algorithm.captum import (to_captum_input,
                                                       captum_output_to_dicts)
@@ -85,5 +86,6 @@
     'LPFormer',
     'SGFormer',
     'Polynormer',
+    'NodeFormer',
     'ARLinkPredictor',
 ]