[Misc] Modify two node examples to make them consitent. (#6599)

examples/sampling/graphbolt/node_classification.py

@@ -207,7 +207,7 @@ class SAGE(nn.Module):
             y = torch.empty(
                 graph.total_num_nodes,
                 self.out_size if is_last_layer else self.hidden_size,
-                dtype=torch.float64,
+                dtype=torch.float32,
                 device=buffer_device,
                 pin_memory=pin_memory,
             )
@@ -215,7 +215,7 @@ class SAGE(nn.Module):
 
             for step, data in tqdm(enumerate(dataloader)):
                 x = feature[data.input_nodes]
-                hidden_x = layer(data.blocks[0], x.float())  # len(blocks) = 1
+                hidden_x = layer(data.blocks[0], x)  # len(blocks) = 1
                 if not is_last_layer:
                     hidden_x = F.relu(hidden_x)
                     hidden_x = self.dropout(hidden_x)
@@ -274,7 +274,7 @@ def evaluate(args, model, graph, features, itemset, num_classes):
     for step, data in tqdm(enumerate(dataloader)):
         x = data.node_features["feat"]
         y.append(data.labels)
-        y_hats.append(model(data.blocks, x.float()))
+        y_hats.append(model(data.blocks, x))
 
     return MF.accuracy(
         torch.cat(y_hats),
@@ -310,7 +310,7 @@ def train(args, graph, features, train_set, valid_set, num_classes, model):
             # in the last layer's computation graph.
             y = data.labels
 
-            y_hat = model(data.blocks, x.float())
+            y_hat = model(data.blocks, x)
 
             # Compute loss.
             loss = F.cross_entropy(y_hat, y)

examples/sampling/node_classification.py

@@ -274,10 +274,16 @@ if __name__ == "__main__":
     parser.add_argument(
         "--mode",
         default="mixed",
-        choices=["cpu", "mixed", "gpu", "compare-to-graphbolt"],
+        choices=["cpu", "mixed", "gpu"],
         help="Training mode. 'cpu' for CPU training, 'mixed' for "
         "CPU-GPU mixed training, 'gpu' for pure-GPU training.",
     )
+    parser.add_argument(
+        "--compare-to-graphbolt",
+        default="false",
+        choices=["false", "true"],
+        help="Whether comparing to GraphBolt or not, 'false' by default.",
+    )
     args = parser.parse_args()
     if not torch.cuda.is_available():
         args.mode = "cpu"
@@ -286,13 +292,15 @@ if __name__ == "__main__":
     # Load and preprocess dataset.
     print("Loading data")
     dataset = AsNodePredDataset(DglNodePropPredDataset("ogbn-products"))
+
     g = dataset[0]
+    if args.compare_to_graphbolt == "false":
         g = g.to("cuda" if args.mode == "gpu" else "cpu")
     num_classes = dataset.num_classes
     # Whether use Unified Virtual Addressing (UVA) for CUDA computation.
     use_uva = args.mode == "mixed"
     device = torch.device("cpu" if args.mode == "cpu" else "cuda")
-    fused_sampling = args.mode != "compare-to-graphbolt"
+    fused_sampling = args.compare_to_graphbolt == "false"
 
     # Create GraphSAGE model.
     in_size = g.ndata["feat"].shape[1]