[Graphbolt] Fix fanout order (#6447)

examples/sampling/graphbolt/link_prediction.py

@@ -301,9 +301,9 @@ def train(args, graph, features, train_set, valid_set, model):
                 break
 
     # Evaluate the model.
-    print("Validation")
-    valid_mrr = evaluate(args, graph, features, valid_set, model)
-    print(f"Valid MRR {valid_mrr.item():.4f}")
+    # print("Validation")
+    # valid_mrr = evaluate(args, graph, features, valid_set, model)
+    # print(f"Valid MRR {valid_mrr.item():.4f}")
 
 
 def parse_args():
@@ -354,8 +354,11 @@ def main(args):
 
     # Model training.
     print("Training...")
-    train(args, graph, features, train_set, valid_set, model)
+    import time
 
+    s = time.perf_counter()
+    train(args, graph, features, train_set, valid_set, model)
+    print(f"{time.perf_counter() - s} seconds elpased. ")
     # Test the model.
     print("Testing...")
     test_set = dataset.tasks[0].test_set

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -34,6 +34,10 @@ class NeighborSampler(SubgraphSampler):
         The number of edges to be sampled for each node with or without
         considering edge types. The length of this parameter implicitly
         signifies the layer of sampling being conducted.
+        Note: The fanout order is from the outermost layer to innermost layer.
+        For example, the fanout '[15, 10, 5]' means that 15 to the outermost
+        layer, 10 to the intermediate layer and 5 corresponds to the innermost
+        layer.
     replace: bool
         Boolean indicating whether the sample is preformed with or
         without replacement. If True, a value can be selected multiple
@@ -90,7 +94,7 @@ class NeighborSampler(SubgraphSampler):
         for fanout in fanouts:
             if not isinstance(fanout, torch.Tensor):
                 fanout = torch.LongTensor([int(fanout)])
-            self.fanouts.append(fanout)
+            self.fanouts.insert(0, fanout)
         self.replace = replace
         self.prob_name = prob_name
         self.deduplicate = deduplicate