[GraphBolt][Doc] Updated MultiGPU tutorial

tutorials/multi/2_node_classification.py

@@ -68,12 +68,7 @@ def __init__(self, in_size, hidden_size, out_size):
         self.hidden_size = hidden_size
         self.out_size = out_size
         # Set the dtype for the layers manually.
-        self.set_layer_dtype(torch.float32)
-
-    def set_layer_dtype(self, dtype):
-        for layer in self.layers:
-            for param in layer.parameters():
-                param.data = param.data.to(dtype)
+        self.float()
 
     def forward(self, blocks, x):
         hidden_x = x
@@ -105,22 +100,38 @@ def create_dataloader(
     features,
     itemset,
     device,
-    drop_last=False,
-    shuffle=True,
-    drop_uneven_inputs=False,
+    is_train,
 ):
     datapipe = gb.DistributedItemSampler(
         item_set=itemset,
         batch_size=1024,
-        drop_last=drop_last,
-        shuffle=shuffle,
-        drop_uneven_inputs=drop_uneven_inputs,
+        drop_last=is_train,
+        shuffle=is_train,
+        drop_uneven_inputs=is_train,
     )
+    datapipe = datapipe.copy_to(device, extra_attrs=["seed_nodes"])
+    # Now that we have moved to device, sample_neighbor and fetch_feature steps
+    # will be executed on GPUs.
     datapipe = datapipe.sample_neighbor(graph, [10, 10, 10])
     datapipe = datapipe.fetch_feature(features, node_feature_keys=["feat"])
-    datapipe = datapipe.copy_to(device)
-    dataloader = gb.DataLoader(datapipe)
-    return dataloader
+    return gb.DataLoader(datapipe)
+
+
+def weighted_reduce(tensor, weight, dst=0):
+    ########################################################################
+    # (HIGHLIGHT) Collect accuracy and loss values from sub-processes and
+    # obtain overall average values.
+    #
+    # `torch.distributed.reduce` is used to reduce tensors from all the
+    # sub-processes to a specified process, ReduceOp.SUM is used by default.
+    #
+    # Because the GPUs may have differing numbers of processed items, we
+    # perform a weighted mean to calculate the exact loss and accuracy.
+    ########################################################################
+    dist.reduce(tensor=tensor, dst=dst)
+    weight = torch.tensor(weight, device=tensor.device)
+    dist.reduce(tensor=weight, dst=dst)
+    return tensor / weight
 
 
 ######################################################################
@@ -140,15 +151,11 @@ def evaluate(rank, model, graph, features, itemset, num_classes, device):
         graph,
         features,
         itemset,
-        drop_last=False,
-        shuffle=False,
-        drop_uneven_inputs=False,
-        device=device,
+        device,
+        is_train=False,
     )
 
-    for step, data in (
-        tqdm.tqdm(enumerate(dataloader)) if rank == 0 else enumerate(dataloader)
-    ):
+    for data in tqdm.tqdm(dataloader) if rank == 0 else dataloader:
         blocks = data.blocks
         x = data.node_features["feat"]
         y.append(data.labels)
@@ -161,7 +168,7 @@ def evaluate(rank, model, graph, features, itemset, num_classes, device):
         num_classes=num_classes,
     )
 
-    return res.to(device)
+    return res.to(device), sum(y_i.size(0) for y_i in y)
 
 
 ######################################################################
@@ -196,22 +203,17 @@ def train(
         features,
         train_set,
         device,
-        drop_last=False,
-        shuffle=True,
-        drop_uneven_inputs=False,
+        is_train=True,
     )
 
     for epoch in range(5):
         epoch_start = time.time()
 
         model.train()
-        total_loss = torch.tensor(0, dtype=torch.float).to(device)
+        total_loss = torch.tensor(0, dtype=torch.float, device=device)
+        num_train_items = 0
         with Join([model]):
-            for step, data in (
-                tqdm.tqdm(enumerate(dataloader))
-                if rank == 0
-                else enumerate(dataloader)
-            ):
+            for data in tqdm.tqdm(dataloader) if rank == 0 else dataloader:
                 # The input features are from the source nodes in the first
                 # layer's computation graph.
                 x = data.node_features["feat"]
@@ -231,40 +233,31 @@ def train(
                 loss.backward()
                 optimizer.step()
 
-                total_loss += loss
+                total_loss += loss * y.size(0)
+                num_train_items += y.size(0)
 
         # Evaluate the model.
         if rank == 0:
             print("Validating...")
-        acc = (
-            evaluate(
-                rank,
-                model,
-                graph,
-                features,
-                valid_set,
-                num_classes,
-                device,
-            )
-            / world_size
+        acc, num_val_items = evaluate(
+            rank,
+            model,
+            graph,
+            features,
+            valid_set,
+            num_classes,
+            device,
         )
-        ########################################################################
-        # (HIGHLIGHT) Collect accuracy and loss values from sub-processes and
-        # obtain overall average values.
-        #
-        # `torch.distributed.reduce` is used to reduce tensors from all the
-        # sub-processes to a specified process, ReduceOp.SUM is used by default.
-        ########################################################################
-        dist.reduce(tensor=acc, dst=0)
-        total_loss /= step + 1
-        dist.reduce(tensor=total_loss, dst=0)
-        dist.barrier()
+        total_loss = weighted_reduce(total_loss, num_train_items)
+        acc = weighted_reduce(acc * num_val_items, num_val_items)
 
+        # We synchronize before measuring the epoch time.
+        torch.cuda.synchronize()
         epoch_end = time.time()
         if rank == 0:
             print(
                 f"Epoch {epoch:05d} | "
-                f"Average Loss {total_loss.item() / world_size:.4f} | "
+                f"Average Loss {total_loss.item():.4f} | "
                 f"Accuracy {acc.item():.4f} | "
                 f"Time {epoch_end - epoch_start:.4f}"
             )
@@ -292,8 +285,9 @@ def run(rank, world_size, devices, dataset):
         rank=rank,
     )
 
-    graph = dataset.graph
-    features = dataset.feature
+    # Pin the graph and features in-place to enable GPU access.
+    graph = dataset.graph.pin_memory_()
+    features = dataset.feature.pin_memory_()
     train_set = dataset.tasks[0].train_set
     valid_set = dataset.tasks[0].validation_set
     num_classes = dataset.tasks[0].metadata["num_classes"]
@@ -325,20 +319,17 @@ def run(rank, world_size, devices, dataset):
     if rank == 0:
         print("Testing...")
     test_set = dataset.tasks[0].test_set
-    test_acc = (
-        evaluate(
-            rank,
-            model,
-            graph,
-            features,
-            itemset=test_set,
-            num_classes=num_classes,
-            device=device,
-        )
-        / world_size
+    test_acc, num_test_items = evaluate(
+        rank,
+        model,
+        graph,
+        features,
+        itemset=test_set,
+        num_classes=num_classes,
+        device=device,
     )
-    dist.reduce(tensor=test_acc, dst=0)
-    dist.barrier()
+    test_acc = weighted_reduce(test_acc * num_test_items, num_test_items)
+
     if rank == 0:
         print(f"Test Accuracy {test_acc.item():.4f}")