diff --git a/examples/pytorch/appnp/train.py b/examples/pytorch/appnp/train.py
index f08cdc29cb94..59d9c5899b4e 100644
--- a/examples/pytorch/appnp/train.py
+++ b/examples/pytorch/appnp/train.py
@@ -25,9 +25,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/cluster_gcn/cluster_gcn.py b/examples/pytorch/cluster_gcn/cluster_gcn.py
index 079f2306866c..6fa96b412bb9 100644
--- a/examples/pytorch/cluster_gcn/cluster_gcn.py
+++ b/examples/pytorch/cluster_gcn/cluster_gcn.py
@@ -46,9 +46,14 @@ def main(args):
         labels = torch.LongTensor(data.labels)
     else:
         labels = torch.FloatTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask).type(torch.bool)
-    val_mask = torch.ByteTensor(data.val_mask).type(torch.bool)
-    test_mask = torch.ByteTensor(data.test_mask).type(torch.bool)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/dgi/train.py b/examples/pytorch/dgi/train.py
index 129a181706b8..9fd0efd3c26d 100644
--- a/examples/pytorch/dgi/train.py
+++ b/examples/pytorch/dgi/train.py
@@ -22,9 +22,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/gat/train.py b/examples/pytorch/gat/train.py
index e77d8fc3bfcc..d79be0d105ea 100644
--- a/examples/pytorch/gat/train.py
+++ b/examples/pytorch/gat/train.py
@@ -41,9 +41,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     num_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/gcn/gcn_mp.py b/examples/pytorch/gcn/gcn_mp.py
index 24affad94840..550aef276fb1 100644
--- a/examples/pytorch/gcn/gcn_mp.py
+++ b/examples/pytorch/gcn/gcn_mp.py
@@ -118,9 +118,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/gcn/train.py b/examples/pytorch/gcn/train.py
index be90b20c5ef4..8a82c34a3430 100644
--- a/examples/pytorch/gcn/train.py
+++ b/examples/pytorch/gcn/train.py
@@ -25,9 +25,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/graphsage/graphsage.py b/examples/pytorch/graphsage/graphsage.py
index 1a4a22eea78b..b5fd307d56c4 100644
--- a/examples/pytorch/graphsage/graphsage.py
+++ b/examples/pytorch/graphsage/graphsage.py
@@ -60,9 +60,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/model_zoo/citation_network/run.py b/examples/pytorch/model_zoo/citation_network/run.py
index cc2648204c2d..fb55e5961b06 100644
--- a/examples/pytorch/model_zoo/citation_network/run.py
+++ b/examples/pytorch/model_zoo/citation_network/run.py
@@ -45,9 +45,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
@@ -147,4 +152,4 @@ def main(args):
             help="graph self-loop (default=False)")
     args = parser.parse_args()
     print(args)
-    main(args)
\ No newline at end of file
+    main(args)
diff --git a/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py b/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py
index 221e336fba9f..be3cae40612e 100644
--- a/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py
+++ b/examples/pytorch/sampling/dis_sampling/gcn_cv_sc_train.py
@@ -24,9 +24,14 @@ def main(args):
 
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py b/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py
index 7134cf56e6b5..d2665ff066a3 100644
--- a/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py
+++ b/examples/pytorch/sampling/dis_sampling/gcn_ns_sc_train.py
@@ -25,9 +25,14 @@ def main(args):
 
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/sampling/gcn_cv_sc.py b/examples/pytorch/sampling/gcn_cv_sc.py
index 0c854d878f48..fabe0431f81c 100644
--- a/examples/pytorch/sampling/gcn_cv_sc.py
+++ b/examples/pytorch/sampling/gcn_cv_sc.py
@@ -149,9 +149,14 @@ def main(args):
 
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/sampling/gcn_ns_sc.py b/examples/pytorch/sampling/gcn_ns_sc.py
index da945a4c1ee7..e5fd6cc39ba9 100644
--- a/examples/pytorch/sampling/gcn_ns_sc.py
+++ b/examples/pytorch/sampling/gcn_ns_sc.py
@@ -120,9 +120,14 @@ def main(args):
 
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/sgc/sgc.py b/examples/pytorch/sgc/sgc.py
index f799269fe588..db092f24d142 100644
--- a/examples/pytorch/sgc/sgc.py
+++ b/examples/pytorch/sgc/sgc.py
@@ -30,9 +30,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/sgc/sgc_reddit.py b/examples/pytorch/sgc/sgc_reddit.py
index f1a25e438d48..de5e9af8d1c9 100644
--- a/examples/pytorch/sgc/sgc_reddit.py
+++ b/examples/pytorch/sgc/sgc_reddit.py
@@ -33,9 +33,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()
diff --git a/examples/pytorch/tagcn/train.py b/examples/pytorch/tagcn/train.py
index 4628d72aec83..3bacc7e8efec 100644
--- a/examples/pytorch/tagcn/train.py
+++ b/examples/pytorch/tagcn/train.py
@@ -23,9 +23,14 @@ def main(args):
     data = load_data(args)
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
-    train_mask = torch.ByteTensor(data.train_mask)
-    val_mask = torch.ByteTensor(data.val_mask)
-    test_mask = torch.ByteTensor(data.test_mask)
+    if hasattr(torch, 'BoolTensor'):
+        train_mask = torch.BoolTensor(data.train_mask)
+        val_mask = torch.BoolTensor(data.val_mask)
+        test_mask = torch.BoolTensor(data.test_mask)
+    else:
+        train_mask = torch.ByteTensor(data.train_mask)
+        val_mask = torch.ByteTensor(data.val_mask)
+        test_mask = torch.ByteTensor(data.test_mask)
     in_feats = features.shape[1]
     n_classes = data.num_labels
     n_edges = data.graph.number_of_edges()