diff --git a/test/test_mobile_optimizer.py b/test/test_mobile_optimizer.py
index eae6175fb024..897c76d8c814 100644
--- a/test/test_mobile_optimizer.py
+++ b/test/test_mobile_optimizer.py
@@ -100,8 +100,8 @@ def forward(self, x):
         torch.testing.assert_allclose(initial_result, optimized_result, rtol=1e-2, atol=1e-3)
 
 
-        optimization_blacklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
-        optimized_scripted_model_no_prepack = optimize_for_mobile(scripted_model, optimization_blacklist_no_prepack)
+        optimization_blocklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
+        optimized_scripted_model_no_prepack = optimize_for_mobile(scripted_model, optimization_blocklist_no_prepack)
         optimized_result_no_prepack = optimized_scripted_model_no_prepack(input_data)
 
         FileCheck().check_count("Tensor = aten::conv2d", 1, exactly=True) \
@@ -118,14 +118,14 @@ def forward(self, x):
         FileCheck().check_count("prim::CallMethod[name=\"forward\"]", 2, exactly=True) \
                    .run(str(get_forward(bn_scripted_module._c).graph))
 
-        optimization_blacklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
-        bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blacklist_no_prepack)
+        optimization_blocklist_no_prepack = {MobileOptimizerType.INSERT_FOLD_PREPACK_OPS}
+        bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blocklist_no_prepack)
         self.assertEqual(len(torch.jit.export_opnames(bn_fold_scripted_module)), 1)
         bn_input = torch.rand(1, 1, 6, 6)
         torch.testing.assert_allclose(bn_scripted_module(bn_input), bn_fold_scripted_module(bn_input), rtol=1e-2, atol=1e-3)
 
-        optimization_blacklist_no_fold_bn = {MobileOptimizerType.CONV_BN_FUSION}
-        no_bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blacklist_no_fold_bn)
+        optimization_blocklist_no_fold_bn = {MobileOptimizerType.CONV_BN_FUSION}
+        no_bn_fold_scripted_module = optimize_for_mobile(bn_scripted_module, optimization_blocklist_no_fold_bn)
         FileCheck().check_count("aten::batch_norm", 1, exactly=True) \
                    .run(str(get_forward_graph(no_bn_fold_scripted_module._c)))
         bn_input = torch.rand(1, 1, 6, 6)