diff --git a/lite/api/model_test.cc b/lite/api/model_test.cc
index 825d4f51ed8a6e2b4690657a766d9c9c5a1a2857..4db08f70047c76cd9cdda7fa278380ec679250af 100644
--- a/lite/api/model_test.cc
+++ b/lite/api/model_test.cc
@@ -37,6 +37,7 @@ void OutputOptModel(const std::string& load_model_dir,
   config.set_valid_places({
       Place{TARGET(kX86), PRECISION(kFloat)},
       Place{TARGET(kARM), PRECISION(kFloat)},
+      Place{TARGET(kHost), PRECISION(kFloat)},
   });
   auto predictor = lite_api::CreatePaddlePredictor(config);
 
diff --git a/lite/core/mir/fusion/conv_elementwise_fuser.cc b/lite/core/mir/fusion/conv_elementwise_fuser.cc
index c3ab3e4c4ca9bd8d6a6eaaf82e40dcb06cf99ea9..abc78edda88e008945e9d184b02e5feef3e5a4b1 100644
--- a/lite/core/mir/fusion/conv_elementwise_fuser.cc
+++ b/lite/core/mir/fusion/conv_elementwise_fuser.cc
@@ -27,8 +27,10 @@ void ConvElementwiseFuser::BuildPattern() {
       VarNode("input")->assert_is_op_input(conv_type_, "Input")->AsInput();
   auto* filter =
       VarNode("filter")->assert_is_op_input(conv_type_, "Filter")->AsInput();
-  auto* bias =
-      VarNode("bias")->assert_is_op_input("elementwise_add", "Y")->AsInput();
+  auto* bias = VarNode("bias")
+                   ->assert_is_op_input("elementwise_add", "Y")
+                   ->AsInput()
+                   ->assert_is_persistable_var();
 
   // create op nodes
   auto* conv2d =