diff --git a/paddle/fluid/inference/tensorrt/test_tensorrt.cc b/paddle/fluid/inference/tensorrt/test_tensorrt.cc
index 36a25e27d78f5b6406fcc0d908018dd81d010a5f..2f5b75c1020041f350f694bbe249da1982bca5fb 100644
--- a/paddle/fluid/inference/tensorrt/test_tensorrt.cc
+++ b/paddle/fluid/inference/tensorrt/test_tensorrt.cc
@@ -16,13 +16,15 @@ limitations under the License. */
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 #include "NvInfer.h"
+#include "paddle/fluid/inference/tensorrt/helper.h"
 #include "paddle/fluid/platform/dynload/tensorrt.h"
 
 namespace dy = paddle::platform::dynload;
 
 class Logger : public nvinfer1::ILogger {
  public:
-  void log(nvinfer1::ILogger::Severity severity, const char* msg) override {
+  void log(nvinfer1::ILogger::Severity severity,
+           const char* msg) TRT_NOEXCEPT override {
     switch (severity) {
       case Severity::kINFO:
         LOG(INFO) << msg;
@@ -74,10 +76,11 @@ nvinfer1::IHostMemory* CreateNetwork() {
   Logger logger;
   // Create the engine.
   nvinfer1::IBuilder* builder = createInferBuilder(&logger);
+  auto config = builder->createBuilderConfig();
   ScopedWeights weights(2.);
   ScopedWeights bias(3.);
 
-  nvinfer1::INetworkDefinition* network = builder->createNetwork();
+  nvinfer1::INetworkDefinition* network = builder->createNetworkV2(0U);
   // Add the input
   auto input = network->addInput(kInputTensor, nvinfer1::DataType::kFLOAT,
                                  nvinfer1::Dims3{1, 1, 1});
@@ -91,8 +94,8 @@ nvinfer1::IHostMemory* CreateNetwork() {
   network->markOutput(*output);
   // Build the engine.
   builder->setMaxBatchSize(1);
-  builder->setMaxWorkspaceSize(1 << 10);
-  auto engine = builder->buildCudaEngine(*network);
+  config->setMaxWorkspaceSize(1 << 10);
+  auto engine = builder->buildEngineWithConfig(*network, *config);
   EXPECT_NE(engine, nullptr);
   // Serialize the engine to create a model, then close.
   nvinfer1::IHostMemory* model = engine->serialize();