diff --git a/paddle/operators/recurrent_op_test.cc b/paddle/operators/recurrent_op_test.cc
index 3fc2954ba1de0f76d5955114f131bd76568b321f..d950296c4a6d0c9bd8b0d0f763b093b1c5011f0b 100644
--- a/paddle/operators/recurrent_op_test.cc
+++ b/paddle/operators/recurrent_op_test.cc
@@ -22,382 +22,233 @@
 #include "paddle/framework/tensor.h"
 #include "paddle/operators/net_op.h"
 
-TEST(rnn, bad) { ASSERT_TRUE(false); }
+namespace paddle {
+namespace operators {
 
-// namespace paddle {
-// namespace operators {
-//
+using namespace paddle::framework;
 // using framework::make_ddim;
 // using framework::DDim;
-//
-// class RecurrentOpTest : public ::testing::Test {
-// protected:
-//  virtual void SetUp() override {
-//    CreateGlobalVariables();
-//    CreateStepNet();
-//    CreateRNNOp();
-//  }
-//
-//  virtual void TearDown() override {}
-//
-//  void CreateGlobalVariables() {
-//    // create input, and init content
-//    LOG(INFO) << "create global variable x";
-//    for (auto inlink : std::vector<std::string>{"x", "x0", "x1", "h"}) {
-//      Variable* x = scope_.NewVar(inlink);
-//      DDim dims = make_ddim(std::vector<int>{
-//          10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
-//      x->GetMutable<Tensor>()->mutable_data<float>(dims,
-//      platform::CPUPlace());
-//    }
-//    // create output alias just for test
-//    for (auto inlink : std::vector<std::string>{"h@alias"}) {
-//      Variable* x = scope_.NewVar(inlink);
-//      DDim dims =
-//          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/});
-//      x->GetMutable<Tensor>()->mutable_data<float>(dims,
-//      platform::CPUPlace());
-//    }
-//
-//    LOG(INFO) << "create global variable w";
-//    Variable* w = scope_.NewVar("rnn/w");
-//    w->GetMutable<Tensor>()->mutable_data<float>(
-//        make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
-//
-//    for (auto boot : std::vector<std::string>{"h_boot"}) {
-//      LOG(INFO) << "create global variable " << boot;
-//      Variable* h_boot = scope_.NewVar(boot);
-//      h_boot->GetMutable<Tensor>()->mutable_data<float>(
-//          make_ddim(std::vector<int>{20 /*batch size*/, 30 /*input dim*/}),
-//          platform::CPUPlace());
-//    }
-//
-//    LOG(INFO) << "create variable step_scopes";
-//    scope_.NewVar("step_scopes");
-//
-//    LOG(INFO) << "create variable h";
-//    scope_.NewVar("h");
-//  }
-//
-//  void CreateRNNOp() {
-//    framework::OpDesc op_desc;
-//
-//    op_desc.set_type("recurrent_op");
-//    // inlinks 0
-//    op_desc.add_inputs("x");
-//    op_desc.add_inputs("x0");
-//    op_desc.add_inputs("x1");
-//    // boot_memories 3
-//    op_desc.add_inputs("h_boot");
-//    // step net 5
-//    op_desc.add_inputs("step_net");
-//    // outlinks 6
-//    op_desc.add_outputs("h");
-//    // step scopes 7
-//    op_desc.add_outputs("step_scopes");
-//
-//    auto _input_format = std::vector<int>{
-//        0,  // in_link
-//        3,  // memories
-//        4   // step_net
-//    };
-//    auto input_format = op_desc.add_attrs();
-//    input_format->set_name("input_format");
-//    input_format->set_type(paddle::framework::AttrType::INTS);
-//    for (auto i : _input_format) {
-//      input_format->add_ints(i);
-//    }
-//
-//    auto output_format = op_desc.add_attrs();
-//    output_format->set_name("output_format");
-//    output_format->set_type(paddle::framework::AttrType::INTS);
-//    for (auto i : std::vector<int>{0, 1, 2}) {
-//      output_format->add_ints(i);
-//    }
-//
-//    auto inlink_alias = op_desc.add_attrs();
-//    inlink_alias->set_name("inlink_alias");
-//    inlink_alias->set_type(paddle::framework::AttrType::STRINGS);
-//
-//    auto outlink_alias = op_desc.add_attrs();
-//    outlink_alias->set_name("outlink_alias");
-//    outlink_alias->set_type(paddle::framework::AttrType::STRINGS);
-//
-//    auto pre_memories = op_desc.add_attrs();
-//    pre_memories->set_name("pre_memories");
-//    pre_memories->set_type(paddle::framework::AttrType::STRINGS);
-//
-//    auto memories = op_desc.add_attrs();
-//    memories->set_name("memories");
-//    memories->set_type(paddle::framework::AttrType::STRINGS);
-//
-//    // create inlink_alias
-//    for (const auto& item :
-//         std::vector<std::string>{"x@alias", "x0@alias", "x1@alias"}) {
-//      inlink_alias->add_strings(item);
-//    }
-//    // pre memories
-//    for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
-//      pre_memories->add_strings(item);
-//    }
-//    // memories
-//    for (const auto& item : std::vector<std::string>{"rnn/h"}) {
-//      memories->add_strings(item);
-//    }
-//    // output alias
-//    for (const auto& item : std::vector<std::string>{"h@alias"}) {
-//      outlink_alias->add_strings(item);
-//    }
-//
-//    rnn_op_ = OpRegistry::CreateOp(op_desc);
-//
-//    LOG(INFO) << "rnn_op finish init";
-//  }
-//
-//  void CreateStepNet() {
-//    LOG(INFO) << "create variable step_net";
-//    Variable* var = scope_.NewVar("step_net");
-//    auto net = var->GetMutable<NetOp>();
-//    net->AddOp(
-//        OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
-//
-//    net->AddOp(
-//        OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
-//    net->CompleteAddOp();
-//  }
-//
-//  // father scope
-//  Scope scope_;
-//  std::shared_ptr<OperatorBase> rnn_op_;
-//};
-//
-// TEST_F(RecurrentOpTest, Run) {
-//  platform::CPUDeviceContext ctx;
-//  rnn_op_->InferShape(scope_);
-//  rnn_op_->Run(scope_, ctx);
-//}
-//
-// class RecurrentGradientAlgorithmTest : public ::testing::Test {
-// protected:
-//  virtual void SetUp() override {
-//    CreateGlobalVariables();
-//    CreateStepScopes();
-//    CreateStepNet();
-//    CreateRNNGradientAlgorithm();
-//
-//    // segment inputs
-//    SegmentInputs();
-//    // link forward memories
-//    LinkeMemories();
-//  }
-//
-//  virtual void TearDown() override {}
-//
-//  void CreateGlobalVariables() {
-//    // inputs: x
-//    LOG(INFO) << "create global variable x";
-//    Variable* x = scope_.NewVar("x");
-//    DDim dims =
-//        make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
-//    x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
-//    // inputs: h_boot
-//    LOG(INFO) << "create global variable h_boot";
-//    Variable* h_boot = scope_.NewVar("h_boot");
-//    h_boot->GetMutable<Tensor>()->mutable_data<float>(
-//        make_ddim({20 /*batch size*/, 30 /*input dim*/}),
-//        platform::CPUPlace());
-//    // inputs: w
-//    LOG(INFO) << "create global variable w";
-//    Variable* w = scope_.NewVar("rnn/w");
-//    w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
-//                                                 platform::CPUPlace());
-//    // inputs: h_grad
-//    LOG(INFO) << "create variable h_grad";
-//    Variable* dh = scope_.NewVar("h_grad");
-//    dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
-//                                                  platform::CPUPlace());
-//    // inputs: step_scopes
-//    LOG(INFO) << "create variable step_scopes";
-//    scope_.NewVar("step_scopes");
-//    // inputs: step_net
-//    LOG(INFO) << "create variable step_net";
-//    scope_.NewVar("step_net");
-//    // outputs: w_grad
-//    LOG(INFO) << "create global variable w_grad";
-//    scope_.NewVar("rnn/w_grad");
-//    // outputs: x_grad
-//    LOG(INFO) << "create global variable x_grad";
-//    scope_.NewVar("x_grad");
-//    // outputs: h_boot_grad
-//    LOG(INFO) << "create global variable h_boot_grad";
-//    scope_.NewVar("h_boot_grad");
-//  }
-//
-//  void CreateStepScopes() {
-//    auto step_scopes =
-//        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-//    for (int i = 0; i < 10; ++i) {
-//      auto& scope = scope_.NewScope();
-//      auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
-//      pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
-//      auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
-//      tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
-//
-//      // for unit test of ConcatOutputs
-//      auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
-//      xg->mutable_data<float>({20, 30}, platform::CPUPlace());
-//
-//      step_scopes->emplace_back(&scope);
-//    }
-//
-//    // last time step
-//    auto g =
-//    (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
-//    g->mutable_data<float>({20, 30}, platform::CPUPlace());
-//  }
-//
-//  void CreateRNNGradientAlgorithm() {
-//    std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
-//    arg->step_net = "step_net";
-//    arg->step_scopes = "step_scopes";
-//    rnn::Link inlink;
-//    inlink.external = "h_grad";
-//    inlink.internal = "rnn/h_grad";
-//    arg->inlinks = std::vector<rnn::Link>{inlink};
-//
-//    rnn::Link outlink;
-//    outlink.external = "x_grad";
-//    outlink.internal = "rnn/x_grad";
-//    arg->outlinks = std::vector<rnn::Link>{outlink};
-//
-//    rnn::MemoryAttr mem_attr;
-//    mem_attr.pre_var = "rnn/h_pre_grad";
-//    mem_attr.var = "rnn/h_grad";
-//    mem_attr.boot_var = "h_boot_grad";
-//    arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
-//
-//    rnn_grad_algo_.Init(std::move(arg));
-//  }
-//
-//  void CreateStepNet() {
-//    LOG(INFO) << "create variable step_net";
-//    Variable* var = scope_.NewVar("step_net");
-//    auto net = var->GetMutable<NetOp>();
-//    net->AddOp(OpRegistry::CreateOp("mul", {"rnn/h_pre", "rnn/w",
-//    "rnn/s_grad"},
-//                                    {"rnn/h_pre_grad", "rnn/w_grad"}, {}));
-//
-//    net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
-//                                    {"rnn/x_grad", "rnn/s_grad"}, {}));
-//    net->CompleteAddOp();
-//  }
-//
-//  void SegmentInputs() {
-//    LOG(INFO) << "segment inputs";
-//    std::vector<std::string> inlinks = {"x"};
-//    std::vector<std::string> inlinks_alias = {"rnn/x"};
-//
-//    rnn::Link inlink;
-//    inlink.external = "x";
-//    inlink.internal = "rnn/x";
-//    auto step_scopes =
-//        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-//    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
-//                       true /*infer_shape_mode*/);
-//  }
-//
-//  void LinkeMemories() {
-//    LOG(INFO) << "link memories";
-//    rnn::MemoryAttr mem_attr;
-//    mem_attr.pre_var = "rnn/h_pre";
-//    mem_attr.var = "rnn/h";
-//    mem_attr.boot_var = "boot_h";
-//    std::vector<rnn::MemoryAttr> memories;
-//    memories.push_back(mem_attr);
-//    auto step_scopes =
-//        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
-//    for (int i = 1; i < 10; ++i) {
-//      rnn::LinkMemories(*step_scopes, memories, i, -1,
-//                        true /*infer_shape_mode*/);
-//    }
-//  }
-//
-//  Scope scope_;
-//  RecurrentGradientAlgorithm rnn_grad_algo_;
-//};
-//
-//// TEST_F(RecurrentGradientAlgorithmTest, Run) {
-////   platform::CPUDeviceContext ctx;
-////   rnn_grad_algo_.Run(scope_, ctx);
-//// }
-//
-//}  // namespace operators
-//}  // namespace paddle
-//
-// TEST(RecurrentOp, LinkMemories) {
-//  using namespace paddle::framework;
-//  using namespace paddle::platform;
-//  using namespace paddle::operators;
-//
-//  // create and init step scopes
-//  size_t len = 10;
-//  std::vector<Scope*> step_scopes;
-//  for (size_t i = 0; i < len; ++i) {
-//    auto scope = new Scope();
-//    scope->NewVar("pre_h");
-//    auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
-//    float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
-//    for (size_t j = 0; j < 15 * 20; ++j) {
-//      data[j] = rand() * (1. / (double)RAND_MAX);
-//    }
-//    step_scopes.push_back(scope);
-//  }
-//
-//  // create MemoryAttr
-//  rnn::MemoryAttr mem_attr;
-//  mem_attr.pre_var = "pre_h";
-//  mem_attr.var = "h";
-//  mem_attr.boot_var = "boot_h";
-//  std::vector<rnn::MemoryAttr> memories;
-//  memories.push_back(mem_attr);
-//
-//  for (size_t i = 1; i < len; ++i) {
-//    rnn::LinkMemories(step_scopes, memories, i, -1, false
-//    /*infer_shape_mode*/);
-//  }
-//  // check
-//  for (size_t i = 0; i < len - 1; ++i) {
-//    const float* a =
-//        step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
-//    const float* b = step_scopes[i + 1]
-//                         ->FindVar("pre_h")
-//                         ->GetMutable<Tensor>()
-//                         ->data<float>();
-//    for (size_t j = 0; j < 15 * 20; ++j) {
-//      ASSERT_FLOAT_EQ(a[j], b[j]);
-//    }
-//  }
-//
-//  for (int i = len - 2; i >= 0; --i) {
-//    rnn::LinkMemories(step_scopes, memories, i, 1, false
-//    /*infer_shape_mode*/);
-//  }
-//  // check
-//  for (int i = len - 2; i >= 0; --i) {
-//    const float* a =
-//        step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
-//    const float* b =
-//        step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
-//    for (size_t j = 0; j < 15 * 20; ++j) {
-//      ASSERT_FLOAT_EQ(a[j], b[j]);
-//    }
-//  }
-//
-//  for (auto s : step_scopes) {
-//    delete s;
-//  }
-//}
-//
-// USE_OP(add_two);
-// USE_OP(mul);
-// USE_OP_WITHOUT_KERNEL(recurrent_op);
+
+class RecurrentGradientAlgorithmTest : public ::testing::Test {
+ protected:
+  virtual void SetUp() override {
+    CreateGlobalVariables();
+    CreateStepScopes();
+    CreateStepNet();
+    CreateRNNGradientAlgorithm();
+
+    // segment inputs
+    SegmentInputs();
+    // link forward memories
+    LinkeMemories();
+  }
+
+  virtual void TearDown() override {}
+
+  void CreateGlobalVariables() {
+    // inputs: x
+    LOG(INFO) << "create global variable x";
+    Variable* x = scope_.NewVar("x");
+    DDim dims =
+        make_ddim({10 /*sent size*/, 20 /*batch size*/, 30 /*input dim*/});
+    x->GetMutable<Tensor>()->mutable_data<float>(dims, platform::CPUPlace());
+    // inputs: h_boot
+    LOG(INFO) << "create global variable h_boot";
+    Variable* h_boot = scope_.NewVar("h_boot");
+    h_boot->GetMutable<Tensor>()->mutable_data<float>(
+        make_ddim({20 /*batch size*/, 30 /*input dim*/}), platform::CPUPlace());
+    // inputs: w
+    LOG(INFO) << "create global variable w";
+    Variable* w = scope_.NewVar("rnn/w");
+    w->GetMutable<Tensor>()->mutable_data<float>(make_ddim({30, 30}),
+                                                 platform::CPUPlace());
+    // inputs: h_grad
+    LOG(INFO) << "create variable h_grad";
+    Variable* dh = scope_.NewVar("h_grad");
+    dh->GetMutable<Tensor>()->mutable_data<float>(make_ddim({10, 20, 30}),
+                                                  platform::CPUPlace());
+    // inputs: step_scopes
+    LOG(INFO) << "create variable step_scopes";
+    scope_.NewVar("step_scopes");
+    // inputs: step_net
+    LOG(INFO) << "create variable step_net";
+    scope_.NewVar("step_net");
+    // outputs: w_grad
+    LOG(INFO) << "create global variable w_grad";
+    scope_.NewVar("rnn/w_grad");
+    // outputs: x_grad
+    LOG(INFO) << "create global variable x_grad";
+    scope_.NewVar("x_grad");
+    // outputs: h_boot_grad
+    LOG(INFO) << "create global variable h_boot_grad";
+    scope_.NewVar("h_boot_grad");
+  }
+
+  void CreateStepScopes() {
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
+    for (int i = 0; i < 10; ++i) {
+      auto& scope = scope_.NewScope();
+      auto pre_t = scope.NewVar("rnn/pre_h")->GetMutable<Tensor>();
+      pre_t->mutable_data<float>({20, 30}, platform::CPUPlace());
+      auto tensor = scope.NewVar("rnn/h")->GetMutable<Tensor>();
+      tensor->mutable_data<float>({20, 30}, platform::CPUPlace());
+
+      // for unit test of ConcatOutputs
+      auto xg = scope.NewVar("rnn/x_grad")->GetMutable<Tensor>();
+      xg->mutable_data<float>({20, 30}, platform::CPUPlace());
+
+      step_scopes->emplace_back(&scope);
+    }
+
+    // last time step
+    auto g = (*step_scopes)[9]->NewVar("rnn/h_pre_grad")->GetMutable<Tensor>();
+    g->mutable_data<float>({20, 30}, platform::CPUPlace());
+  }
+
+  void CreateRNNGradientAlgorithm() {
+    std::unique_ptr<rnn::Argument> arg(new rnn::Argument());
+    arg->step_net = "step_net";
+    arg->step_scopes = "step_scopes";
+    rnn::Link inlink;
+    inlink.external = "h_grad";
+    inlink.internal = "rnn/h_grad";
+    arg->inlinks = std::vector<rnn::Link>{inlink};
+
+    rnn::Link outlink;
+    outlink.external = "x_grad";
+    outlink.internal = "rnn/x_grad";
+    arg->outlinks = std::vector<rnn::Link>{outlink};
+
+    rnn::MemoryAttr mem_attr;
+    mem_attr.pre_var = "rnn/h_pre_grad";
+    mem_attr.var = "rnn/h_grad";
+    mem_attr.boot_var = "h_boot_grad";
+    arg->memories = std::vector<rnn::MemoryAttr>{mem_attr};
+
+    rnn_grad_algo_.Init(std::move(arg));
+  }
+
+  void CreateStepNet() {
+    LOG(INFO) << "create variable step_net";
+    Variable* var = scope_.NewVar("step_net");
+    auto net = var->GetMutable<NetOp>();
+    // TODO(qingqing) modify backward op create for RNNOp unit test
+    // and the unit test will be removed to Python.
+    // net->AddOp(OpRegistry::CreateOp("mul", {"X", {"rnn/h_pre", "rnn/w",
+    //     "rnn/s_grad"}}, {"Y", {"rnn/h_pre_grad", "rnn/w_grad"}}, {}));
+
+    // net->AddOp(OpRegistry::CreateOp("add_two", {"X", {"rnn/h_grad"}},
+    //			    {"Y", {"rnn/x_grad"}}, {"Out", "rnn/s_grad"}}, {}));
+    net->CompleteAddOp();
+  }
+
+  void SegmentInputs() {
+    LOG(INFO) << "segment inputs";
+    std::vector<std::string> inlinks = {"x"};
+    std::vector<std::string> inlinks_alias = {"rnn/x"};
+
+    rnn::Link inlink;
+    inlink.external = "x";
+    inlink.internal = "rnn/x";
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
+    rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
+                       true /*infer_shape_mode*/);
+  }
+
+  void LinkeMemories() {
+    LOG(INFO) << "link memories";
+    rnn::MemoryAttr mem_attr;
+    mem_attr.pre_var = "rnn/h_pre";
+    mem_attr.var = "rnn/h";
+    mem_attr.boot_var = "boot_h";
+    std::vector<rnn::MemoryAttr> memories;
+    memories.push_back(mem_attr);
+    auto step_scopes =
+        scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
+    for (int i = 1; i < 10; ++i) {
+      rnn::LinkMemories(*step_scopes, memories, i, -1,
+                        true /*infer_shape_mode*/);
+    }
+  }
+
+  Scope scope_;
+  RecurrentGradientAlgorithm rnn_grad_algo_;
+};
+
+// TEST_F(RecurrentGradientAlgorithmTest, Run) {
+//   platform::CPUDeviceContext ctx;
+//   rnn_grad_algo_.Run(scope_, ctx);
+// }
+
+}  // namespace operators
+}  // namespace paddle
+
+TEST(RecurrentOp, LinkMemories) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+  using namespace paddle::operators;
+
+  // create and init step scopes
+  size_t len = 10;
+  std::vector<Scope*> step_scopes;
+  for (size_t i = 0; i < len; ++i) {
+    auto scope = new Scope();
+    scope->NewVar("pre_h");
+    auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
+    float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      data[j] = rand() * (1. / (double)RAND_MAX);
+    }
+    step_scopes.push_back(scope);
+  }
+
+  // create MemoryAttr
+  rnn::MemoryAttr mem_attr;
+  mem_attr.pre_var = "pre_h";
+  mem_attr.var = "h";
+  mem_attr.boot_var = "boot_h";
+  std::vector<rnn::MemoryAttr> memories;
+  memories.push_back(mem_attr);
+
+  for (size_t i = 1; i < len; ++i) {
+    rnn::LinkMemories(step_scopes, memories, i, -1, false
+                      /*infer_shape_mode*/);
+  }
+  // check
+  for (size_t i = 0; i < len - 1; ++i) {
+    const float* a =
+        step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
+    const float* b = step_scopes[i + 1]
+                         ->FindVar("pre_h")
+                         ->GetMutable<Tensor>()
+                         ->data<float>();
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
+    }
+  }
+
+  for (int i = len - 2; i >= 0; --i) {
+    rnn::LinkMemories(step_scopes, memories, i, 1, false
+                      /*infer_shape_mode*/);
+  }
+  // check
+  for (int i = len - 2; i >= 0; --i) {
+    const float* a =
+        step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
+    const float* b =
+        step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
+    for (size_t j = 0; j < 15 * 20; ++j) {
+      ASSERT_FLOAT_EQ(a[j], b[j]);
+    }
+  }
+
+  for (auto s : step_scopes) {
+    delete s;
+  }
+}
+
+USE_OP(add_two);
+USE_OP(mul);
+USE_OP_WITHOUT_KERNEL(recurrent_op);