diff --git a/paddle/fluid/eager/to_static/run_program_op_func.h b/paddle/fluid/eager/to_static/run_program_op_func.h
index 277319bc700b652855576db248463b424846e2e9..416739bbbb177a9c626844b8e80c06e873101d83 100644
--- a/paddle/fluid/eager/to_static/run_program_op_func.h
+++ b/paddle/fluid/eager/to_static/run_program_op_func.h
@@ -57,7 +57,6 @@ inline void run_program_dygraph_function(
     auto grad_node = std::make_shared<GradNodeRunProgram>(1, 2);
 
     grad_node->SetFwdOutNames(out_names);
-    grad_node->SetOut(out);
     // Set Attributes
     grad_node->SetAttrMap(attrs);
     // Set TensorWrappers
diff --git a/paddle/fluid/eager/to_static/run_program_op_node.h b/paddle/fluid/eager/to_static/run_program_op_node.h
index 3c2fa59cf1b163b568759c74ee5a9f1868961901..8d2dd38113c94cb7c26231bc63ed58aec304d544 100644
--- a/paddle/fluid/eager/to_static/run_program_op_node.h
+++ b/paddle/fluid/eager/to_static/run_program_op_node.h
@@ -362,13 +362,16 @@ class GradNodeRunProgram : public egr::GradNodeBase {
       std::vector<std::vector<paddle::experimental::Tensor>> &grads,  // NOLINT
       bool create_graph) override {
     VLOG(3) << "Running Eager Backward Node: GradNodeRunProgram";
-    PADDLE_ENFORCE_EQ(
-        grads.size(), 1,
-        paddle::platform::errors::InvalidArgument(
-            "The out_grads.size() of RunProgramGradOp should be equal to 1."));
+    std::vector<std::vector<paddle::experimental::Tensor>> hooked_grads =
+        GradNodeRunProgram::ApplyGradientHooks(grads);
+    PADDLE_ENFORCE_EQ(hooked_grads.size(), 1,
+                      paddle::platform::errors::InvalidArgument(
+                          "The hooked_grads.size() of RunProgramGradOp should "
+                          "be equal to 1."));
 
-    egr::EagerUtils::FillZeroForEmptyGradInputs(&grads, this->InputMeta());
-    VLOG(3) << "out_grads[0].size() : " << grads[0].size();
+    egr::EagerUtils::FillZeroForEmptyGradInputs(&hooked_grads,
+                                                this->InputMeta());
+    VLOG(3) << "hooked_grads[0].size() : " << hooked_grads[0].size();
     std::vector<paddle::experimental::Tensor> x_grad;
     std::vector<paddle::experimental::Tensor> params_grad;
     ConstructXGradTensors(x_, &x_grad);
@@ -382,21 +385,15 @@ class GradNodeRunProgram : public egr::GradNodeBase {
       params_grad_ptr.emplace_back(&i);
     }
 
-    PADDLE_ENFORCE_EQ(
-        grads[0].size(), fwd_out_names_.size(),
-        paddle::platform::errors::InvalidArgument(
-            "The grads[0].size() and fwd_out_names_.size() should be equal."));
+    PADDLE_ENFORCE_EQ(hooked_grads[0].size(), fwd_out_names_.size(),
+                      paddle::platform::errors::InvalidArgument(
+                          "The hooked_grads[0].size() and "
+                          "fwd_out_names_.size() should be equal."));
     for (size_t i = 0; i < fwd_out_names_.size(); ++i) {
-      auto &out_grad = egr::EagerUtils::unsafe_autograd_meta(*out_[i])->Grad();
-      const_cast<paddle::experimental::Tensor &>(out_grad).set_impl(
-          grads[0][i].impl());
-
-      const_cast<paddle::experimental::Tensor &>(grads[0][i])
-          .set_name(fwd_out_names_[i] + "@GRAD");
+      hooked_grads[0][i].set_name(fwd_out_names_[i] + "@GRAD");
     }
-
-    RunProgramGradAPI(x_, params_, grads[0], step_scope_, attrs_, x_grad_ptr,
-                      params_grad_ptr);
+    RunProgramGradAPI(x_, params_, hooked_grads[0], step_scope_, attrs_,
+                      x_grad_ptr, params_grad_ptr);
     VLOG(3) << "End Eager Backward Node: GradNodeRunProgram";
     return {x_grad, params_grad};
   }
@@ -428,10 +425,6 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     fwd_out_names_ = out_names;
   }
 
-  void SetOut(const std::vector<paddle::experimental::Tensor *> &out) {
-    out_ = out;
-  }
-
  protected:
   void ConstructXGradTensors(
       const std::vector<paddle::experimental::Tensor> &x,
@@ -454,6 +447,9 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     for (auto &t : param) {
       auto t_meta = egr::EagerUtils::unsafe_autograd_meta(t);
       auto t_grad = egr::EagerUtils::unsafe_autograd_meta(t)->Grad();
+      // In eager mode, the number of param_grad should be the same as
+      // param, so here an empty Tensor is added for the param with
+      // stop_gradient=True
       if (t_meta->StopGradient()) {
         param_grad->emplace_back();
       } else if (t_grad.is_dense_tensor()) {
@@ -472,7 +468,6 @@ class GradNodeRunProgram : public egr::GradNodeBase {
   std::vector<paddle::framework::Scope *> step_scope_;
 
   std::vector<std::string> fwd_out_names_;
-  std::vector<paddle::experimental::Tensor *> out_;
 
   // Attribute Map
   paddle::framework::AttributeMap attrs_;