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 f6b8e21cd8b17f..c767ad0b6106c7 100644
--- a/paddle/fluid/eager/to_static/run_program_op_func.h
+++ b/paddle/fluid/eager/to_static/run_program_op_func.h
@@ -20,9 +20,12 @@
 #include "paddle/fluid/eager/eager_tensor.h"
 #include "paddle/fluid/eager/to_static/run_program_op_node.h"
 #include "paddle/fluid/eager/utils.h"
+#include "paddle/fluid/framework/tensor_ref_array.h"
 #include "paddle/fluid/memory/allocation/allocator.h"
 #include "paddle/pir/include/core/block.h"
+#include "paddle/pir/include/core/builtin_type.h"
 #include "paddle/pir/include/core/value.h"
+#include "paddle/pir/include/dialect/control_flow/ir/cf_type.h"
 
 // Filter params without grads in global block. In this case, we will
 // tag its AutogradMeta with stop_gradient = True to avoid fault from
@@ -244,8 +247,9 @@ inline void pir_run_program_ad_func(
       trace_backward, &p_autograd_x, &p_autograd_params);
 
   // Create Middle Output for GradNode.
-  auto middle_size =
-      PADDLE_GET_CONST(std::vector<::pir::Value>, attrs.at("fm")).size();
+  auto middle_values =
+      PADDLE_GET_CONST(std::vector<::pir::Value>, attrs.at("fm"));
+  auto middle_size = middle_values.size();
   auto output_size =
       PADDLE_GET_CONST(std::vector<::pir::Value>, attrs.at("fo")).size();
   auto middles = std::vector<paddle::Tensor*>();
@@ -264,8 +268,14 @@ inline void pir_run_program_ad_func(
     grad_node->GetMiddle().resize(middle_size);
     grad_node->GetOutputs().resize(output_size);
     for (size_t i = 0; i < middle_size; ++i) {
-      grad_node->GetMiddle()[i] =
-          paddle::Tensor(std::make_shared<phi::DenseTensor>());
+      auto middle_value = middle_values[i];
+      if (middle_value.type().isa<pir::DenseTensorType>()) {
+        grad_node->GetMiddle()[i] =
+            paddle::Tensor(std::make_shared<phi::DenseTensor>());
+      } else if (middle_value.type().isa<pir::OutletType>()) {
+        grad_node->GetMiddle()[i] = paddle::Tensor(
+            std::make_shared<paddle::framework::VariableRefArray>());
+      }
       middles.push_back(&grad_node->GetMiddle()[i]);
     }
 
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 fdebfbb1e3771f..da04f129c01aa7 100644
--- a/paddle/fluid/eager/to_static/run_program_op_node.h
+++ b/paddle/fluid/eager/to_static/run_program_op_node.h
@@ -19,6 +19,7 @@
 #include "paddle/fluid/eager/tensor_wrapper.h"
 #include "paddle/fluid/framework/executor_cache.h"
 #include "paddle/fluid/framework/new_executor/interpretercore.h"
+#include "paddle/fluid/framework/tensor_ref_array.h"
 #include "paddle/fluid/framework/variable_helper.h"
 #include "paddle/fluid/ir_adaptor/translator/program_translator.h"
 #include "paddle/fluid/operators/run_program_op.h"
@@ -120,10 +121,20 @@ static void CheckOutputVarStatus(const paddle::framework::Variable &src_var,
                           "RunProgram(Grad)Op's internal scope holds "
                           "wrong type. Expect type is SelectedRows",
                           name));
+  } else if (paddle::framework::VariableRefArray::classof(
+                 dst_tensor.impl().get())) {
+    auto &src_tensor = src_var.Get<paddle::framework::VariableRefArray>();
+    PADDLE_ENFORCE_EQ(paddle::framework::VariableRefArray::classof(&src_tensor),
+                      true,
+                      paddle::platform::errors::InvalidArgument(
+                          "The output tensor %s get from "
+                          "RunProgram(Grad)Op's internal scope holds "
+                          "wrong type. Expect type is VariableRefArray",
+                          name));
   } else {
     PADDLE_THROW(paddle::platform::errors::InvalidArgument(
         "The RunProgram(Grad)Op only support output "
-        "variable of type LoDTensor or SelectedRows",
+        "variable of type DenseTensor, SelectedRows or VariableRefArray",
         name));
   }
 }
@@ -320,6 +331,17 @@ static void ShareTensorsFromScopeByValue(
       auto *dst_tensor = const_cast<phi::SelectedRows *>(
           dynamic_cast<const phi::SelectedRows *>(tensors[i]->impl().get()));
       *dst_tensor = src_tensor;
+    } else if (var->IsType<paddle::framework::VariableRefArray>()) {
+      auto &src_tensor = var->Get<paddle::framework::VariableRefArray>();
+      auto *dst_tensor = const_cast<paddle::framework::VariableRefArray *>(
+          dynamic_cast<const paddle::framework::VariableRefArray *>(
+              tensors[i]->impl().get()));
+      *dst_tensor = src_tensor;
+    } else {
+      PADDLE_THROW(paddle::platform::errors::InvalidArgument(
+          "The RunProgram(Grad)Op only support output "
+          "variable of type DenseTensor, SelectedRows or VariableRefArray",
+          name));
     }
   }
 }
diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc
index 35c19c8f00c76e..a24afc3585a717 100644
--- a/paddle/fluid/pybind/pir.cc
+++ b/paddle/fluid/pybind/pir.cc
@@ -970,11 +970,14 @@ AnalysisMiddleVariable(const Program &program,
       program.block(),
       forward_range,
       [&middle_values, &backward_inputs, &x_or_param](Operation *op) {
-        for (auto &t : op->results()) {
-          auto v = Value(t.Value::impl());
-          if (backward_inputs.count(v) && !x_or_param.count(v))
-            middle_values.push_back(v);
-        }
+        pir::Walk(op, [&](Operation *inner_op) {
+          for (auto &t : inner_op->results()) {
+            auto v = Value(t.Value::impl());
+            if (backward_inputs.count(v) && !x_or_param.count(v)) {
+              middle_values.push_back(v);
+            }
+          }
+        });
       });
   return std::make_pair(middle_values, backward_inputs);
 }
diff --git a/test/dygraph_to_static/test_ifelse.py b/test/dygraph_to_static/test_ifelse.py
index a05f3d07510e9c..fef4c48d495125 100644
--- a/test/dygraph_to_static/test_ifelse.py
+++ b/test/dygraph_to_static/test_ifelse.py
@@ -23,7 +23,6 @@
     enable_to_static_guard,
     test_ast_only,
     test_legacy_and_pt_and_pir,
-    test_legacy_only,
     test_pir_only,
 )
 from ifelse_simple_func import (
@@ -338,7 +337,7 @@ def _run(self, to_static=False):
             ret = net(x_v)
             return ret.numpy()
 
-    @test_legacy_only
+    @test_legacy_and_pt_and_pir
     def test_ast_to_func(self):
         self.assertTrue((self._run_dygraph() == self._run_static()).all())