fix bug of RefNode output

mindspore/ccsrc/runtime/framework/actor/data_prepare_actor.cc

@@ -180,9 +180,14 @@ void UpdateRefNodeOutputDeviceAddress(const KernelGraphPtr &graph) {
     // Just compare shared_ptr of two DeviceAddress.
     // The ptr of DeviceAddress may still be nullptr.
     if (input_addr != ref_node_output_addr) {
-      // The output of RefNode will not be used by subsequent Node.
+      // AnfAlgo::SetOutputAddr cannot update the device_address of frontend Tensor
-      // So update the DeviceAddress of the kernel directly instead of updating the ptr of the DeviceAddress.
+      // if the output of RefNode is used by subsequent nodes.
-      AnfAlgo::SetOutputAddr(input_addr, output_index, ref_node.get());
+      // Because the frontend Tensor is copied from backend Tensor and the shared_ptr of Tensor is different.
+      if (input_addr->GetMutablePtr() == nullptr) {
+        AnfAlgo::SetOutputAddr(input_addr, output_index, ref_node.get());
+      } else {
+        ref_node_output_addr->set_ptr(input_addr->GetMutablePtr());
+      }
     }
   }
 }

tests/st/pynative/mindrt/test_refnode.py

@@ -0,0 +1,37 @@
+import pytest
+import numpy as np
+from mindspore import ops, Tensor, context
+from mindspore.common.parameter import Parameter
+from mindspore.nn import Cell
+
+
+class AssignNet(Cell):
+    def __init__(self, input_variable):
+        super(AssignNet, self).__init__()
+        self.op = ops.Assign()
+        self.input_data = input_variable
+
+    def construct(self, input_x):
+        return self.op(self.input_data, input_x)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_assign_as_output():
+    """
+    Feature: PyNative MindRT
+    Description: Test PyNative MindRT RefNode.
+    Expectation: No exception.
+    """
+
+    np.random.seed(0)
+    input_np = np.random.randn(5, 5).astype(dtype=np.int32)
+    context.set_context(mode=context.PYNATIVE_MODE)
+    input_variable = Parameter(Tensor(np.random.randn(5, 5).astype(dtype=np.float32)))
+    input_x = Tensor(input_np)
+    net = AssignNet(input_variable)
+    out = net(input_x)
+    assert input_np.all() == out.asnumpy().astype(dtype=np.int32).all()