diff --git a/mindspore/ccsrc/pynative/pynative_execute.cc b/mindspore/ccsrc/pynative/pynative_execute.cc
index d72f89399e..38a3e2a5f5 100644
--- a/mindspore/ccsrc/pynative/pynative_execute.cc
+++ b/mindspore/ccsrc/pynative/pynative_execute.cc
@@ -351,13 +351,13 @@ py::object RunOpInVM(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
     for (size_t i = 0; i < op_inputs.size(); i++) {
       py::object input = op_inputs[i];
       if (py::hasattr(input, "__parameter__")) {
-        result[i] = py::getattr(input, "data");
-      } else {
-        auto tensor = py::cast<tensor::TensorPtr>(input);
-        auto new_tensor = std::make_shared<tensor::Tensor>(tensor->data_type(), tensor->shape(), tensor->data_ptr());
-        new_tensor->set_device_address(tensor->device_address());
-        new_tensor->set_dirty(tensor->is_dirty());
-        result[i] = new_tensor;
+        input = py::getattr(input, "data");
+      }
+      auto tensor = py::cast<tensor::TensorPtr>(input);
+      auto new_tensor = std::make_shared<tensor::Tensor>(tensor->data_type(), tensor->shape(), tensor->data_ptr());
+      new_tensor->set_device_address(tensor->device_address());
+      new_tensor->set_dirty(tensor->is_dirty());
+      result[i] = new_tensor;
       }
     }
     *status = PYNATIVE_SUCCESS;
diff --git a/mindspore/ops/composite/base.py b/mindspore/ops/composite/base.py
index 632efa0cc1..0f28d9572f 100644
--- a/mindspore/ops/composite/base.py
+++ b/mindspore/ops/composite/base.py
@@ -120,6 +120,9 @@ class GradOperation(GradOperation_):
         """ Pynative forward run to build grad graph. """
         if self.sens_param:
             args = args[:-1]
+        for arg in args:
+            if not isinstance(arg, Tensor):
+                raise TypeError("grad inputs should be tensor in pynative mode")
         if isinstance(fn, FunctionType):
             _pynative_exec.set_grad_flag(True)
             _pynative_exec.new_graph(fn, *args)
@@ -150,9 +153,6 @@ class GradOperation(GradOperation_):
             else:
                 @_wrap_func
                 def after_grad(*args):
-                    for arg in args:
-                        if not isinstance(arg, Tensor):
-                            raise TypeError("grad inputs should be tensor in pynative mode")
                     self._pynative_forward_run(args, fn)
                     _pynative_exec.grad(grad_, fn, weights, *args)
                     out = _pynative_exec(*args)