diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc
index 1a6fe7ded304e21796c946a85cbfc7a911977021..4730f6a4ec887a2ba8cbc6837a6f27c35b5f43dc 100644
--- a/paddle/fluid/framework/tensor_util.cc
+++ b/paddle/fluid/framework/tensor_util.cc
@@ -84,6 +84,12 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
   }
 #endif
 #ifdef PADDLE_WITH_CUDA
+  else if (platform::is_cuda_pinned_place(src_place) &&  // NOLINT
+           platform::is_cuda_pinned_place(dst_place)) {
+    memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
+                 BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
+                 size);
+  }
   else if (platform::is_cuda_pinned_place(src_place) &&  // NOLINT
            platform::is_cpu_place(dst_place)) {
     memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
@@ -285,6 +291,12 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
   }
 #endif
 #ifdef PADDLE_WITH_CUDA
+  else if (platform::is_cuda_pinned_place(src_place) &&  // NOLINT
+           platform::is_cuda_pinned_place(dst_place)) {
+    memory::Copy(BOOST_GET_CONST(platform::CUDAPinnedPlace, dst_place), dst_ptr,
+                 BOOST_GET_CONST(platform::CUDAPinnedPlace, src_place), src_ptr,
+                 size);
+  }
   else if (platform::is_cuda_pinned_place(src_place) &&  // NOLINT
            platform::is_cpu_place(dst_place)) {
     memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
diff --git a/paddle/fluid/operators/assign_op.h b/paddle/fluid/operators/assign_op.h
index ed4ba24a74bea2aaedbbef4908904e2696826290..001a18859618c3d9e7bd75867669850020666826 100644
--- a/paddle/fluid/operators/assign_op.h
+++ b/paddle/fluid/operators/assign_op.h
@@ -54,7 +54,7 @@ class AssignFunctor {
     out_rows.set_height(rows.height());
     auto &t = rows.value();
     auto *m = out_rows.mutable_value();
-    framework::TensorCopy(t, dev_ctx_.GetPlace(), dev_ctx_, m);
+    framework::TensorCopy(t, t.place(), m);
   }
 
   template <typename T>
@@ -70,7 +70,7 @@ class AssignFunctor {
                    framework::LoDTensor *out) const {
     if (lod_tensor.numel() == 0) return;
     auto &out_tensor = *out;
-    TensorCopy(lod_tensor, dev_ctx_.GetPlace(), dev_ctx_, &out_tensor);
+    TensorCopy(lod_tensor, lod_tensor.place(), &out_tensor);
     out_tensor.set_lod(lod_tensor.lod());
   }
 
diff --git a/paddle/fluid/pybind/imperative.cc b/paddle/fluid/pybind/imperative.cc
index 14e76ebd26304ee1c191f7fa265d8cc5c3fbca49..b5dad4034a5f34a9626886c5e2c0830e3c74b3ad 100644
--- a/paddle/fluid/pybind/imperative.cc
+++ b/paddle/fluid/pybind/imperative.cc
@@ -718,6 +718,54 @@ void BindImperative(py::module *m_ptr) {
                 loss.clear_gradient()
                 print("After clear_gradient {}".format(loss.grad))
       )DOC")
+      .def("clone",
+           [](std::shared_ptr<imperative::VarBase> &self) {
+             const auto &tensor = self->Var().Get<framework::LoDTensor>();
+             PADDLE_ENFORCE_EQ(
+                 tensor.IsInitialized(), true,
+                 platform::errors::InvalidArgument(
+                     "%s has not been initialized", self->Name()));
+             auto tracer = imperative::GetCurrentTracer();
+             auto new_var = std::make_shared<imperative::VarBase>(
+                 true, tracer->GenerateUniqueName(self->Name() + "_clone"));
+             framework::AttributeMap attrs;
+             imperative::NameVarBaseMap ins = {{"X", {self}}};
+             imperative::NameVarBaseMap outs = {{"Out", {new_var}}};
+             tracer->TraceOp("assign", ins, outs, attrs);
+             return new_var;
+           },
+           py::return_value_policy::copy, R"DOC(
+
+        Returns a new Tensor, which is clone of origin Tensor, and it remains in the current graph.
+        It will always have a Tensor copy.
+        Tn addition, the cloned Tensor provides gradient propagation.
+
+        Returns: The cloned Tensor.
+
+        Examples:
+            .. code-block:: python
+
+              import paddle
+
+              x = paddle.to_tensor(1.0, stop_gradient=False)
+              clone_x = x.clone()
+              y = clone_x**2
+              y.backward()
+              print(clone_x.stop_gradient) # False
+              print(clone_x.grad)          # [2.0], support gradient propagation
+              print(x.stop_gradient)       # False
+              print(x.grad)                # [2.0], clone_x support gradient propagation for x
+
+              x = paddle.to_tensor(1.0)
+              clone_x = x.clone()
+              clone_x.stop_gradient = False
+              z = clone_x**3
+              z.backward()
+              print(clone_x.stop_gradient) # False
+              print(clone_x.grad)          # [3.0], support gradient propagation
+              print(x.stop_gradient) # True
+              print(x.grad)          # None
+       )DOC")
       .def("_run_backward",
            [](imperative::VarBase &self, const imperative::Tracer &tracer,
               bool retain_graph) {
diff --git a/python/paddle/fluid/tests/unittests/test_var_base.py b/python/paddle/fluid/tests/unittests/test_var_base.py
index f6ac6715f6748a7962f29a3002fb84cc4cbf3628..6d4258a426d05654f98faac21d80ffca58862f8c 100644
--- a/python/paddle/fluid/tests/unittests/test_var_base.py
+++ b/python/paddle/fluid/tests/unittests/test_var_base.py
@@ -55,6 +55,15 @@ class TestVarBase(unittest.TestCase):
                     np.array_equal(x.numpy(), np.array([1.2]).astype(
                         'float32')))
                 self.assertEqual(x.dtype, core.VarDesc.VarType.FP32)
+                clone_x = x.clone()
+                self.assertTrue(
+                    np.array_equal(clone_x.numpy(),
+                                   np.array([1.2]).astype('float32')))
+                self.assertEqual(clone_x.dtype, core.VarDesc.VarType.FP32)
+                y = clone_x**2
+                y.backward()
+                self.assertTrue(
+                    np.array_equal(x.grad, np.array([2.4]).astype('float32')))
 
                 # set_default_dtype take effect on complex
                 x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False)
@@ -405,6 +414,7 @@ class TestVarBase(unittest.TestCase):
         self.assertListEqual(list(var_base.shape), list(static_var.shape))
 
     def test_tensor_str(self):
+        paddle.enable_static()
         paddle.disable_static(paddle.CPUPlace())
         paddle.manual_seed(10)
         a = paddle.rand([10, 20])