diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc
index 372fc74d19d0347ebdc073c75083c629e7cf6229..6c4ba10e90e2165c118d26e19ff53c792faa056d 100644
--- a/paddle/fluid/pybind/pybind.cc
+++ b/paddle/fluid/pybind/pybind.cc
@@ -506,6 +506,17 @@ static int GetNCCLVersion() {
 }
 #endif
 
+template <typename PlaceType>
+static void TensorCopyFrom(framework::Tensor *dst, const framework::Tensor &src,
+                           const PlaceType &place, int64_t batch_size) {
+  if (batch_size < 0) {
+    framework::TensorCopy(src, place, dst);
+  } else {
+    auto sliced = src.Slice(0, batch_size);
+    framework::TensorCopy(sliced, place, dst);
+  }
+}
+
 #ifdef PADDLE_WITH_AVX
 PYBIND11_MODULE(core_avx, m) {
 #else
@@ -755,16 +766,17 @@ PYBIND11_MODULE(core_noavx, m) {
               paddle::framework::proto::VarType::Type type) {
              return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
            })
-      .def("_copy_from",
-           [](framework::Tensor &self, const framework::Tensor &other,
-              const platform::Place &place, int64_t batch_size) {
-             if (batch_size < 0) {
-               framework::TensorCopy(other, place, &self);
-             } else {
-               auto sliced = other.Slice(0, batch_size);
-               framework::TensorCopy(sliced, place, &self);
-             }
-           },
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::CPUPlace>,
+           py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::XPUPlace>,
+           py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPlace>,
+           py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::NPUPlace>,
+           py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPinnedPlace>,
+           py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
+      .def("_copy_from", &TensorCopyFrom<paddle::platform::Place>,
            py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
       .def("set", SetTensorFromPyArray<paddle::platform::CPUPlace>,
            py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
diff --git a/python/paddle/fluid/tests/unittests/test_tensor_copy_from.py b/python/paddle/fluid/tests/unittests/test_tensor_copy_from.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a91c2182d1c5231b8f4305ce0586a5cb0f13a52
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/test_tensor_copy_from.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import unittest
+import numpy as np
+from paddle.fluid.core import LoDTensor as Tensor
+
+
+class TestTensorCopyFrom(unittest.TestCase):
+    def test_main(self):
+        place = paddle.CPUPlace()
+        np_value = np.random.random(size=[10, 30]).astype('float32')
+        t_src = Tensor()
+        t_src.set(np_value, place)
+        self.assertTrue(np.array_equal(np_value, t_src))
+
+        t_dst1 = Tensor()
+        t_dst1._copy_from(t_src, place)
+        self.assertTrue(np.array_equal(np_value, t_dst1))
+
+        t_dst2 = Tensor()
+        t_dst2._copy_from(t_src, place, 5)
+        self.assertTrue(np.array_equal(np.array(np_value[0:5]), t_dst2))
+
+
+if __name__ == "__main__":
+    unittest.main()