diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py
index 0d9d617c4d220e0b7fb36fad1be1dcd5fa1625b5..f6cca91374e58d39cda125fda79861810726e135 100644
--- a/python/paddle/fluid/executor.py
+++ b/python/paddle/fluid/executor.py
@@ -404,29 +404,35 @@ def _as_lodtensor(data, place, dtype=None):
             >>>     ...
 
         Args:
-            data(numpy.ndarray): a instance of array
+            data(numpy.ndarray|list|tuple|scalar): a instance of array, scalar, list or tuple
             data(core.Place): the place of created tensor
-            dtype(core.VarDesc.VarType): the expected data type of created tensor
+            dtype(core.VarDesc.VarType|str): the expected data type of created tensor
 
         Returns:
             LoDTensor
         """
-    if isinstance(data, list):
-        raise RuntimeError("Some of your feed data hold LoD information. \
-                They can not be completely cast from a list of Python \
-                ndarray to LoDTensor. Please convert data to LoDTensor \
-                directly before feeding the data.\
-                ")
-
-    #NOTE(zhiqiu): convert python builtin ,like float and int, to numpy array
+    #NOTE(zhiqiu): convert python builtin, like float, int, and list, to numpy ndarray
     if not isinstance(data, np.ndarray):
+        assert dtype is not None, 'The dtype should be given when feed data is not np.ndarray'
+        dtype = convert_dtype(dtype) if isinstance(
+            dtype, core.VarDesc.VarType) else dtype
         if np.isscalar(data):
-            assert dtype is not None, 'dtype should be given when casting python scalar to tensor'
-            dtype = convert_dtype(dtype) if isinstance(
-                dtype, core.VarDesc.VarType) else dtype
             data = np.array([data]).astype(dtype)
+        elif isinstance(data, (list, tuple)):
+            data = np.array(data)
+            if data.dtype == np.object:
+                raise TypeError(
+                    "\n\tFaild to convert input data to a regular ndarray :\n\t* Usually "
+                    "this means the input data contains nested lists with different lengths. "
+                    "Please consider using 'fluid.create_lod_tensor' to convert it to a LoD-Tensor."
+                )
+            data = data.astype(dtype)
+        else:
+            raise TypeError(
+                "Convert data of type {} to Tensor is not supported".format(
+                    type(data)))
 
-    # single tensor case
+    # convert numpy.ndarray to tensor
     tensor = core.LoDTensor()
     tensor.set(data, place)
     return tensor
diff --git a/python/paddle/fluid/tests/unittests/test_executor_feed_scalar.py b/python/paddle/fluid/tests/unittests/test_executor_feed_non_tensor.py
similarity index 72%
rename from python/paddle/fluid/tests/unittests/test_executor_feed_scalar.py
rename to python/paddle/fluid/tests/unittests/test_executor_feed_non_tensor.py
index 562f3066efba78059d6d42de3f25b5e8a5f54c0d..23c4191f6cfd8cd19777fe4660acbee4f0c98a66 100644
--- a/python/paddle/fluid/tests/unittests/test_executor_feed_scalar.py
+++ b/python/paddle/fluid/tests/unittests/test_executor_feed_non_tensor.py
@@ -84,6 +84,28 @@ class TestExecutor(unittest.TestCase):
             self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
             self.assertEqual(type(a), int)
 
+    def test_program_feed_list(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        scope = fluid.Scope()
+        with fluid.program_guard(main_program, startup_program):
+            with fluid.scope_guard(scope):
+                cpu = fluid.CPUPlace()
+                exe = fluid.Executor(cpu)
+                lr, cost = self.net()
+                exe.run(startup_program)
+                train_data = [[1.0], [2.0], [3.0], [4.0]]
+                y_true = [[2.0], [4.0], [6.0], [8.0]]
+                a = 0
+                _lr, _ = exe.run(feed={'x': train_data,
+                                       'y': y_true,
+                                       'lr': a},
+                                 fetch_list=[lr, cost],
+                                 return_numpy=False)
+            self.assertEqual(_lr._dtype(), lr.dtype)
+            self.assertEqual(_lr._dtype(), fluid.core.VarDesc.VarType.FP32)
+            self.assertEqual(type(y_true), list)
+
     def test_compiled_program_feed_scalar(self):
         main_program = fluid.Program()
         startup_program = fluid.Program()
@@ -125,10 +147,32 @@ class TestAsLodTensor(unittest.TestCase):
                                               fluid.core.VarDesc.VarType.FP64)
         self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.FP64)
 
-    def test_as_lodtensor_error(self):
+    def test_as_lodtensor_assertion_error(self):
         cpu = fluid.CPUPlace()
         self.assertRaises(AssertionError, fluid.executor._as_lodtensor, 1, cpu)
 
+    def test_as_lodtensor_type_error(self):
+        cpu = fluid.CPUPlace()
+        self.assertRaises(TypeError, fluid.executor._as_lodtensor, {"a": 1},
+                          cpu, fluid.core.VarDesc.VarType.INT32)
+
+    def test_as_lodtensor_list(self):
+        cpu = fluid.CPUPlace()
+        tensor = fluid.executor._as_lodtensor([1, 2], cpu,
+                                              fluid.core.VarDesc.VarType.FP64)
+        self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.FP64)
+
+    def test_as_lodtensor_tuple(self):
+        cpu = fluid.CPUPlace()
+        tensor = fluid.executor._as_lodtensor((1, 2), cpu,
+                                              fluid.core.VarDesc.VarType.FP64)
+        self.assertEqual(tensor._dtype(), fluid.core.VarDesc.VarType.FP64)
+
+    def test_as_lodtensor_nested_list(self):
+        cpu = fluid.CPUPlace()
+        self.assertRaises(TypeError, fluid.executor._as_lodtensor,
+                          [[1], [1, 2]], cpu, fluid.core.VarDesc.VarType.INT32)
+
 
 if __name__ == '__main__':
     unittest.main()