diff --git a/python/paddle/static/nn/control_flow.py b/python/paddle/static/nn/control_flow.py
index c5d52654775418b822d767a18859dabb1c5eb63f..5e2e3add40cda182c9f470861e1130d85a4ed6b8 100644
--- a/python/paddle/static/nn/control_flow.py
+++ b/python/paddle/static/nn/control_flow.py
@@ -395,6 +395,10 @@ def assign_skip_lod_tensor_array(input, output):
                     input.shape, output.shape
                 )
             )
+        # NOTE(dev): Avoid assign if input is output in Variable level which means
+        # input is not generated in While sub block and modified by in-place and only
+        # belong to inplace ops in constructing program process, because in-place pass
+        # is only available in Graph level.
         paddle.assign(input, output)
 
 
diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py
index 3248aa30103f7d1ffae9eb586b1e825f91280c0e..3533fbc7c237ba5e1d738dd329ce014f4f5d403f 100644
--- a/python/paddle/tensor/creation.py
+++ b/python/paddle/tensor/creation.py
@@ -2030,6 +2030,10 @@ def assign(x, output=None):
             result2 = paddle.assign(data)  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
             result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
     """
+    # speed up
+    if x is output and isinstance(x, Variable):
+        return x
+
     input = x
     helper = LayerHelper('assign', **locals())
     check_type(
@@ -2038,7 +2042,6 @@ def assign(x, output=None):
         (Variable, np.ndarray, list, tuple, float, int, bool),
         'assign',
     )
-    is_inplace = True if output is not None else False
 
     if np.isscalar(input) and not isinstance(input, str):
         input = np.array([input])