diff --git a/paddle/fluid/operators/elementwise/elementwise_pow_op.h b/paddle/fluid/operators/elementwise/elementwise_pow_op.h
index 535d838209d0e418554d7f6facc932af6fff6185..8cc4b166fc491e56d011560e02cc102f845ebf64 100755
--- a/paddle/fluid/operators/elementwise/elementwise_pow_op.h
+++ b/paddle/fluid/operators/elementwise/elementwise_pow_op.h
@@ -23,20 +23,17 @@ namespace operators {
 template <typename T>
 struct PowFunctor {
   inline HOSTDEVICE T operator()(T a, T b) const {
-    // TODO(wujionghao): A potential speed improvement is supporting different
-    // types in C++.
-    // #ifdef __CUDA_ARCH__
-    //     // On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
-    //     // it will return a float number like 2.99... , which floor to 2
-    //     // when cast to int by default and it is wrong.
-    //     // Use llrint to cast it to the nearest integer, which is 3.
-    //     if (std::is_integral<T>::value) {
-    //       return std::llrint(std::pow(a, b));
-    //     }
-    // #endif
+// TODO(wujionghao): A potential speed improvement is supporting different
+// types in C++.
+#ifdef __CUDA_ARCH__
+    // On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
+    // it will return a float number like 2.99... , which floor to 2
+    // when cast to int by default and it is wrong.
+    // Use llrint to cast it to the nearest integer, which is 3.
     if (std::is_integral<T>::value) {
       return std::llrint(std::pow(a, b));
     }
+#endif
     return std::pow(a, b);
   }
 };
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
index f6d5c83ef20ff7a52cc8cf7477065efe05d7132e..3a5dcd02fd786594ba1d858eee4d5e220becaada 100755
--- a/python/paddle/tensor/math.py
+++ b/python/paddle/tensor/math.py
@@ -172,12 +172,12 @@ def pow(x, y, name=None):
             x = paddle.to_tensor([1, 2, 3])
             y = 2
             res = paddle.pow(x, y)
-            print(res.numpy()) # [1 4 9]
+            print(res) # [1 4 9]
             
             # example 2: y is a Tensor
             y = paddle.full(shape=[1], fill_value=2, dtype='float32')
             res = paddle.pow(x, y)
-            print(res.numpy()) # [1 4 9]
+            print(res) # [1 4 9]
 
     """
     # in dynamic graph mode
@@ -185,14 +185,9 @@ def pow(x, y, name=None):
         if isinstance(y, (int, float)):
             return core.ops.pow(x, 'factor', y)
         elif isinstance(y, (paddle.Tensor, Variable)):
-
             if x.dtype != y.dtype:
                 y = cast(y, dtype='float64')
                 x = cast(x, dtype='float64')
-                out_dygraph = _elementwise_op_in_dygraph(
-                x, y, axis=-1, act=None, op_name='elementwise_pow')
-                return out_dygraph
-
             return _elementwise_op_in_dygraph(
                 x, y, axis=-1, act=None, op_name='elementwise_pow')
         else:
@@ -213,9 +208,7 @@ def pow(x, y, name=None):
             if x.dtype != y.dtype:
                 y = cast(y, dtype='float64')
                 x = cast(x, dtype='float64')
-                out = helper.create_variable_for_type_inference(dtype=x.dtype)
-            else:
-                out = helper.create_variable_for_type_inference(dtype=x.dtype)
+            out = helper.create_variable_for_type_inference(dtype=x.dtype)
             return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
         else:
             raise TypeError('y must be scalar or tensor type, but received: %s '% (type(y)))