diff --git a/python/paddle/distribution.py b/python/paddle/distribution.py
index ba4bfa8708b2ae3d8ae6643393e0f87cc9c6b360..acb7251a15dbfff7e032079c2be2e973e81aa5f4 100644
--- a/python/paddle/distribution.py
+++ b/python/paddle/distribution.py
@@ -25,6 +25,7 @@ from .fluid.layers import control_flow
 from .fluid.layers import tensor
 from .fluid.layers import ops
 from .fluid.layers import nn
+from .fluid import core
 from .fluid.framework import in_dygraph_mode
 from .tensor.math import elementwise_mul, elementwise_div, elementwise_add, elementwise_sub
 import math
@@ -87,7 +88,7 @@ class Distribution(object):
 
         return is_variable
 
-    def _to_variable(self, *args):
+    def _to_tensor(self, *args):
         """
         Argument convert args to Tensor
 
@@ -134,7 +135,7 @@ class Uniform(Distribution):
 
     Mathematical Details
 
-    The probability density function (pdf) is,
+    The probability density function (pdf) is
 
     .. math::
 
@@ -154,8 +155,8 @@ class Uniform(Distribution):
     [broadcasting](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basic_concept/broadcasting_en.html) (e.g., `high - low` is a valid operation).
 
     Args:
-        low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is float32 or int
-        high(int|float|list|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is float32 or int
+        low(int|float|list|numpy.ndarray|Tensor): The lower boundary of uniform distribution.The data type is int, float32, list, numpy.ndarray or Tensor
+        high(int|float|list|numpy.ndarray|Tensor): The higher boundary of uniform distribution.The data type is int, float32, list, numpy.ndarray or Tensor
         name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Examples:
@@ -218,7 +219,7 @@ class Uniform(Distribution):
         else:
             if isinstance(low, float) and isinstance(high, float):
                 self.all_arg_is_float = True
-            self.low, self.high = self._to_variable(low, high)
+            self.low, self.high = self._to_tensor(low, high)
 
     def sample(self, shape, seed=0):
         """Generate samples of the specified shape.
@@ -272,10 +273,13 @@ class Uniform(Distribution):
         if in_dygraph_mode():
             lb_bool = self.low < value
             ub_bool = value < self.high
-            lb = tensor.cast(lb_bool, dtype=value.dtype)
-            ub = tensor.cast(ub_bool, dtype=value.dtype)
-            return elementwise_sub(
-                nn.log(lb * ub), nn.log(self.high - self.low), name=name)
+
+            dtype = value.dtype
+            lb = core.ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype',
+                               dtype)
+            ub = core.ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype',
+                               dtype)
+            return nn.log(lb * ub) - nn.log(self.high - self.low)
 
         check_variable_and_dtype(value, 'value', ['float32', 'float64'],
                                  'log_prob')
@@ -301,9 +305,13 @@ class Uniform(Distribution):
         if in_dygraph_mode():
             lb_bool = self.low < value
             ub_bool = value < self.high
-            lb = tensor.cast(lb_bool, dtype=value.dtype)
-            ub = tensor.cast(ub_bool, dtype=value.dtype)
-            return elementwise_div((lb * ub), (self.high - self.low), name=name)
+
+            dtype = value.dtype
+            lb = core.ops.cast(lb_bool, 'in_dtype', lb_bool.dtype, 'out_dtype',
+                               dtype)
+            ub = core.ops.cast(ub_bool, 'in_dtype', ub_bool.dtype, 'out_dtype',
+                               dtype)
+            return (lb * ub) / (self.high - self.low)
 
         check_variable_and_dtype(value, 'value', ['float32', 'float64'],
                                  'log_prob')
@@ -330,7 +338,7 @@ class Normal(Distribution):
 
     Mathematical details
 
-    The probability density function (pdf) is,
+    The probability density function (pdf) is
 
     .. math::
 
@@ -347,8 +355,8 @@ class Normal(Distribution):
     * :math:`Z`: is the normalization constant.
 
     Args:
-        loc(int|float|list|numpy.ndarray|Tensor): The mean of normal distribution.The data type is float32 or int.
-        scale(int|float|list|numpy.ndarray|Tensor): The std of normal distribution.The data type is float32 or int.
+        loc(int|float|list|numpy.ndarray|Tensor): The mean of normal distribution.The data type is int, float32, list, numpy.ndarray or Tensor.
+        scale(int|float|list|numpy.ndarray|Tensor): The std of normal distribution.The data type is int, float32, list, numpy.ndarray or Tensor.
         name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Examples:
@@ -414,7 +422,7 @@ class Normal(Distribution):
         else:
             if isinstance(loc, float) and isinstance(scale, float):
                 self.all_arg_is_float = True
-            self.loc, self.scale = self._to_variable(loc, scale)
+            self.loc, self.scale = self._to_tensor(loc, scale)
 
     def sample(self, shape, seed=0):
         """Generate samples of the specified shape.