diff --git a/mindspore/nn/probability/distribution/bernoulli.py b/mindspore/nn/probability/distribution/bernoulli.py
index 31253259e5..028ee175f1 100644
--- a/mindspore/nn/probability/distribution/bernoulli.py
+++ b/mindspore/nn/probability/distribution/bernoulli.py
@@ -15,6 +15,7 @@
 """Bernoulli Distribution"""
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
+from mindspore.ops import composite as C
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_prob, check_type
 
@@ -116,7 +117,7 @@ class Bernoulli(Distribution):
         self.select = P.Select()
         self.sq = P.Square()
         self.sqrt = P.Sqrt()
-        self.uniform = P.UniformReal(seed=seed)
+        self.uniform = C.uniform
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -256,7 +257,6 @@ class Bernoulli(Distribution):
         probs1 = self.probs if probs is None else probs
         l_zero = self.const(0.0)
         h_one = self.const(1.0)
-        sample_uniform = self.uniform(shape + self.shape(probs1), l_zero, h_one)
+        sample_uniform = self.uniform(shape + self.shape(probs1), l_zero, h_one, self.seed)
         sample = self.less(sample_uniform, probs1)
-        sample = self.cast(sample, self.dtype)
-        return sample
+        return self.cast(sample, self.dtype)
diff --git a/mindspore/nn/probability/distribution/exponential.py b/mindspore/nn/probability/distribution/exponential.py
index 6f15958f95..8564935e09 100644
--- a/mindspore/nn/probability/distribution/exponential.py
+++ b/mindspore/nn/probability/distribution/exponential.py
@@ -15,6 +15,7 @@
 """Exponential Distribution"""
 import numpy as np
 from mindspore.ops import operations as P
+from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_greater_zero, check_type
@@ -107,7 +108,8 @@ class Exponential(Distribution):
 
         self.minval = np.finfo(np.float).tiny
 
-    # ops needed for the class
+        # ops needed for the class
+        self.cast = P.Cast()
         self.const = P.ScalarToArray()
         self.dtypeop = P.DType()
         self.exp = P.Exp()
@@ -118,7 +120,7 @@ class Exponential(Distribution):
         self.shape = P.Shape()
         self.sqrt = P.Sqrt()
         self.sq = P.Square()
-        self.uniform = P.UniformReal(seed=seed)
+        self.uniform = C.uniform
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -251,5 +253,6 @@ class Exponential(Distribution):
         rate = self.rate if rate is None else rate
         minval = self.const(self.minval)
         maxval = self.const(1.0)
-        sample = self.uniform(shape + self.shape(rate), minval, maxval)
-        return -self.log(sample) / rate
+        sample_uniform = self.uniform(shape + self.shape(rate), minval, maxval, self.seed)
+        sample = -self.log(sample_uniform) / rate
+        return self.cast(sample, self.dtype)
diff --git a/mindspore/nn/probability/distribution/geometric.py b/mindspore/nn/probability/distribution/geometric.py
index 87ad7ad8a4..2c67bb5588 100644
--- a/mindspore/nn/probability/distribution/geometric.py
+++ b/mindspore/nn/probability/distribution/geometric.py
@@ -15,6 +15,7 @@
 """Geometric Distribution"""
 import numpy as np
 from mindspore.ops import operations as P
+from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_prob, check_type
@@ -109,6 +110,7 @@ class Geometric(Distribution):
         self.minval = np.finfo(np.float).tiny
 
         # ops needed for the class
+        self.cast = P.Cast()
         self.const = P.ScalarToArray()
         self.dtypeop = P.DType()
         self.fill = P.Fill()
@@ -121,7 +123,7 @@ class Geometric(Distribution):
         self.shape = P.Shape()
         self.sq = P.Square()
         self.sqrt = P.Sqrt()
-        self.uniform = P.UniformReal(seed=seed)
+        self.uniform = C.uniform
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -269,5 +271,6 @@ class Geometric(Distribution):
         probs = self.probs if probs is None else probs
         minval = self.const(self.minval)
         maxval = self.const(1.0)
-        sample_uniform = self.uniform(shape + self.shape(probs), minval, maxval)
-        return self.floor(self.log(sample_uniform) / self.log(1.0 - probs))
+        sample_uniform = self.uniform(shape + self.shape(probs), minval, maxval, self.seed)
+        sample = self.floor(self.log(sample_uniform) / self.log(1.0 - probs))
+        return self.cast(sample, self.dtype)
diff --git a/mindspore/nn/probability/distribution/uniform.py b/mindspore/nn/probability/distribution/uniform.py
index b1f2aba90a..6aff1ef775 100644
--- a/mindspore/nn/probability/distribution/uniform.py
+++ b/mindspore/nn/probability/distribution/uniform.py
@@ -14,6 +14,7 @@
 # ============================================================================
 """Uniform Distribution"""
 from mindspore.ops import operations as P
+from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import convert_to_batch, check_greater, check_type
@@ -108,7 +109,8 @@ class Uniform(Distribution):
             self._low = low
             self._high = high
 
-    # ops needed for the class
+        # ops needed for the class
+        self.cast = P.Cast()
         self.const = P.ScalarToArray()
         self.dtypeop = P.DType()
         self.exp = P.Exp()
@@ -121,8 +123,8 @@ class Uniform(Distribution):
         self.shape = P.Shape()
         self.sq = P.Square()
         self.sqrt = P.Sqrt()
-        self.uniform = P.UniformReal(seed=seed)
         self.zeroslike = P.ZerosLike()
+        self.uniform = C.uniform
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -284,6 +286,6 @@ class Uniform(Distribution):
         broadcast_shape = self.shape(low + high)
         l_zero = self.const(0.0)
         h_one = self.const(1.0)
-        sample_uniform = self.uniform(shape + broadcast_shape, l_zero, h_one)
+        sample_uniform = self.uniform(shape + broadcast_shape, l_zero, h_one, self.seed)
         sample = (high - low) * sample_uniform + low
-        return sample
+        return self.cast(sample, self.dtype)