diff --git a/mindspore/nn/probability/distribution/beta.py b/mindspore/nn/probability/distribution/beta.py
index 2a07375bfe..ff9ced01cd 100644
--- a/mindspore/nn/probability/distribution/beta.py
+++ b/mindspore/nn/probability/distribution/beta.py
@@ -238,7 +238,8 @@ class Beta(Distribution):
         comp1 = self.greater(concentration1, 1.)
         comp2 = self.greater(concentration0, 1.)
         cond = self.logicaland(comp1, comp2)
-        nan = self.fill(self.dtype, self.broadcast_shape, np.nan)
+        batch_shape = self.shape(concentration1 + concentration0)
+        nan = self.fill(self.dtype, batch_shape, np.nan)
         mode = (concentration1 - 1.) / (concentration1 + concentration0 - 2.)
         return self.select(cond, mode, nan)
 
diff --git a/mindspore/nn/probability/distribution/poisson.py b/mindspore/nn/probability/distribution/poisson.py
index b91ffa94f3..a543e5b4c0 100644
--- a/mindspore/nn/probability/distribution/poisson.py
+++ b/mindspore/nn/probability/distribution/poisson.py
@@ -212,6 +212,7 @@ class Poisson(Distribution):
         """
         value = self._check_value(value, "value")
         value = self.cast(value, self.dtype)
+        value = self.floor(value)
         rate = self._check_param_type(rate)
         log_rate = self.log(rate)
         zeros = self.fill(self.dtypeop(value), self.shape(value), 0.0)
@@ -239,6 +240,7 @@ class Poisson(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
+        value = self.floor(value)
         rate = self._check_param_type(rate)
         zeros = self.fill(self.dtypeop(value), self.shape(value), 0.0)
         comp = self.less(value, zeros)
@@ -259,6 +261,9 @@ class Poisson(Distribution):
         """
         shape = self.checktuple(shape, 'shape')
         rate = self._check_param_type(rate)
+
+        # now Poisson sampler supports only fp32
+        rate = self.cast(rate, mstype.float32)
         origin_shape = shape + self.shape(rate)
         if origin_shape == ():
             sample_shape = (1,)