add parameter type check in normal and uniform distribution

mindspore/nn/probability/distribution/_utils/utils.py

@@ -368,3 +368,20 @@ class CheckTensor(PrimitiveWithInfer):
         if isinstance(x, Tensor):
             return x
         raise TypeError(f"For {name}, input type should be a Tensor.")
+
+def common_dtype(arg_a, name_a, arg_b, name_b, hint_type):
+    """
+    check if arg_a and arg_b have the same dtype.
+    """
+    if hasattr(arg_a, 'dtype') and hasattr(arg_b, 'dtype'):
+        if isinstance(arg_a, np.ndarray):
+            a_dtype = mstype.pytype_to_dtype(arg_a.dtype)
+        if isinstance(arg_a, np.ndarray):
+            b_dtype = mstype.pytype_to_dtype(arg_b.dtype)
+        if a_dtype != b_dtype:
+            raise TypeError(f"{name_a} and {name_b} should have the same dtype.")
+        int_type = mstype.int_type + mstype.uint_type
+        if a_dtype in int_type or a_dtype == mstype.float64:
+            return mstype.float32
+        return a_dtype
+    return hint_type

mindspore/nn/probability/distribution/bernoulli.py

@@ -32,7 +32,7 @@ class Bernoulli(Distribution):
         name (str): name of the distribution. Default: Bernoulli.
 
     Note:
-        probs should be proper probabilities (0 <= p <= 1).
+        probs should be proper probabilities (0 < p < 1).
         Dist_spec_args is probs.
 
     Examples:

mindspore/nn/probability/distribution/distribution.py

@@ -26,8 +26,9 @@ class Distribution(Cell):
     Base class for all mathematical distributions.
 
     Args:
+        seed (int): random seed used in sampling.
         dtype (mindspore.dtype): type of the distribution.
-        name (str): name of the distribution.
+        name (str): Python str name prefixed to Ops created by this class. Default: subclass name.
         param (dict): parameters used to initialize the distribution.
 
     Note:

mindspore/nn/probability/distribution/geometric.py

@@ -35,7 +35,7 @@ class Geometric(Distribution):
         name (str): name of the distribution. Default: Geometric.
 
     Note:
-        probs should be proper probabilities (0 <= p <= 1).
+        probs should be proper probabilities (0 < p < 1).
         Dist_spec_args is probs.
 
     Examples:
@@ -141,7 +141,7 @@ class Geometric(Distribution):
     @property
     def probs(self):
         """
-        Returns the probability for the outcome is 1.
+        Returns the probability of success of the Bernoulli trail.
         """
         return self._probs
 

mindspore/nn/probability/distribution/normal.py

@@ -19,7 +19,7 @@ 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, check_distribution_name,\
-                          raise_none_error
+                          raise_none_error, common_dtype
 from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, erf_generic
 
 class Normal(Distribution):
@@ -104,7 +104,7 @@ class Normal(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Normal, self).__init__(seed, dtype, name, param)
-        self.parameter_type = dtype
+        self.parameter_type = common_dtype(mean, 'mean', sd, 'sd', self.dtype)
         if  mean is not None and sd is not None:
             self._mean_value = cast_to_tensor(mean, self.parameter_type)
             self._sd_value = cast_to_tensor(sd, self.parameter_type)
@@ -126,6 +126,8 @@ class Normal(Distribution):
         self.sq = P.Square()
         self.sqrt = P.Sqrt()
         self.zeroslike = P.ZerosLike()
+        self.dtypeop = P.DType()
+        self.sametypeshape = P.SameTypeShape()
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -143,7 +145,6 @@ class Normal(Distribution):
                 self.checktensor(mean, 'mean')
             else:
                 mean = self.checktensor(mean, 'mean')
-            mean = self.cast(mean, self.parameter_type)
         else:
             mean = self._mean_value if self._mean_value is not None else raise_none_error('mean')
         if sd is not None:
@@ -151,12 +152,14 @@ class Normal(Distribution):
                 self.checktensor(sd, 'sd')
             else:
                 sd = self.checktensor(sd, 'sd')
-            sd = self.cast(sd, self.parameter_type)
         else:
             sd = self._sd_value if self._sd_value is not None else raise_none_error('sd')
         batch_shape = self.shape(mean + sd)
-        mean = mean * self.fill(self.dtype, batch_shape, 1.0)
-        sd = sd * self.fill(self.dtype, batch_shape, 1.0)
+        mean = mean * self.fill(self.dtypeop(mean), batch_shape, 1.0)
+        sd = sd * self.fill(self.dtypeop(sd), batch_shape, 1.0)
+        self.sametypeshape(mean, sd)
+        mean = self.cast(mean, self.parameter_type)
+        sd = self.cast(sd, self.parameter_type)
         return mean, sd
 
     def _mean(self, mean=None, sd=None):

mindspore/nn/probability/distribution/uniform.py

@@ -18,7 +18,7 @@ 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, check_type, check_distribution_name,\
-                          raise_none_error
+                          raise_none_error, common_dtype
 from ._utils.custom_ops import exp_generic, log_generic
 
 class Uniform(Distribution):
@@ -103,7 +103,7 @@ class Uniform(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Uniform, self).__init__(seed, dtype, name, param)
-        self.parameter_type = dtype
+        self.parameter_type = common_dtype(low, 'low', high, 'high', self.dtype)
         if low is not None and high is not None:
             self._low = cast_to_tensor(low, dtype)
             self._high = cast_to_tensor(high, dtype)
@@ -130,6 +130,8 @@ class Uniform(Distribution):
         self.zeroslike = P.ZerosLike()
         self.uniform = C.uniform
 
+        self.sametypeshape = P.SameTypeShape()
+
     def extend_repr(self):
         if self.is_scalar_batch:
             str_info = f'low = {self.low}, high = {self.high}'
@@ -146,7 +148,6 @@ class Uniform(Distribution):
                 self.checktensor(low, 'low')
             else:
                 low = self.checktensor(low, 'low')
-            low = self.cast(low, self.parameter_type)
         else:
             low = self.low if self.low is not None else raise_none_error('low')
         if high is not None:
@@ -154,12 +155,14 @@ class Uniform(Distribution):
                 self.checktensor(high, 'high')
             else:
                 high = self.checktensor(high, 'high')
-            high = self.cast(high, self.parameter_type)
         else:
             high = self.high if self.high is not None else raise_none_error('high')
         batch_shape = self.shape(high - low)
-        high = high * self.fill(self.dtype, batch_shape, 1.0)
-        low = low * self.fill(self.dtype, batch_shape, 1.0)
+        high = high * self.fill(self.dtypeop(high), batch_shape, 1.0)
+        low = low * self.fill(self.dtypeop(low), batch_shape, 1.0)
+        self.sametypeshape(high, low)
+        low = self.cast(low, self.parameter_type)
+        high = self.cast(high, self.parameter_type)
         return low, high
 
     @property