added type check in distributions and fixed bugs in cast_to_tensor

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

@@ -15,6 +15,7 @@
 """Utitly functions to help distribution class."""
 import numpy as np
 from mindspore.ops import _utils as utils
+from mindspore.ops.primitive import constexpr
 from mindspore.common.tensor import Tensor
 from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
@@ -23,7 +24,7 @@ from mindspore.ops import composite as C
 import mindspore.nn as nn
 import mindspore.nn.probability as msp
 
-def cast_to_tensor(t, hint_dtype=mstype.float32):
+def cast_to_tensor(t, hint_type=mstype.float32):
     """
     Cast an user input value into a Tensor of dtype.
     If the input t is of type Parameter, t is directly returned as a Parameter.
@@ -38,24 +39,27 @@ def cast_to_tensor(t, hint_dtype=mstype.float32):
     Returns:
         Tensor.
     """
+    if t is None:
+        raise ValueError(f'Input cannot be None in cast_to_tensor')
     if isinstance(t, Parameter):
         return t
+    t_type = hint_type
     if isinstance(t, Tensor):
-        if t.dtype != hint_dtype:
-            raise TypeError(f"Input tensor should be type {hint_dtype}.")
         #check if the Tensor in shape of Tensor(4)
         if t.dim() == 0:
             value = t.asnumpy()
-            return Tensor([value], dtype=hint_dtype)
+            return Tensor([value], dtype=t_type)
         #convert the type of tensor to dtype
-        return t
+        return Tensor(t.asnumpy(), dtype=t_type)
     if isinstance(t, (list, np.ndarray)):
-        return Tensor(t, dtype=hint_dtype)
-    if np.isscalar(t):
-        return Tensor([t], dtype=hint_dtype)
-    raise RuntimeError("Input type is not supported.")
-
-def convert_to_batch(t, batch_shape, hint_dtype):
+        return Tensor(t, dtype=t_type)
+    if isinstance(t, bool):
+        raise TypeError(f'Input cannot be Type Bool')
+    if isinstance(t, (int, float)):
+        return Tensor([t], dtype=t_type)
+    raise TypeError("Input type is not supported.")
+
+def convert_to_batch(t, batch_shape, required_type):
     """
     Convert a Tensor to a given batch shape.
 
@@ -72,8 +76,8 @@ def convert_to_batch(t, batch_shape, hint_dtype):
     """
     if isinstance(t, Parameter):
         return t
-    t = cast_to_tensor(t, hint_dtype)
-    return Tensor(np.broadcast_to(t.asnumpy(), batch_shape), dtype=hint_dtype)
+    t = cast_to_tensor(t, required_type)
+    return Tensor(np.broadcast_to(t.asnumpy(), batch_shape), dtype=required_type)
 
 def check_scalar_from_param(params):
     """
@@ -91,7 +95,7 @@ def check_scalar_from_param(params):
             return False
         if isinstance(value, (str, type(params['dtype']))):
             continue
-        elif np.isscalar(value):
+        elif isinstance(value, (int, float)):
             continue
         else:
             return False
@@ -119,10 +123,11 @@ def calc_broadcast_shape_from_param(params):
         if isinstance(value, Parameter):
             value_t = value.default_input
         else:
-            value_t = cast_to_tensor(value, params['dtype'])
+            value_t = cast_to_tensor(value, mstype.float32)
         broadcast_shape = utils.get_broadcast_shape(broadcast_shape, list(value_t.shape), params['name'])
     return tuple(broadcast_shape)
 
+
 def check_greater_equal_zero(value, name):
     """
     Check if the given Tensor is greater zero.
@@ -155,14 +160,17 @@ def check_greater_zero(value, name):
         ValueError: if the input value is less than or equal to zero.
 
     """
+    if value is None:
+        raise ValueError(f'input value cannot be None in check_greater_zero')
     if isinstance(value, Parameter):
-        if isinstance(value.default_input, MetaTensor):
+        if not isinstance(value.default_input, Tensor):
             return
         value = value.default_input
     comp = np.less(np.zeros(value.shape), value.asnumpy())
     if not comp.all():
         raise ValueError(f'{name} should be greater than zero.')
 
+
 def check_greater(a, b, name_a, name_b):
     """
     Check if Tensor b is strictly greater than Tensor a.
@@ -176,6 +184,8 @@ def check_greater(a, b, name_a, name_b):
     Raises:
         ValueError: if b is less than or equal to a
     """
+    if a is None or b is None:
+        raise ValueError(f'input value cannot be None in check_greater')
     if isinstance(a, Parameter) or isinstance(b, Parameter):
         return
     comp = np.less(a.asnumpy(), b.asnumpy())
@@ -193,6 +203,8 @@ def check_prob(p):
     Raises:
         ValueError: if p is not a proper probability.
     """
+    if p is None:
+        raise ValueError(f'input value cannot be None in check_greater_zero')
     if isinstance(p, Parameter):
         if not isinstance(p.default_input, Tensor):
             return
@@ -259,3 +271,12 @@ def check_tensor_type(name, inputs, valid_type):
 def check_type(data_type, value_type, name):
     if not data_type in value_type:
         raise TypeError(f"For {name}, valid type include {value_type}, {data_type} is invalid")
+
+@constexpr
+def raise_none_error(name):
+    raise ValueError(f"{name} should be specified. Value cannot be None")
+
+@constexpr
+def check_distribution_name(name, expected_name):
+    if name != expected_name:
+        raise ValueError(f"Distribution should be {expected_name}.")

mindspore/nn/probability/distribution/bernoulli.py

@@ -17,7 +17,7 @@ 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
+from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, raise_none_error
 
 class Bernoulli(Distribution):
     """
@@ -99,8 +99,9 @@ class Bernoulli(Distribution):
         valid_dtype = mstype.int_type + mstype.uint_type
         check_type(dtype, valid_dtype, "Bernoulli")
         super(Bernoulli, self).__init__(seed, dtype, name, param)
+        self.parameter_type = mstype.float32
         if probs is not None:
-            self._probs = cast_to_tensor(probs, hint_dtype=mstype.float32)
+            self._probs = cast_to_tensor(probs, mstype.float32)
             check_prob(self.probs)
         else:
             self._probs = probs
@@ -111,6 +112,7 @@ class Bernoulli(Distribution):
         self.dtypeop = P.DType()
         self.erf = P.Erf()
         self.exp = P.Exp()
+        self.floor = P.Floor()
         self.fill = P.Fill()
         self.log = P.Log()
         self.less = P.Less()
@@ -139,14 +141,19 @@ class Bernoulli(Distribution):
         .. math::
             MEAN(B) = probs1
         """
-        return self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
+        return probs1
 
     def _mode(self, probs1=None):
         r"""
         .. math::
             MODE(B) = 1 if probs1 > 0.5 else = 0
         """
-        probs1 = self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
         prob_type = self.dtypeop(probs1)
         zeros = self.fill(prob_type, self.shape(probs1), 0.0)
         ones = self.fill(prob_type, self.shape(probs1), 1.0)
@@ -158,7 +165,9 @@ class Bernoulli(Distribution):
         .. math::
             VAR(B) = probs1 * probs0
         """
-        probs1 = self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
         probs0 = 1.0 - probs1
         return self.exp(self.log(probs0) + self.log(probs1))
 
@@ -167,7 +176,9 @@ class Bernoulli(Distribution):
         .. math::
             H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1)
         """
-        probs1 = self.probs if probs is None else probs
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         probs0 = 1 - probs1
         return -1 * (probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
 
@@ -180,9 +191,8 @@ class Bernoulli(Distribution):
             probs1_b (Tensor): probs1 of distribution b.
             probs1_a (Tensor): probs1 of distribution a. Default: self.probs.
         """
-        if dist == 'Bernoulli':
-            return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
-        return None
+        check_distribution_name(dist, 'Bernoulli')
+        return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
 
     def _log_prob(self, value, probs=None):
         r"""
@@ -196,7 +206,13 @@ class Bernoulli(Distribution):
             pmf(k) = probs1 if k = 1;
             pmf(k) = probs0 if k = 0;
         """
-        probs1 = self.probs if probs is None else probs
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, mstype.float32)
+        value = self.floor(value)
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         probs0 = 1.0 - probs1
         return self.log(probs1) * value + self.log(probs0) * (1.0 - value)
 
@@ -213,7 +229,13 @@ class Bernoulli(Distribution):
             cdf(k) = probs0 if 0 <= k <1;
             cdf(k) = 1 if k >=1;
         """
-        probs1 = self.probs if probs is None else probs
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, mstype.float32)
+        value = self.floor(value)
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         prob_type = self.dtypeop(probs1)
         value = value * self.fill(prob_type, self.shape(probs1), 1.0)
         probs0 = 1.0 - probs1 * self.fill(prob_type, self.shape(value), 1.0)
@@ -230,19 +252,23 @@ class Bernoulli(Distribution):
 
         Args:
             dist (str): type of the distributions. Should be "Bernoulli" in this case.
-            probs1_b (Tensor): probs1 of distribution b.
-            probs1_a (Tensor): probs1 of distribution a. Default: self.probs.
+            probs1_b (Tensor, Number): probs1 of distribution b.
+            probs1_a (Tensor, Number): probs1 of distribution a. Default: self.probs.
 
         .. math::
             KL(a||b) = probs1_a * \log(\frac{probs1_a}{probs1_b}) +
                        probs0_a * \log(\frac{probs0_a}{probs0_b})
         """
-        if dist == 'Bernoulli':
-            probs1_a = self.probs if probs1_a is None else probs1_a
-            probs0_a = 1.0 - probs1_a
-            probs0_b = 1.0 - probs1_b
-            return probs1_a * self.log(probs1_a / probs1_b) + probs0_a * self.log(probs0_a / probs0_b)
-        return None
+        check_distribution_name(dist, 'Bernoulli')
+        if probs1_b is None:
+            raise_none_error("probs1_b")
+        probs1_b = self.cast(probs1_b, self.parameter_type)
+        probs1_a = self.cast(probs1_a, self.parameter_type) if probs1_a is not None else self.probs
+        if probs1_a is None:
+            raise_none_error("probs1_a")
+        probs0_a = 1.0 - probs1_a
+        probs0_b = 1.0 - probs1_b
+        return probs1_a * self.log(probs1_a / probs1_b) + probs0_a * self.log(probs0_a / probs0_b)
 
     def _sample(self, shape=(), probs=None):
         """
@@ -250,12 +276,14 @@ class Bernoulli(Distribution):
 
         Args:
             shape (tuple): shape of the sample. Default: ().
-            probs (Tensor): probs1 of the samples. Default: self.probs.
+            probs (Tensor, Number): probs1 of the samples. Default: self.probs.
 
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        probs1 = self.probs if probs is None else probs
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("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, self.seed)

mindspore/nn/probability/distribution/exponential.py

@@ -18,7 +18,8 @@ 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
+from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name,\
+                          raise_none_error
 
 class Exponential(Distribution):
     """
@@ -100,8 +101,9 @@ class Exponential(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, "Exponential")
         super(Exponential, self).__init__(seed, dtype, name, param)
+        self.parameter_type = dtype
         if rate is not None:
-            self._rate = cast_to_tensor(rate, dtype)
+            self._rate = cast_to_tensor(rate, self.parameter_type)
             check_greater_zero(self._rate, "rate")
         else:
             self._rate = rate
@@ -141,16 +143,19 @@ class Exponential(Distribution):
         .. math::
             MEAN(EXP) = \frac{1.0}{\lambda}.
         """
-        rate = self.rate if rate is None else rate
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         return 1.0 / rate
 
-
     def _mode(self, rate=None):
         r"""
         .. math::
             MODE(EXP) = 0.
         """
-        rate = self.rate if rate is None else rate
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         return self.fill(self.dtype, self.shape(rate), 0.)
 
     def _sd(self, rate=None):
@@ -158,7 +163,9 @@ class Exponential(Distribution):
         .. math::
             sd(EXP) = \frac{1.0}{\lambda}.
         """
-        rate = self.rate if rate is None else rate
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         return 1.0 / rate
 
     def _entropy(self, rate=None):
@@ -166,7 +173,9 @@ class Exponential(Distribution):
         .. math::
             H(Exp) = 1 - \log(\lambda).
         """
-        rate = self.rate if rate is None else rate
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         return 1.0 - self.log(rate)
 
 
@@ -179,9 +188,9 @@ class Exponential(Distribution):
             rate_b (Tensor): rate of distribution b.
             rate_a (Tensor): rate of distribution a. Default: self.rate.
         """
-        if dist == 'Exponential':
-            return self._entropy(rate=rate_a) + self._kl_loss(dist, rate_b, rate_a)
-        return None
+        check_distribution_name(dist, 'Exponential')
+        return self._entropy(rate=rate_a) + self._kl_loss(dist, rate_b, rate_a)
+
 
     def _prob(self, value, rate=None):
         r"""
@@ -198,7 +207,12 @@ class Exponential(Distribution):
         .. math::
             pdf(x) = rate * \exp(-1 * \lambda * x) if x >= 0 else 0
         """
-        rate = self.rate if rate is None else rate
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         prob = self.exp(self.log(rate) - rate * value)
         zeros = self.fill(self.dtypeop(prob), self.shape(prob), 0.0)
         comp = self.less(value, zeros)
@@ -218,7 +232,12 @@ class Exponential(Distribution):
         .. math::
             cdf(x) = 1.0 - \exp(-1 * \lambda * x) if x >= 0 else 0
         """
-        rate = self.rate if rate is None else rate
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         cdf = 1.0 - self.exp(-1. * rate * value)
         zeros = self.fill(self.dtypeop(cdf), self.shape(cdf), 0.0)
         comp = self.less(value, zeros)
@@ -234,10 +253,14 @@ class Exponential(Distribution):
             rate_b (Tensor): rate of distribution b.
             rate_a (Tensor): rate of distribution a. Default: self.rate.
         """
-        if dist == 'Exponential':
-            rate_a = self.rate if rate_a is None else rate_a
-            return self.log(rate_a) - self.log(rate_b) + rate_b / rate_a - 1.0
-        return None
+        check_distribution_name(dist, 'Exponential')
+        if rate_b is None:
+            raise_none_error("rate_b")
+        rate_b = self.cast(rate_b, self.parameter_type)
+        rate_a = self.cast(rate_a, self.parameter_type) if rate_a is not None else self.rate
+        if rate_a is None:
+            raise_none_error("rate_a")
+        return self.log(rate_a) - self.log(rate_b) + rate_b / rate_a - 1.0
 
     def _sample(self, shape=(), rate=None):
         """
@@ -250,7 +273,9 @@ class Exponential(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        rate = self.rate if rate is None else rate
+        rate = self.cast(rate, self.parameter_type) if rate is not None else self.rate
+        if rate is None:
+            raise_none_error("rate")
         minval = self.const(self.minval)
         maxval = self.const(1.0)
         sample_uniform = self.uniform(shape + self.shape(rate), minval, maxval, self.seed)

mindspore/nn/probability/distribution/geometric.py

@@ -18,7 +18,8 @@ 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
+from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name,\
+                          raise_none_error
 
 class Geometric(Distribution):
     """
@@ -101,8 +102,9 @@ class Geometric(Distribution):
         valid_dtype = mstype.int_type + mstype.uint_type
         check_type(dtype, valid_dtype, "Geometric")
         super(Geometric, self).__init__(seed, dtype, name, param)
+        self.parameter_type = mstype.float32
         if probs is not None:
-            self._probs = cast_to_tensor(probs, hint_dtype=mstype.float32)
+            self._probs = cast_to_tensor(probs, self.parameter_type)
             check_prob(self._probs)
         else:
             self._probs = probs
@@ -145,7 +147,9 @@ class Geometric(Distribution):
         .. math::
             MEAN(Geo) = \fratc{1 - probs1}{probs1}
         """
-        probs1 = self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
         return (1. - probs1) / probs1
 
     def _mode(self, probs1=None):
@@ -153,7 +157,9 @@ class Geometric(Distribution):
         .. math::
             MODE(Geo) = 0
         """
-        probs1 = self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
         return self.fill(self.dtypeop(probs1), self.shape(probs1), 0.)
 
     def _var(self, probs1=None):
@@ -161,7 +167,9 @@ class Geometric(Distribution):
         .. math::
             VAR(Geo) = \frac{1 - probs1}{probs1 ^ {2}}
         """
-        probs1 = self.probs if probs1 is None else probs1
+        probs1 = self.cast(probs1, self.parameter_type) if probs1 is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs1")
         return (1.0 - probs1) / self.sq(probs1)
 
     def _entropy(self, probs=None):
@@ -169,7 +177,9 @@ class Geometric(Distribution):
         .. math::
             H(Geo) = \frac{-1 * probs0 \log_2 (1-probs0)\ - prob1 * \log_2 (1-probs1)\ }{probs1}
         """
-        probs1 = self.probs if probs is None else probs
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         probs0 = 1.0 - probs1
         return (-probs0 * self.log(probs0) - probs1 * self.log(probs1)) / probs1
 
@@ -182,9 +192,8 @@ class Geometric(Distribution):
             probs1_b (Tensor): probability of success of distribution b.
             probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
         """
-        if dist == 'Geometric':
-            return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
-        return None
+        check_distribution_name(dist, 'Geometric')
+        return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
 
     def _prob(self, value, probs=None):
         r"""
@@ -198,14 +207,13 @@ class Geometric(Distribution):
             pmf(k) = probs0 ^k * probs1 if k >= 0;
             pmf(k) = 0 if k < 0.
         """
-        probs1 = self.probs if probs is None else probs
-        dtype = self.dtypeop(value)
-        if self.issubclass(dtype, mstype.int_):
-            pass
-        elif self.issubclass(dtype, mstype.float_):
-            value = self.floor(value)
-        else:
-            return None
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, mstype.float32)
+        value = self.floor(value)
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         pmf = self.exp(self.log(1.0 - probs1) * value + self.log(probs1))
         zeros = self.fill(self.dtypeop(probs1), self.shape(pmf), 0.0)
         comp = self.less(value, zeros)
@@ -224,15 +232,14 @@ class Geometric(Distribution):
             cdf(k) = 0 if k < 0.
 
         """
-        probs1 = self.probs if probs is None else probs
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, mstype.float32)
+        value = self.floor(value)
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         probs0 = 1.0 - probs1
-        dtype = self.dtypeop(value)
-        if self.issubclass(dtype, mstype.int_):
-            pass
-        elif self.issubclass(dtype, mstype.float_):
-            value = self.floor(value)
-        else:
-            return None
         cdf = 1.0 - self.pow(probs0, value + 1.0)
         zeros = self.fill(self.dtypeop(probs1), self.shape(cdf), 0.0)
         comp = self.less(value, zeros)
@@ -251,12 +258,16 @@ class Geometric(Distribution):
         .. math::
             KL(a||b) = \log(\frac{probs1_a}{probs1_b}) + \frac{probs0_a}{probs1_a} * \log(\frac{probs0_a}{probs0_b})
         """
-        if dist == 'Geometric':
-            probs1_a = self.probs if probs1_a is None else probs1_a
-            probs0_a = 1.0 - probs1_a
-            probs0_b = 1.0 - probs1_b
-            return self.log(probs1_a / probs1_b) + (probs0_a / probs1_a) * self.log(probs0_a / probs0_b)
-        return None
+        check_distribution_name(dist, 'Geometric')
+        if probs1_b is None:
+            raise_none_error("probs1_b")
+        probs1_b = self.cast(probs1_b, self.parameter_type)
+        probs1_a = self.cast(probs1_a, self.parameter_type) if probs1_a is not None else self.probs
+        if probs1_a is None:
+            raise_none_error("probs1_a")
+        probs0_a = 1.0 - probs1_a
+        probs0_b = 1.0 - probs1_b
+        return self.log(probs1_a / probs1_b) + (probs0_a / probs1_a) * self.log(probs0_a / probs0_b)
 
     def _sample(self, shape=(), probs=None):
         """
@@ -269,9 +280,11 @@ class Geometric(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        probs = self.probs if probs is None else probs
+        probs1 = self.cast(probs, self.parameter_type) if probs is not None else self.probs
+        if probs1 is None:
+            raise_none_error("probs")
         minval = self.const(self.minval)
         maxval = self.const(1.0)
-        sample_uniform = self.uniform(shape + self.shape(probs), minval, maxval, self.seed)
-        sample = self.floor(self.log(sample_uniform) / self.log(1.0 - probs))
+        sample_uniform = self.uniform(shape + self.shape(probs1), minval, maxval, self.seed)
+        sample = self.floor(self.log(sample_uniform) / self.log(1.0 - probs1))
         return self.cast(sample, self.dtype)

mindspore/nn/probability/distribution/normal.py

@@ -18,8 +18,8 @@ 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_zero, check_type
-
+from ._utils.utils import convert_to_batch, check_greater_zero, check_type, check_distribution_name,\
+                          raise_none_error
 
 class Normal(Distribution):
     """
@@ -103,9 +103,10 @@ class Normal(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, "Normal")
         super(Normal, self).__init__(seed, dtype, name, param)
+        self.parameter_type = dtype
         if  mean is not None and sd is not None:
-            self._mean_value = convert_to_batch(mean, self.broadcast_shape, dtype)
-            self._sd_value = convert_to_batch(sd, self.broadcast_shape, dtype)
+            self._mean_value = convert_to_batch(mean, self.broadcast_shape, self.parameter_type)
+            self._sd_value = convert_to_batch(sd, self.broadcast_shape, self.parameter_type)
             check_greater_zero(self._sd_value, "Standard deviation")
         else:
             self._mean_value = mean
@@ -113,6 +114,7 @@ class Normal(Distribution):
 
 
         #ops needed for the class
+        self.cast = P.Cast()
         self.const = P.ScalarToArray()
         self.erf = P.Erf()
         self.exp = P.Exp()
@@ -141,31 +143,51 @@ class Normal(Distribution):
         """
         Mean of the distribution.
         """
-        mean = self._mean_value if mean is None or sd is None else mean
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         return mean
 
     def _mode(self, mean=None, sd=None):
         """
         Mode of the distribution.
         """
-        mean = self._mean_value if mean is None or sd is None else mean
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         return mean
 
     def _sd(self, mean=None, sd=None):
         """
         Standard deviation of the distribution.
         """
-        sd = self._sd_value if mean is None or sd is None else sd
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         return sd
 
-    def _entropy(self, sd=None):
+    def _entropy(self, mean=None, sd=None):
         r"""
         Evaluate entropy.
 
         .. math::
             H(X) = \log(\sqrt(numpy.e * 2. * numpy.pi * \sq(\sigma)))
         """
-        sd = self._sd_value if sd is None else sd
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         return self.log(self.sqrt(self.const(np.e * 2. * np.pi))) + self.log(sd)
 
     def _cross_entropy(self, dist, mean_b, sd_b, mean_a=None, sd_a=None):
@@ -179,9 +201,8 @@ class Normal(Distribution):
             mean_a (Tensor): mean of distribution a. Default: self._mean_value.
             sd_a (Tensor): standard deviation distribution a. Default: self._sd_value.
         """
-        if dist == 'Normal':
-            return self._entropy(sd=sd_a) + self._kl_loss(dist, mean_b, sd_b, mean_a, sd_a)
-        return None
+        check_distribution_name(dist, 'Normal')
+        return self._entropy(mean=mean_a, sd=sd_a) + self._kl_loss(dist, mean_b, sd_b, mean_a, sd_a)
 
     def _log_prob(self, value, mean=None, sd=None):
         r"""
@@ -195,10 +216,17 @@ class Normal(Distribution):
         .. math::
             L(x) = -1* \frac{(x - \mu)^2}{2. * \sigma^2} - \log(\sqrt(2* \pi * \sigma^2))
         """
-        mean = self._mean_value if mean is None else mean
-        sd = self._sd_value if sd is None else sd
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         unnormalized_log_prob = -1. * (self.sq(value - mean)) / (2. * self.sq(sd))
-        neg_normalization = -1. * self.log(self.sqrt(self.const(2. * np.pi))) - self.log(sd)
+        neg_normalization = -1. * self.log(self.const(2. * np.pi)) / 2. - self.log(sd)
         return unnormalized_log_prob + neg_normalization
 
     def _cdf(self, value, mean=None, sd=None):
@@ -213,8 +241,15 @@ class Normal(Distribution):
         .. math::
             cdf(x) = 0.5 * (1+ Erf((x - \mu) / ( \sigma * \sqrt(2))))
         """
-        mean = self._mean_value if mean is None else mean
-        sd = self._sd_value if sd is None else sd
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         sqrt2 = self.sqrt(self.const(2.0))
         adjusted = (value - mean) / (sd * sqrt2)
         return 0.5 * (1.0 + self.erf(adjusted))
@@ -234,13 +269,23 @@ class Normal(Distribution):
             KL(a||b) = 0.5 * (\frac{MEAN(a)}{STD(b)} - \frac{MEAN(b)}{STD(b)}) ^ 2 +
                        0.5 * EXPM1(2 * (\log(STD(a)) - \log(STD(b))) - (\log(STD(a)) - \log(STD(b)))
         """
-        if dist == 'Normal':
-            mean_a = self._mean_value if mean_a is None else mean_a
-            sd_a = self._sd_value if sd_a is None else sd_a
-            diff_log_scale = self.log(sd_a) - self.log(sd_b)
-            squared_diff = self.sq(mean_a / sd_b - mean_b / sd_b)
-            return 0.5 * squared_diff + 0.5 * self.expm1(2 * diff_log_scale) - diff_log_scale
-        return None
+        check_distribution_name(dist, 'Normal')
+        if mean_b is None:
+            raise_none_error("mean_b")
+        if sd_b is None:
+            raise_none_error("sd_b")
+        mean_b = self.cast(mean_b, self.parameter_type)
+        sd_b = self.cast(sd_b, self.parameter_type)
+        mean_a = self.cast(mean_a, self.parameter_type) if mean_a is not None else self._mean_value
+        sd_a = self.cast(sd_a, self.parameter_type) if sd_a is not None else self._sd_value
+        if mean_a is None:
+            raise_none_error("mean_a")
+        if sd_a is None:
+            raise_none_error("sd_a")
+        diff_log_scale = self.log(sd_a) - self.log(sd_b)
+        squared_diff = self.sq(mean_a / sd_b - mean_b / sd_b)
+        return 0.5 * squared_diff + 0.5 * self.expm1(2 * diff_log_scale) - diff_log_scale
+
 
     def _sample(self, shape=(), mean=None, sd=None):
         """
@@ -254,8 +299,12 @@ class Normal(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        mean = self._mean_value if mean is None else mean
-        sd = self._sd_value if sd is None else sd
+        mean = self.cast(mean, self.parameter_type) if mean is not None else self._mean_value
+        if mean is None:
+            raise_none_error("mean")
+        sd = self.cast(sd, self.parameter_type) if sd is not None else self._sd_value
+        if sd is None:
+            raise_none_error("sd")
         batch_shape = self.shape(self.zeroslike(mean) + self.zeroslike(sd))
         sample_shape = shape + batch_shape
         sample_norm = C.normal(sample_shape, mean, sd, self.seed)

mindspore/nn/probability/distribution/uniform.py

@@ -17,7 +17,8 @@ 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
+from ._utils.utils import convert_to_batch, check_greater, check_type, check_distribution_name,\
+                          raise_none_error
 
 class Uniform(Distribution):
     """
@@ -101,6 +102,7 @@ class Uniform(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, "Uniform")
         super(Uniform, self).__init__(seed, dtype, name, param)
+        self.parameter_type = dtype
         if low is not None and high is not None:
             self._low = convert_to_batch(low, self.broadcast_shape, dtype)
             self._high = convert_to_batch(high, self.broadcast_shape, dtype)
@@ -153,8 +155,12 @@ class Uniform(Distribution):
         .. math::
             range(U) = high -low
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         return high - low
 
     def _mean(self, low=None, high=None):
@@ -162,18 +168,25 @@ class Uniform(Distribution):
         .. math::
             MEAN(U) = \frac{low + high}{2}.
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         return (low + high) / 2.
 
-
     def _var(self, low=None, high=None):
         r"""
         .. math::
             VAR(U) = \frac{(high -low) ^ 2}{12}.
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         return self.sq(high - low) / 12.0
 
     def _entropy(self, low=None, high=None):
@@ -181,8 +194,12 @@ class Uniform(Distribution):
         .. math::
             H(U) = \log(high - low).
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         return self.log(high - low)
 
     def _cross_entropy(self, dist, low_b, high_b, low_a=None, high_a=None):
@@ -196,9 +213,8 @@ class Uniform(Distribution):
             low_a (Tensor): lower bound of distribution a. Default: self.low.
             high_a (Tensor): upper bound of distribution a. Default: self.high.
         """
-        if dist == 'Uniform':
-            return self._entropy(low=low_a, high=high_a) + self._kl_loss(dist, low_b, high_b, low_a, high_a)
-        return None
+        check_distribution_name(dist, 'Uniform')
+        return self._entropy(low=low_a, high=high_a) + self._kl_loss(dist, low_b, high_b, low_a, high_a)
 
     def _prob(self, value, low=None, high=None):
         r"""
@@ -214,8 +230,15 @@ class Uniform(Distribution):
             pdf(x) = \frac{1.0}{high -low} if low <= x <= high;
             pdf(x) = 0 if x > high;
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         neg_ones = self.fill(self.dtype, self.shape(value), -1.0)
         prob = self.exp(neg_ones * self.log(high - low))
         broadcast_shape = self.shape(prob)
@@ -236,13 +259,22 @@ class Uniform(Distribution):
             low_a (Tensor): lower bound of distribution a. Default: self.low.
             high_a (Tensor): upper bound of distribution a. Default: self.high.
         """
-        if dist == 'Uniform':
-            low_a = self.low if low_a is None else low_a
-            high_a = self.high if high_a is None else high_a
-            kl = self.log(high_b - low_b) / self.log(high_a - low_a)
-            comp = self.logicaland(self.lessequal(low_b, low_a), self.lessequal(high_a, high_b))
-            return self.select(comp, kl, self.log(self.zeroslike(kl)))
-        return None
+        check_distribution_name(dist, 'Uniform')
+        if low_b is None:
+            raise_none_error("low_b")
+        if high_b is None:
+            raise_none_error("high_b")
+        low_b = self.cast(low_b, self.parameter_type)
+        high_b = self.cast(high_b, self.parameter_type)
+        low_a = self.cast(low_a, self.parameter_type) if low_a is not None else self.low
+        if low_a is None:
+            raise_none_error("low_a")
+        high_a = self.cast(high_a, self.parameter_type) if high_a is not None else self.high
+        if high_a is None:
+            raise_none_error("high_a")
+        kl = self.log(high_b - low_b) / self.log(high_a - low_a)
+        comp = self.logicaland(self.lessequal(low_b, low_a), self.lessequal(high_a, high_b))
+        return self.select(comp, kl, self.log(self.zeroslike(kl)))
 
     def _cdf(self, value, low=None, high=None):
         r"""
@@ -258,8 +290,15 @@ class Uniform(Distribution):
             cdf(x) = \frac{x - low}{high -low} if low <= x <= high;
             cdf(x) = 1 if x > high;
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        if value is None:
+            raise_none_error("value")
+        value = self.cast(value, self.dtype)
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         prob = (value - low) / (high - low)
         broadcast_shape = self.shape(prob)
         zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
@@ -281,8 +320,12 @@ class Uniform(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        low = self.low if low is None else low
-        high = self.high if high is None else high
+        low = self.cast(low, self.parameter_type) if low is not None else self.low
+        if low is None:
+            raise_none_error("low")
+        high = self.cast(high, self.parameter_type) if high is not None else self.high
+        if high is None:
+            raise_none_error("high")
         broadcast_shape = self.shape(low + high)
         l_zero = self.const(0.0)
         h_one = self.const(1.0)