changed distribution api

mindspore/nn/probability/distribution/bernoulli.py

@@ -34,55 +34,56 @@ class Bernoulli(Distribution):
 
     Examples:
         >>> # To initialize a Bernoulli distribution of prob 0.5
-        >>> n = nn.Bernoulli(0.5, dtype=mstype.int32)
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> b = msd.Bernoulli(0.5, dtype=mstype.int32)
         >>>
         >>> # The following creates two independent Bernoulli distributions
-        >>> n = nn.Bernoulli([0.5, 0.5], dtype=mstype.int32)
+        >>> b = msd.Bernoulli([0.5, 0.5], dtype=mstype.int32)
         >>>
         >>> # A Bernoulli distribution can be initilized without arguments
-        >>> # In this case, probs must be passed in through construct.
-        >>> n = nn.Bernoulli(dtype=mstype.int32)
+        >>> # In this case, probs must be passed in through args during function calls.
+        >>> b = msd.Bernoulli(dtype=mstype.int32)
         >>>
-        >>> # To use Bernoulli distribution in a network
+        >>> # To use Bernoulli in a network
         >>> class net(Cell):
         >>>     def __init__(self):
         >>>         super(net, self).__init__():
-        >>>         self.b1 = nn.Bernoulli(0.5, dtype=mstype.int32)
-        >>>         self.b2 = nn.Bernoulli(dtype=mstype.int32)
+        >>>         self.b1 = msd.Bernoulli(0.5, dtype=mstype.int32)
+        >>>         self.b2 = msd.Bernoulli(dtype=mstype.int32)
         >>>
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, probs_b, probs_a):
         >>>
         >>>         # Similar calls can be made to other probability functions
         >>>         # by replacing 'prob' with the name of the function
-        >>>         ans = self.b1('prob', value)
+        >>>         ans = self.b1.prob(value)
         >>>         # Evaluate with the respect to distribution b
-        >>>         ans = self.b1('prob', value, probs_b)
+        >>>         ans = self.b1.prob(value, probs_b)
         >>>
-        >>>         # probs must be passed in through construct
-        >>>         ans = self.b2('prob', value, probs_a)
+        >>>         # probs must be passed in during function calls
+        >>>         ans = self.b2.prob(value, probs_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage like 'mean'
-        >>>         # Will return [0.0]
-        >>>         ans = self.b1('mean')
-        >>>         # Will return mean_b
-        >>>         ans = self.b1('mean', probs_b)
+        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
+        >>>         # Will return 0.5
+        >>>         ans = self.b1.mean()
+        >>>         # Will return probs_b
+        >>>         ans = self.b1.mean(probs_b)
         >>>
-        >>>         # probs must be passed in through construct
-        >>>         ans = self.b2('mean', probs_a)
+        >>>         # probs must be passed in during function calls
+        >>>         ans = self.b2.mean(probs_a)
         >>>
         >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
-        >>>         ans = self.b1('kl_loss', 'Bernoulli', probs_b)
-        >>>         ans = self.b1('kl_loss', 'Bernoulli', probs_b, probs_a)
+        >>>         ans = self.b1.kl_loss('Bernoulli', probs_b)
+        >>>         ans = self.b1.kl_loss('Bernoulli', probs_b, probs_a)
         >>>
-        >>>         # Additional probs_a must be passed in through construct
-        >>>         ans = self.b2('kl_loss', 'Bernoulli', probs_b, probs_a)
+        >>>         # Additional probs_a must be passed in through
+        >>>         ans = self.b2.kl_loss('Bernoulli', probs_b, probs_a)
         >>>
-        >>>         # Sample Usage
-        >>>         ans = self.b1('sample')
-        >>>         ans = self.b1('sample', (2,3))
-        >>>         ans = self.b1('sample', (2,3), probs_b)
-        >>>         ans = self.b2('sample', (2,3), probs_a)
+        >>>         # Sample
+        >>>         ans = self.b1.sample()
+        >>>         ans = self.b1.sample((2,3))
+        >>>         ans = self.b1.sample((2,3), probs_b)
+        >>>         ans = self.b2.sample((2,3), probs_a)
     """
 
     def __init__(self,
@@ -130,71 +131,61 @@ class Bernoulli(Distribution):
         """
         return self._probs
 
-    def _mean(self, name='mean', probs1=None):
+    def _mean(self, probs1=None):
         r"""
         .. math::
             MEAN(B) = probs1
         """
-        if name == 'mean':
-            return self.probs if probs1 is None else probs1
-        return None
+        return self.probs if probs1 is None else probs1
 
-    def _mode(self, name='mode', probs1=None):
+    def _mode(self, probs1=None):
         r"""
         .. math::
             MODE(B) = 1 if probs1 > 0.5 else = 0
         """
-        if name == 'mode':
-            probs1 = self.probs if probs1 is None else 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)
-            comp = self.less(0.5, probs1)
-            return self.select(comp, ones, zeros)
-        return None
+        probs1 = self.probs if probs1 is None else 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)
+        comp = self.less(0.5, probs1)
+        return self.select(comp, ones, zeros)
 
-    def _var(self, name='var', probs1=None):
+    def _var(self, probs1=None):
         r"""
         .. math::
             VAR(B) = probs1 * probs0
         """
-        if name in self._variance_functions:
-            probs1 = self.probs if probs1 is None else probs1
-            probs0 = 1.0 - probs1
-            return probs0 * probs1
-        return None
+        probs1 = self.probs if probs1 is None else probs1
+        probs0 = 1.0 - probs1
+        return probs0 * probs1
 
-    def _entropy(self, name='entropy', probs=None):
+    def _entropy(self, probs=None):
         r"""
         .. math::
             H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1)
         """
-        if name == 'entropy':
-            probs1 = self.probs if probs is None else probs
-            probs0 = 1 - probs1
-            return -1 * (probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
-        return None
+        probs1 = self.probs if probs is None else probs
+        probs0 = 1 - probs1
+        return -1 * (probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
 
-    def _cross_entropy(self, name, dist, probs1_b, probs1_a=None):
+    def _cross_entropy(self, dist, probs1_b, probs1_a=None):
         """
         Evaluate cross_entropy between Bernoulli distributions.
 
         Args:
-            name (str): name of the funtion.
             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.
         """
-        if name == 'cross_entropy' and dist == 'Bernoulli':
-            return self._entropy(probs=probs1_a) + self._kl_loss(name, dist, probs1_b, probs1_a)
+        if dist == 'Bernoulli':
+            return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
         return None
 
-    def _prob(self, name, value, probs=None):
+    def _prob(self, value, probs=None):
         r"""
         pmf of Bernoulli distribution.
 
         Args:
-            name (str): name of the function. Should be "prob" when passed in from construct.
             value (Tensor): a Tensor composed of only zeros and ones.
             probs (Tensor): probability of outcome is 1. Default: self.probs.
 
@@ -202,18 +193,15 @@ class Bernoulli(Distribution):
             pmf(k) = probs1 if k = 1;
             pmf(k) = probs0 if k = 0;
         """
-        if name in self._prob_functions:
-            probs1 = self.probs if probs is None else probs
-            probs0 = 1.0 - probs1
-            return (probs1 * value) + (probs0 * (1.0 - value))
-        return None
+        probs1 = self.probs if probs is None else probs
+        probs0 = 1.0 - probs1
+        return (probs1 * value) + (probs0 * (1.0 - value))
 
-    def _cdf(self, name, value, probs=None):
+    def _cdf(self, value, probs=None):
         r"""
         cdf of Bernoulli distribution.
 
         Args:
-            name (str): name of the function.
             value (Tensor): value to be evaluated.
             probs (Tensor): probability of outcome is 1. Default: self.probs.
 
@@ -222,25 +210,22 @@ class Bernoulli(Distribution):
             cdf(k) = probs0 if 0 <= k <1;
             cdf(k) = 1 if k >=1;
         """
-        if name in self._cdf_survival_functions:
-            probs1 = self.probs if probs is None else 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)
-            comp_zero = self.less(value, 0.0)
-            comp_one = self.less(value, 1.0)
-            zeros = self.fill(prob_type, self.shape(value), 0.0)
-            ones = self.fill(prob_type, self.shape(value), 1.0)
-            less_than_zero = self.select(comp_zero, zeros, probs0)
-            return self.select(comp_one, less_than_zero, ones)
-        return None
+        probs1 = self.probs if probs is None else 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)
+        comp_zero = self.less(value, 0.0)
+        comp_one = self.less(value, 1.0)
+        zeros = self.fill(prob_type, self.shape(value), 0.0)
+        ones = self.fill(prob_type, self.shape(value), 1.0)
+        less_than_zero = self.select(comp_zero, zeros, probs0)
+        return self.select(comp_one, less_than_zero, ones)
 
-    def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
+    def _kl_loss(self, dist, probs1_b, probs1_a=None):
         r"""
         Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
 
         Args:
-            name (str): name of the funtion.
             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.
@@ -249,31 +234,28 @@ class Bernoulli(Distribution):
             KL(a||b) = probs1_a * \log(\fract{probs1_a}{probs1_b}) +
                        probs0_a * \log(\fract{probs0_a}{probs0_b})
         """
-        if name in self._divergence_functions and dist == 'Bernoulli':
+        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
 
-    def _sample(self, name, shape=(), probs=None):
+    def _sample(self, shape=(), probs=None):
         """
         Sampling.
 
         Args:
-            name (str): name of the function. Should always be 'sample' when passed in from construct.
             shape (tuple): shape of the sample. Default: ().
             probs (Tensor): probs1 of the samples. Default: self.probs.
 
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        if name == 'sample':
-            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 = self.less(sample_uniform, probs1)
-            sample = self.cast(sample, self.dtype)
-            return sample
-        return None
+        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 = self.less(sample_uniform, probs1)
+        sample = self.cast(sample, self.dtype)
+        return sample

mindspore/nn/probability/distribution/distribution.py

@@ -27,11 +27,7 @@ class Distribution(Cell):
 
     Note:
         Derived class should override operations such as ,_mean, _prob,
-        and _log_prob. Functions should be called through construct when
-        used inside a network. Arguments should be passed in through *args
-        in the form  of function name followed by additional arguments.
-        Functions such as cdf and prob, require a value to be passed in while
-        functions such as mean and sd do not require arguments other than name.
+        and _log_prob. Arguments should be passed in through *args.
 
         Dist_spec_args are unique for each type of distribution. For example, mean and sd
         are the dist_spec_args for a Normal distribution.
@@ -73,11 +69,6 @@ class Distribution(Cell):
         self._set_log_survival()
         self._set_cross_entropy()
 
-        self._prob_functions = ('prob', 'log_prob')
-        self._cdf_survival_functions = ('cdf', 'log_cdf', 'survival_function', 'log_survival')
-        self._variance_functions = ('var', 'sd')
-        self._divergence_functions = ('kl_loss', 'cross_entropy')
-
     @property
     def name(self):
         return self._name
@@ -185,7 +176,7 @@ class Distribution(Cell):
         Evaluate the log probability(pdf or pmf) at the given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_log_prob(*args)
@@ -204,7 +195,7 @@ class Distribution(Cell):
         Evaluate the probability (pdf or pmf) at given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_prob(*args)
@@ -223,7 +214,7 @@ class Distribution(Cell):
         Evaluate the cdf at given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_cdf(*args)
@@ -260,7 +251,7 @@ class Distribution(Cell):
         Evaluate the log cdf at given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_log_cdf(*args)
@@ -279,7 +270,7 @@ class Distribution(Cell):
         Evaluate the survival function at given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_survival(*args)
@@ -307,7 +298,7 @@ class Distribution(Cell):
         Evaluate the log survival function at given value.
 
         Note:
-            Args must include name of the function and value.
+            Args must include value.
             Dist_spec_args are optional.
         """
         return self._call_log_survival(*args)
@@ -326,7 +317,7 @@ class Distribution(Cell):
         Evaluate the KL divergence, i.e. KL(a||b).
 
         Note:
-            Args must include name of the function, type of the distribution, parameters of distribution b.
+            Args must include type of the distribution, parameters of distribution b.
             Parameters for distribution a are optional.
         """
         return self._kl_loss(*args)
@@ -336,7 +327,7 @@ class Distribution(Cell):
         Evaluate the mean.
 
         Note:
-            Args must include the name of function. Dist_spec_args are optional.
+            Dist_spec_args are optional.
         """
         return self._mean(*args)
 
@@ -345,7 +336,7 @@ class Distribution(Cell):
         Evaluate the mode.
 
         Note:
-            Args must include the name of function. Dist_spec_args are optional.
+            Dist_spec_args are optional.
         """
         return self._mode(*args)
 
@@ -354,7 +345,7 @@ class Distribution(Cell):
         Evaluate the standard deviation.
 
         Note:
-            Args must include the name of function. Dist_spec_args are optional.
+            Dist_spec_args are optional.
         """
         return self._call_sd(*args)
 
@@ -363,7 +354,7 @@ class Distribution(Cell):
         Evaluate the variance.
 
         Note:
-            Args must include the name of function. Dist_spec_args are optional.
+            Dist_spec_args are optional.
         """
         return self._call_var(*args)
 
@@ -390,7 +381,7 @@ class Distribution(Cell):
         Evaluate the entropy.
 
         Note:
-            Args must include the name of function. Dist_spec_args are optional.
+            Dist_spec_args are optional.
         """
         return self._entropy(*args)
 
@@ -399,7 +390,7 @@ class Distribution(Cell):
         Evaluate the cross_entropy between distribution a and b.
 
         Note:
-            Args must include name of the function, type of the distribution, parameters of distribution b.
+            Args must include type of the distribution, parameters of distribution b.
             Parameters for distribution a are optional.
         """
         return self._call_cross_entropy(*args)
@@ -421,13 +412,13 @@ class Distribution(Cell):
             *args (list): arguments passed in through construct.
 
         Note:
-            Args must include name of the function.
-            Shape of the sample and dist_spec_args are optional.
+            Shape of the sample is default to ().
+            Dist_spec_args are optional.
         """
         return self._sample(*args)
 
 
-    def construct(self, *inputs):
+    def construct(self, name, *args):
         """
         Override construct in Cell.
 
@@ -437,35 +428,36 @@ class Distribution(Cell):
             'var', 'sd', 'entropy', 'kl_loss', 'cross_entropy', 'sample'.
 
         Args:
-            *inputs (list): inputs[0] is always the name of the function.
-        """
-
-        if inputs[0] == 'log_prob':
-            return self._call_log_prob(*inputs)
-        if inputs[0] == 'prob':
-            return self._call_prob(*inputs)
-        if inputs[0] == 'cdf':
-            return self._call_cdf(*inputs)
-        if inputs[0] == 'log_cdf':
-            return self._call_log_cdf(*inputs)
-        if inputs[0] == 'survival_function':
-            return self._call_survival(*inputs)
-        if inputs[0] == 'log_survival':
-            return self._call_log_survival(*inputs)
-        if inputs[0] == 'kl_loss':
-            return self._kl_loss(*inputs)
-        if inputs[0] == 'mean':
-            return self._mean(*inputs)
-        if inputs[0] == 'mode':
-            return self._mode(*inputs)
-        if inputs[0] == 'sd':
-            return self._call_sd(*inputs)
-        if inputs[0] == 'var':
-            return self._call_var(*inputs)
-        if inputs[0] == 'entropy':
-            return self._entropy(*inputs)
-        if inputs[0] == 'cross_entropy':
-            return self._call_cross_entropy(*inputs)
-        if inputs[0] == 'sample':
-            return self._sample(*inputs)
+            name (str): name of the function.
+            *args (list): list of arguments needed for the function.
+        """
+
+        if name == 'log_prob':
+            return self._call_log_prob(*args)
+        if name == 'prob':
+            return self._call_prob(*args)
+        if name == 'cdf':
+            return self._call_cdf(*args)
+        if name == 'log_cdf':
+            return self._call_log_cdf(*args)
+        if name == 'survival_function':
+            return self._call_survival(*args)
+        if name == 'log_survival':
+            return self._call_log_survival(*args)
+        if name == 'kl_loss':
+            return self._kl_loss(*args)
+        if name == 'mean':
+            return self._mean(*args)
+        if name == 'mode':
+            return self._mode(*args)
+        if name == 'sd':
+            return self._call_sd(*args)
+        if name == 'var':
+            return self._call_var(*args)
+        if name == 'entropy':
+            return self._entropy(*args)
+        if name == 'cross_entropy':
+            return self._call_cross_entropy(*args)
+        if name == 'sample':
+            return self._sample(*args)
         return None

mindspore/nn/probability/distribution/exponential.py

@@ -35,55 +35,56 @@ class Exponential(Distribution):
 
     Examples:
         >>> # To initialize an Exponential distribution of rate 0.5
-        >>> n = nn.Exponential(0.5, dtype=mstype.float32)
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> e = msd.Exponential(0.5, dtype=mstype.float32)
         >>>
         >>> # The following creates two independent Exponential distributions
-        >>> n = nn.Exponential([0.5, 0.5], dtype=mstype.float32)
+        >>> e = msd.Exponential([0.5, 0.5], dtype=mstype.float32)
         >>>
-        >>> # A Exponential distribution can be initilized without arguments
-        >>> # In this case, rate must be passed in through construct.
-        >>> n = nn.Exponential(dtype=mstype.float32)
+        >>> # An Exponential distribution can be initilized without arguments
+        >>> # In this case, rate must be passed in through args during function calls
+        >>> e = msd.Exponential(dtype=mstype.float32)
         >>>
-        >>> # To use Exponential distribution in a network
+        >>> # To use Exponential in a network
         >>> class net(Cell):
         >>>     def __init__(self):
         >>>         super(net, self).__init__():
-        >>>         self.e1 = nn.Exponential(0.5, dtype=mstype.float32)
-        >>>         self.e2 = nn.Exponential(dtype=mstype.float32)
+        >>>         self.e1 = msd.Exponential(0.5, dtype=mstype.float32)
+        >>>         self.e2 = msd.Exponential(dtype=mstype.float32)
         >>>
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, rate_b, rate_a):
         >>>
         >>>         # Similar calls can be made to other probability functions
         >>>         # by replacing 'prob' with the name of the function
-        >>>         ans = self.e1('prob', value)
+        >>>         ans = self.e1.prob(value)
         >>>         # Evaluate with the respect to distribution b
-        >>>         ans = self.e1('prob', value, rate_b)
+        >>>         ans = self.e1.prob(value, rate_b)
         >>>
-        >>>         # Rate must be passed in through construct
-        >>>         ans = self.e2('prob', value, rate_a)
+        >>>         # Rate must be passed in during function calls
+        >>>         ans = self.e2.prob(value, rate_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
-        >>>         # Will return [0.0]
-        >>>         ans = self.e1('mean')
-        >>>         # Will return mean_b
-        >>>         ans = self.e1('mean', rate_b)
+        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as'mean'
+        >>>         # Will return 2
+        >>>         ans = self.e1.mean()
+        >>>         # Will return 1 / rate_b
+        >>>         ans = self.e1.mean(rate_b)
         >>>
-        >>>         # Rate must be passed in through construct
-        >>>         ans = self.e2('mean', rate_a)
+        >>>         # Rate must be passed in during function calls
+        >>>         ans = self.e2.mean(rate_a)
         >>>
         >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
-        >>>         ans = self.e1('kl_loss', 'Exponential', rate_b)
-        >>>         ans = self.e1('kl_loss', 'Exponential', rate_b, rate_a)
+        >>>         ans = self.e1.kl_loss('Exponential', rate_b)
+        >>>         ans = self.e1.kl_loss('Exponential', rate_b, rate_a)
         >>>
-        >>>         # Additional rate must be passed in through construct
-        >>>         ans = self.e2('kl_loss', 'Exponential', rate_b, rate_a)
+        >>>         # Additional rate must be passed in
+        >>>         ans = self.e2.kl_loss('Exponential', rate_b, rate_a)
         >>>
-        >>>         # Sample Usage
-        >>>         ans = self.e1('sample')
-        >>>         ans = self.e1('sample', (2,3))
-        >>>         ans = self.e1('sample', (2,3), rate_b)
-        >>>         ans = self.e2('sample', (2,3), rate_a)
+        >>>         # Sample
+        >>>         ans = self.e1.sample()
+        >>>         ans = self.e1.sample((2,3))
+        >>>         ans = self.e1.sample((2,3), rate_b)
+        >>>         ans = self.e2.sample((2,3), rate_a)
     """
 
     def __init__(self,
@@ -131,67 +132,59 @@ class Exponential(Distribution):
         """
         return self._rate
 
-    def _mean(self, name='mean', rate=None):
+    def _mean(self, rate=None):
         r"""
         .. math::
             MEAN(EXP) = \fract{1.0}{\lambda}.
         """
-        if name == 'mean':
-            rate = self.rate if rate is None else rate
-            return 1.0 / rate
-        return None
+        rate = self.rate if rate is None else rate
+        return 1.0 / rate
 
-    def _mode(self, name='mode', rate=None):
+
+    def _mode(self, rate=None):
         r"""
         .. math::
             MODE(EXP) = 0.
         """
-        if name == 'mode':
-            rate = self.rate if rate is None else rate
-            return self.fill(self.dtype, self.shape(rate), 0.)
-        return None
+        rate = self.rate if rate is None else rate
+        return self.fill(self.dtype, self.shape(rate), 0.)
 
-    def _sd(self, name='sd', rate=None):
+    def _sd(self, rate=None):
         r"""
         .. math::
             sd(EXP) = \fract{1.0}{\lambda}.
         """
-        if name in self._variance_functions:
-            rate = self.rate if rate is None else rate
-            return 1.0 / rate
-        return None
+        rate = self.rate if rate is None else rate
+        return 1.0 / rate
 
-    def _entropy(self, name='entropy', rate=None):
+    def _entropy(self, rate=None):
         r"""
         .. math::
             H(Exp) = 1 - \log(\lambda).
         """
         rate = self.rate if rate is None else rate
-        if name == 'entropy':
-            return 1.0 - self.log(rate)
-        return None
+        return 1.0 - self.log(rate)
 
-    def _cross_entropy(self, name, dist, rate_b, rate_a=None):
+
+    def _cross_entropy(self, dist, rate_b, rate_a=None):
         """
         Evaluate cross_entropy between Exponential distributions.
 
         Args:
-            name (str): name of the funtion. Should always be "cross_entropy" when passed in from construct.
             dist (str): type of the distributions. Should be "Exponential" in this case.
             rate_b (Tensor): rate of distribution b.
             rate_a (Tensor): rate of distribution a. Default: self.rate.
         """
-        if name == 'cross_entropy' and dist == 'Exponential':
-            return self._entropy(rate=rate_a) + self._kl_loss(name, dist, rate_b, rate_a)
+        if dist == 'Exponential':
+            return self._entropy(rate=rate_a) + self._kl_loss(dist, rate_b, rate_a)
         return None
 
-    def _prob(self, name, value, rate=None):
+    def _prob(self, value, rate=None):
         r"""
         pdf of Exponential distribution.
 
         Args:
             Args:
-            name (str): name of the function.
             value (Tensor): value to be evaluated.
             rate (Tensor): rate of the distribution. Default: self.rate.
 
@@ -201,20 +194,17 @@ class Exponential(Distribution):
         .. math::
             pdf(x) = rate * \exp(-1 * \lambda * x) if x >= 0 else 0
         """
-        if name in self._prob_functions:
-            rate = self.rate if rate is None else rate
-            prob = rate * self.exp(-1. * rate * value)
-            zeros = self.fill(self.dtypeop(prob), self.shape(prob), 0.0)
-            comp = self.less(value, zeros)
-            return self.select(comp, zeros, prob)
-        return None
+        rate = self.rate if rate is None else rate
+        prob = rate * self.exp(-1. * rate * value)
+        zeros = self.fill(self.dtypeop(prob), self.shape(prob), 0.0)
+        comp = self.less(value, zeros)
+        return self.select(comp, zeros, prob)
 
-    def _cdf(self, name, value, rate=None):
+    def _cdf(self, value, rate=None):
         r"""
         cdf of Exponential distribution.
 
         Args:
-            name (str): name of the function.
             value (Tensor): value to be evaluated.
             rate (Tensor): rate of the distribution. Default: self.rate.
 
@@ -224,45 +214,40 @@ class Exponential(Distribution):
         .. math::
             cdf(x) = 1.0 - \exp(-1 * \lambda * x) if x >= 0 else 0
         """
-        if name in self._cdf_survival_functions:
-            rate = self.rate if rate is None else 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)
-            return self.select(comp, zeros, cdf)
-        return None
+        rate = self.rate if rate is None else 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)
+        return self.select(comp, zeros, cdf)
+
 
-    def _kl_loss(self, name, dist, rate_b, rate_a=None):
+    def _kl_loss(self, dist, rate_b, rate_a=None):
         """
         Evaluate exp-exp kl divergence, i.e. KL(a||b).
 
         Args:
-            name (str): name of the funtion.
             dist (str): type of the distributions. Should be "Exponential" in this case.
             rate_b (Tensor): rate of distribution b.
             rate_a (Tensor): rate of distribution a. Default: self.rate.
         """
-        if name in self._divergence_functions and dist == 'Exponential':
+        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
 
-    def _sample(self, name, shape=(), rate=None):
+    def _sample(self, shape=(), rate=None):
         """
         Sampling.
 
         Args:
-            name (str): name of the function.
             shape (tuple): shape of the sample. Default: ().
             rate (Tensor): rate of the distribution. Default: self.rate.
 
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        if name == 'sample':
-            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
-        return None
+        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

mindspore/nn/probability/distribution/geometric.py

@@ -36,55 +36,56 @@ class Geometric(Distribution):
 
     Examples:
         >>> # To initialize a Geometric distribution of prob 0.5
-        >>> n = nn.Geometric(0.5, dtype=mstype.int32)
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> n = msd.Geometric(0.5, dtype=mstype.int32)
         >>>
         >>> # The following creates two independent Geometric distributions
-        >>> n = nn.Geometric([0.5, 0.5], dtype=mstype.int32)
+        >>> n = msd.Geometric([0.5, 0.5], dtype=mstype.int32)
         >>>
         >>> # A Geometric distribution can be initilized without arguments
-        >>> # In this case, probs must be passed in through construct.
-        >>> n = nn.Geometric(dtype=mstype.int32)
+        >>> # In this case, probs must be passed in through args during function calls.
+        >>> n = msd.Geometric(dtype=mstype.int32)
         >>>
-        >>> # To use Geometric distribution in a network
+        >>> # To use Geometric in a network
         >>> class net(Cell):
         >>>     def __init__(self):
         >>>         super(net, self).__init__():
-        >>>         self.g1 = nn.Geometric(0.5, dtype=mstype.int32)
-        >>>         self.g2 = nn.Geometric(dtype=mstype.int32)
+        >>>         self.g1 = msd.Geometric(0.5, dtype=mstype.int32)
+        >>>         self.g2 = msd.Geometric(dtype=mstype.int32)
         >>>
         >>>     # Tthe following calls are valid in construct
         >>>     def construct(self, value, probs_b, probs_a):
         >>>
         >>>         # Similar calls can be made to other probability functions
         >>>         # by replacing 'prob' with the name of the function
-        >>>         ans = self.g1('prob', value)
+        >>>         ans = self.g1.prob(value)
         >>>         # Evaluate with the respect to distribution b
-        >>>         ans = self.g1('prob', value, probs_b)
+        >>>         ans = self.g1.prob(value, probs_b)
         >>>
-        >>>         # Probs must be passed in through construct
-        >>>         ans = self.g2('prob', value, probs_a)
+        >>>         # Probs must be passed in during function calls
+        >>>         ans = self.g2.prob(value, probs_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
-        >>>         # Will return [0.0]
-        >>>         ans = self.g1('mean')
-        >>>         # Will return mean_b
-        >>>         ans = self.g1('mean', probs_b)
+        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
+        >>>         # Will return 1.0
+        >>>         ans = self.g1.mean()
+        >>>         # Another possible usage
+        >>>         ans = self.g1.mean(probs_b)
         >>>
-        >>>         # Probs must be passed in through construct
-        >>>         ans = self.g2('mean', probs_a)
+        >>>         # Probs must be passed in during function calls
+        >>>         ans = self.g2.mean(probs_a)
         >>>
         >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
-        >>>         ans = self.g1('kl_loss', 'Geometric', probs_b)
-        >>>         ans = self.g1('kl_loss', 'Geometric', probs_b, probs_a)
+        >>>         ans = self.g1.kl_loss('Geometric', probs_b)
+        >>>         ans = self.g1.kl_loss('Geometric', probs_b, probs_a)
         >>>
-        >>>         # Additional probs must be passed in through construct
-        >>>         ans = self.g2('kl_loss', 'Geometric', probs_b, probs_a)
+        >>>         # Additional probs must be passed in
+        >>>         ans = self.g2.kl_loss('Geometric', probs_b, probs_a)
         >>>
-        >>>         # Sample Usage
-        >>>         ans = self.g1('sample')
-        >>>         ans = self.g1('sample', (2,3))
-        >>>         ans = self.g1('sample', (2,3), probs_b)
-        >>>         ans = self.g2('sample', (2,3), probs_a)
+        >>>         # Sample
+        >>>         ans = self.g1.sample()
+        >>>         ans = self.g1.sample((2,3))
+        >>>         ans = self.g1.sample((2,3), probs_b)
+        >>>         ans = self.g2.sample((2,3), probs_a)
     """
 
     def __init__(self,
@@ -134,67 +135,57 @@ class Geometric(Distribution):
         """
         return self._probs
 
-    def _mean(self, name='mean', probs1=None):
+    def _mean(self, probs1=None):
         r"""
         .. math::
             MEAN(Geo) = \fratc{1 - probs1}{probs1}
         """
-        if name == 'mean':
-            probs1 = self.probs if probs1 is None else probs1
-            return (1. - probs1) / probs1
-        return None
+        probs1 = self.probs if probs1 is None else probs1
+        return (1. - probs1) / probs1
 
-    def _mode(self, name='mode', probs1=None):
+    def _mode(self, probs1=None):
         r"""
         .. math::
             MODE(Geo) = 0
         """
-        if name == 'mode':
-            probs1 = self.probs if probs1 is None else probs1
-            return self.fill(self.dtypeop(probs1), self.shape(probs1), 0.)
-        return None
+        probs1 = self.probs if probs1 is None else probs1
+        return self.fill(self.dtypeop(probs1), self.shape(probs1), 0.)
 
-    def _var(self, name='var', probs1=None):
+    def _var(self, probs1=None):
         r"""
         .. math::
             VAR(Geo) = \fract{1 - probs1}{probs1 ^ {2}}
         """
-        if name in self._variance_functions:
-            probs1 = self.probs if probs1 is None else probs1
-            return (1.0 - probs1) / self.sq(probs1)
-        return None
+        probs1 = self.probs if probs1 is None else probs1
+        return (1.0 - probs1) / self.sq(probs1)
 
-    def _entropy(self, name='entropy', probs=None):
+    def _entropy(self, probs=None):
         r"""
         .. math::
             H(Geo) = \fract{-1 * probs0 \log_2 (1-probs0)\ - prob1 * \log_2 (1-probs1)\ }{probs1}
         """
-        if name == 'entropy':
-            probs1 = self.probs if probs is None else probs
-            probs0 = 1.0 - probs1
-            return (-probs0 * self.log(probs0) - probs1 * self.log(probs1)) / probs1
-        return None
+        probs1 = self.probs if probs is None else probs
+        probs0 = 1.0 - probs1
+        return (-probs0 * self.log(probs0) - probs1 * self.log(probs1)) / probs1
 
-    def _cross_entropy(self, name, dist, probs1_b, probs1_a=None):
+    def _cross_entropy(self, dist, probs1_b, probs1_a=None):
         r"""
         Evaluate cross_entropy between Geometric distributions.
 
         Args:
-            name (str): name of the funtion. Should always be "cross_entropy" when passed in from construct.
             dist (str): type of the distributions. Should be "Geometric" in this case.
             probs1_b (Tensor): probability of success of distribution b.
             probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
         """
-        if name == 'cross_entropy' and dist == 'Geometric':
-            return self._entropy(probs=probs1_a) + self._kl_loss(name, dist, probs1_b, probs1_a)
+        if dist == 'Geometric':
+            return self._entropy(probs=probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
         return None
 
-    def _prob(self, name, value, probs=None):
+    def _prob(self, value, probs=None):
         r"""
         pmf of Geometric distribution.
 
         Args:
-            name (str): name of the function. Should be "prob" when passed in from construct.
             value (Tensor): a Tensor composed of only natural numbers.
             probs (Tensor): probability of success. Default: self.probs.
 
@@ -202,27 +193,24 @@ class Geometric(Distribution):
             pmf(k) = probs0 ^k * probs1 if k >= 0;
             pmf(k) = 0 if k < 0.
         """
-        if name in self._prob_functions:
-            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
-            pmf = self.pow((1.0 - probs1), value) * probs1
-            zeros = self.fill(self.dtypeop(probs1), self.shape(pmf), 0.0)
-            comp = self.less(value, zeros)
-            return self.select(comp, zeros, pmf)
-        return None
+        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
+        pmf = self.pow((1.0 - probs1), value) * probs1
+        zeros = self.fill(self.dtypeop(probs1), self.shape(pmf), 0.0)
+        comp = self.less(value, zeros)
+        return self.select(comp, zeros, pmf)
 
-    def _cdf(self, name, value, probs=None):
+    def _cdf(self, value, probs=None):
         r"""
         cdf of Geometric distribution.
 
         Args:
-            name (str): name of the function.
             value (Tensor): a Tensor composed of only natural numbers.
             probs (Tensor): probability of success. Default: self.probs.
 
@@ -231,28 +219,26 @@ class Geometric(Distribution):
             cdf(k) = 0 if k < 0.
 
         """
-        if name in self._cdf_survival_functions:
-            probs1 = self.probs if probs is None else 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)
-            return self.select(comp, zeros, cdf)
-        return None
+        probs1 = self.probs if probs is None else 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)
+        return self.select(comp, zeros, cdf)
 
-    def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
+
+    def _kl_loss(self, dist, probs1_b, probs1_a=None):
         r"""
         Evaluate Geometric-Geometric kl divergence, i.e. KL(a||b).
 
         Args:
-            name (str): name of the funtion.
             dist (str): type of the distributions. Should be "Geometric" in this case.
             probs1_b (Tensor): probability of success of distribution b.
             probs1_a (Tensor): probability of success of distribution a. Default: self.probs.
@@ -260,29 +246,26 @@ class Geometric(Distribution):
         .. math::
             KL(a||b) = \log(\fract{probs1_a}{probs1_b}) + \fract{probs0_a}{probs1_a} * \log(\fract{probs0_a}{probs0_b})
         """
-        if name in self._divergence_functions and dist == 'Geometric':
+        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
 
-    def _sample(self, name, shape=(), probs=None):
+    def _sample(self, shape=(), probs=None):
         """
         Sampling.
 
         Args:
-            name (str): name of the function. Should always be 'sample' when passed in from construct.
             shape (tuple): shape of the sample. Default: ().
             probs (Tensor): probability of success. Default: self.probs.
 
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        if name == 'sample':
-            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))
-        return None
+        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))

mindspore/nn/probability/distribution/normal.py

@@ -17,7 +17,6 @@ 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 mindspore.context import get_context
 from .distribution import Distribution
 from ._utils.utils import convert_to_batch, check_greater_equal_zero
 
@@ -39,55 +38,56 @@ class Normal(Distribution):
 
     Examples:
         >>> # To initialize a Normal distribution of mean 3.0 and standard deviation 4.0
-        >>> n = nn.Normal(3.0, 4.0, dtype=mstype.float32)
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> n = msd.Normal(3.0, 4.0, dtype=mstype.float32)
         >>>
         >>> # The following creates two independent Normal distributions
-        >>> n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
+        >>> n = msd.Normal([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
         >>>
-        >>> # A normal distribution can be initilize without arguments
-        >>> # In this case, mean and sd must be passed in through construct.
-        >>> n = nn.Normal(dtype=mstype.float32)
+        >>> # A Normal distribution can be initilize without arguments
+        >>> # In this case, mean and sd must be passed in through args.
+        >>> n = msd.Normal(dtype=mstype.float32)
         >>>
-        >>> # To use normal in a network
+        >>> # To use Normal in a network
         >>> class net(Cell):
         >>>     def __init__(self):
         >>>         super(net, self).__init__():
-        >>>         self.n1 = nn.Normal(0.0, 1.0, dtype=mstype.float32)
-        >>>         self.n2 = nn.Normal(dtype=mstype.float32)
+        >>>         self.n1 = msd.Nomral(0.0, 1.0, dtype=mstype.float32)
+        >>>         self.n2 = msd.Normal(dtype=mstype.float32)
         >>>
         >>>     # The following calls are valid in construct
         >>>     def construct(self, value, mean_b, sd_b, mean_a, sd_a):
         >>>
         >>>         # Similar calls can be made to other probability functions
         >>>         # by replacing 'prob' with the name of the function
-        >>>         ans = self.n1('prob', value)
+        >>>         ans = self.n1.prob(value)
         >>>         # Evaluate with the respect to distribution b
-        >>>         ans = self.n1('prob', value, mean_b, sd_b)
+        >>>         ans = self.n1.prob(value, mean_b, sd_b)
         >>>
-        >>>         # mean and sd must be passed in through construct
-        >>>         ans = self.n2('prob', value, mean_a, sd_a)
+        >>>         # mean and sd must be passed in during function calls
+        >>>         ans = self.n2.prob(value, mean_a, sd_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
-        >>>         # Will return [0.0]
-        >>>         ans = self.n1('mean')
-        >>>         # Will return mean_b
-        >>>         ans = self.n1('mean', mean_b, sd_b)
+        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
+        >>>         # will return [0.0]
+        >>>         ans = self.n1.mean()
+        >>>         # will return mean_b
+        >>>         ans = self.n1.mean(mean_b, sd_b)
         >>>
-        >>>         # mean and sd must be passed in through construct
-        >>>         ans = self.n2('mean', mean_a, sd_a)
+        >>>         # mean and sd must be passed during function calls
+        >>>         ans = self.n2.mean(mean_a, sd_a)
         >>>
         >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
-        >>>         ans = self.n1('kl_loss', 'Normal', mean_b, sd_b)
-        >>>         ans = self.n1('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
+        >>>         ans = self.n1.kl_loss('Normal', mean_b, sd_b)
+        >>>         ans = self.n1.kl_loss('Normal', mean_b, sd_b, mean_a, sd_a)
         >>>
-        >>>         # Additional mean and sd must be passed in through construct
-        >>>         ans = self.n2('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
+        >>>         # Additional mean and sd must be passed
+        >>>         ans = self.n2.kl_loss('Normal', mean_b, sd_b, mean_a, sd_a)
         >>>
-        >>>         # Sample Usage
-        >>>         ans = self.n1('sample')
-        >>>         ans = self.n1('sample', (2,3))
-        >>>         ans = self.n1('sample', (2,3), mean_b, sd_b)
-        >>>         ans = self.n2('sample', (2,3), mean_a, sd_a)
+        >>>         # Sample
+        >>>         ans = self.n1.sample()
+        >>>         ans = self.n1.sample((2,3))
+        >>>         ans = self.n1.sample((2,3), mean_b, sd_b)
+        >>>         ans = self.n2.sample((2,3), mean_a, sd_a)
     """
 
     def __init__(self,
@@ -114,7 +114,7 @@ class Normal(Distribution):
         self.const = P.ScalarToArray()
         self.erf = P.Erf()
         self.exp = P.Exp()
-        self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
+        self.expm1 = self._expm1_by_step
         self.fill = P.Fill()
         self.log = P.Log()
         self.shape = P.Shape()
@@ -135,67 +135,57 @@ class Normal(Distribution):
         """
         return self.exp(x) - 1.0
 
-    def _mean(self, name='mean', mean=None, sd=None):
+    def _mean(self, mean=None, sd=None):
         """
         Mean of the distribution.
         """
-        if name == 'mean':
-            mean = self._mean_value if mean is None or sd is None else mean
-            return mean
-        return None
+        mean = self._mean_value if mean is None or sd is None else mean
+        return mean
 
-    def _mode(self, name='mode', mean=None, sd=None):
+    def _mode(self, mean=None, sd=None):
         """
         Mode of the distribution.
         """
-        if name == 'mode':
-            mean = self._mean_value if mean is None or sd is None else mean
-            return mean
-        return None
+        mean = self._mean_value if mean is None or sd is None else mean
+        return mean
 
-    def _sd(self, name='sd', mean=None, sd=None):
+    def _sd(self, mean=None, sd=None):
         """
         Standard deviation of the distribution.
         """
-        if name in self._variance_functions:
-            sd = self._sd_value if mean is None or sd is None else sd
-            return sd
-        return None
+        sd = self._sd_value if mean is None or sd is None else sd
+        return sd
 
-    def _entropy(self, name='entropy', sd=None):
+    def _entropy(self, sd=None):
         r"""
         Evaluate entropy.
 
         .. math::
             H(X) = \log(\sqrt(numpy.e * 2. * numpy.pi * \sq(\sigma)))
         """
-        if name == 'entropy':
-            sd = self._sd_value if sd is None else sd
-            return self.log(self.sqrt(np.e * 2. * np.pi * self.sq(sd)))
-        return None
+        sd = self._sd_value if sd is None else sd
+        return self.log(self.sqrt(np.e * 2. * np.pi * self.sq(sd)))
 
-    def _cross_entropy(self, name, dist, mean_b, sd_b, mean_a=None, sd_a=None):
+    def _cross_entropy(self, dist, mean_b, sd_b, mean_a=None, sd_a=None):
         r"""
         Evaluate cross_entropy between normal distributions.
 
         Args:
-            name (str): name of the funtion passed in from construct. Should always be "cross_entropy".
             dist (str): type of the distributions. Should be "Normal" in this case.
             mean_b (Tensor): mean of distribution b.
             sd_b (Tensor): standard deviation distribution b.
             mean_a (Tensor): mean of distribution a. Default: self._mean_value.
             sd_a (Tensor): standard deviation distribution a. Default: self._sd_value.
         """
-        if name == 'cross_entropy' and dist == 'Normal':
-            return self._entropy(sd=sd_a) + self._kl_loss(name, dist, mean_b, sd_b, mean_a, sd_a)
+        if dist == 'Normal':
+            return self._entropy(sd=sd_a) + self._kl_loss(dist, mean_b, sd_b, mean_a, sd_a)
         return None
 
-    def _log_prob(self, name, value, mean=None, sd=None):
+    def _log_prob(self, value, mean=None, sd=None):
         r"""
         Evaluate log probability.
 
         Args:
-            name (str): name of the funtion passed in from construct.
             value (Tensor): value to be evaluated.
             mean (Tensor): mean of the distribution. Default: self._mean_value.
             sd (Tensor): standard deviation the distribution. Default: self._sd_value.
@@ -203,20 +193,17 @@ class Normal(Distribution):
         .. math::
             L(x) = -1* \fract{(x - \mu)^2}{2. * \sigma^2} - \log(\sqrt(2* \pi * \sigma^2))
         """
-        if name in self._prob_functions:
-            mean = self._mean_value if mean is None else mean
-            sd = self._sd_value if sd is None else sd
-            unnormalized_log_prob = -1. * (self.sq(value - mean)) / (2. * self.sq(sd))
-            neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
-            return unnormalized_log_prob + neg_normalization
-        return None
+        mean = self._mean_value if mean is None else mean
+        sd = self._sd_value if sd is None else sd
+        unnormalized_log_prob = -1. * (self.sq(value - mean)) / (2. * self.sq(sd))
+        neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
+        return unnormalized_log_prob + neg_normalization
 
-    def _cdf(self, name, value, mean=None, sd=None):
+    def _cdf(self, value, mean=None, sd=None):
         r"""
         Evaluate cdf of given value.
 
         Args:
-            name (str): name of the funtion passed in from construct. Should always be "cdf".
             value (Tensor): value to be evaluated.
             mean (Tensor): mean of the distribution. Default: self._mean_value.
             sd (Tensor): standard deviation the distribution. Default: self._sd_value.
@@ -224,20 +211,17 @@ class Normal(Distribution):
         .. math::
             cdf(x) = 0.5 * (1+ Erf((x - \mu) / ( \sigma * \sqrt(2))))
         """
-        if name in self._cdf_survival_functions:
-            mean = self._mean_value if mean is None else mean
-            sd = self._sd_value if sd is None else sd
-            sqrt2 = self.sqrt(self.const(2.0))
-            adjusted = (value - mean) / (sd * sqrt2)
-            return 0.5 * (1.0 + self.erf(adjusted))
-        return None
+        mean = self._mean_value if mean is None else mean
+        sd = self._sd_value if sd is None else sd
+        sqrt2 = self.sqrt(self.const(2.0))
+        adjusted = (value - mean) / (sd * sqrt2)
+        return 0.5 * (1.0 + self.erf(adjusted))
 
-    def _kl_loss(self, name, dist, mean_b, sd_b, mean_a=None, sd_a=None):
+    def _kl_loss(self, dist, mean_b, sd_b, mean_a=None, sd_a=None):
         r"""
         Evaluate Normal-Normal kl divergence, i.e. KL(a||b).
 
         Args:
-            name (str): name of the funtion passed in from construct.
             dist (str): type of the distributions. Should be "Normal" in this case.
             mean_b (Tensor): mean of distribution b.
             sd_b (Tensor): standard deviation distribution b.
@@ -248,7 +232,7 @@ class Normal(Distribution):
             KL(a||b) = 0.5 * (\fract{MEAN(a)}{STD(b)} - \fract{MEAN(b)}{STD(b)}) ^ 2 +
                        0.5 * EXPM1(2 * (\log(STD(a)) - \log(STD(b))) - (\log(STD(a)) - \log(STD(b)))
         """
-        if name in self._divergence_functions and dist == 'Normal':
+        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)
@@ -256,12 +240,11 @@ class Normal(Distribution):
             return 0.5 * squared_diff + 0.5 * self.expm1(2 * diff_log_scale) - diff_log_scale
         return None
 
-    def _sample(self, name, shape=(), mean=None, sd=None):
+    def _sample(self, shape=(), mean=None, sd=None):
         """
         Sampling.
 
         Args:
-            name (str): name of the function. Should always be 'sample' when passed in from construct.
             shape (tuple): shape of the sample. Default: ().
             mean (Tensor): mean of the samples. Default: self._mean_value.
             sd (Tensor): standard deviation of the samples. Default: self._sd_value.
@@ -269,14 +252,12 @@ class Normal(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        if name == 'sample':
-            mean = self._mean_value if mean is None else mean
-            sd = self._sd_value if sd is None else sd
-            batch_shape = self.shape(self.zeroslike(mean) + self.zeroslike(sd))
-            sample_shape = shape + batch_shape
-            mean_zero = self.const(0.0)
-            sd_one = self.const(1.0)
-            sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
-            sample = mean + sample_norm * sd
-            return sample
-        return None
+        mean = self._mean_value if mean is None else mean
+        sd = self._sd_value if sd is None else sd
+        batch_shape = self.shape(self.zeroslike(mean) + self.zeroslike(sd))
+        sample_shape = shape + batch_shape
+        mean_zero = self.const(0.0)
+        sd_one = self.const(1.0)
+        sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
+        sample = mean + sample_norm * sd
+        return sample

mindspore/nn/probability/distribution/uniform.py

@@ -35,55 +35,56 @@ class Uniform(Distribution):
 
     Examples:
         >>> # To initialize a Uniform distribution of mean 3.0 and standard deviation 4.0
-        >>> n = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> u = msd.Uniform(0.0, 1.0, dtype=mstype.float32)
         >>>
         >>> # The following creates two independent Uniform distributions
-        >>> n = nn.Uniform([0.0, 0.0], [1.0, 2.0], dtype=mstype.float32)
+        >>> u = msd.Uniform([0.0, 0.0], [1.0, 2.0], dtype=mstype.float32)
         >>>
         >>> # A Uniform distribution can be initilized without arguments
-        >>> # In this case, high and low must be passed in through construct.
-        >>> n = nn.Uniform(dtype=mstype.float32)
+        >>> # In this case, high and low must be passed in through args during function calls.
+        >>> u = msd.Uniform(dtype=mstype.float32)
         >>>
         >>> # To use Uniform in a network
         >>> class net(Cell):
         >>>     def __init__(self)
         >>>         super(net, self).__init__():
-        >>>         self.u1 = nn.Uniform(0.0, 1.0, dtype=mstype.float32)
-        >>>         self.u2 = nn.Uniform(dtype=mstype.float32)
+        >>>         self.u1 = msd.Uniform(0.0, 1.0, dtype=mstype.float32)
+        >>>         self.u2 = msd.Uniform(dtype=mstype.float32)
         >>>
         >>>     # All the following calls in construct are valid
         >>>     def construct(self, value, low_b, high_b, low_a, high_a):
         >>>
         >>>         # Similar calls can be made to other probability functions
         >>>         # by replacing 'prob' with the name of the function
-        >>>         ans = self.u1('prob', value)
+        >>>         ans = self.u1.prob(value)
         >>>         # Evaluate with the respect to distribution b
-        >>>         ans = self.u1('prob', value, low_b, high_b)
+        >>>         ans = self.u1.prob(value, low_b, high_b)
         >>>
-        >>>         # High and low must be passed in through construct
-        >>>         ans = self.u2('prob', value, low_a, high_a)
+        >>>         # High and low must be passed in during function calls
+        >>>         ans = self.u2.prob(value, low_a, high_a)
         >>>
-        >>>         # Functions 'sd', 'var', 'entropy' have the same usage with 'mean'
-        >>>         # Will return [0.0]
-        >>>         ans = self.u1('mean')
-        >>>         # Will return low_b
-        >>>         ans = self.u1('mean', low_b, high_b)
+        >>>         # Functions 'sd', 'var', 'entropy' have the same usage as 'mean'
+        >>>         # Will return 0.5
+        >>>         ans = self.u1.mean()
+        >>>         # Will return (low_b + high_b) / 2
+        >>>         ans = self.u1.mean(low_b, high_b)
         >>>
-        >>>         # High and low must be passed in through construct
-        >>>         ans = self.u2('mean', low_a, high_a)
+        >>>         # High and low must be passed in during function calls
+        >>>         ans = self.u2.mean(low_a, high_a)
         >>>
         >>>         # Usage of 'kl_loss' and 'cross_entropy' are similar
-        >>>         ans = self.u1('kl_loss', 'Uniform', low_b, high_b)
-        >>>         ans = self.u1('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
+        >>>         ans = self.u1.kl_loss('Uniform', low_b, high_b)
+        >>>         ans = self.u1.kl_loss('Uniform', low_b, high_b, low_a, high_a)
         >>>
-        >>>         # Additional high and low must be passed in through construct
-        >>>         ans = self.u2('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
+        >>>         # Additional high and low must be passed
+        >>>         ans = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
         >>>
-        >>>         # Sample Usage
-        >>>         ans = self.u1('sample')
-        >>>         ans = self.u1('sample', (2,3))
-        >>>         ans = self.u1('sample', (2,3), low_b, high_b)
-        >>>         ans = self.u2('sample', (2,3), low_a, high_a)
+        >>>         # Sample
+        >>>         ans = self.u1.sample()
+        >>>         ans = self.u1.sample((2,3))
+        >>>         ans = self.u1.sample((2,3), low_b, high_b)
+        >>>         ans = self.u2.sample((2,3), low_a, high_a)
     """
 
     def __init__(self,
@@ -142,73 +143,64 @@ class Uniform(Distribution):
         """
         return self._high
 
-    def _range(self, name='range', low=None, high=None):
+    def _range(self, low=None, high=None):
         r"""
         Return the range of the distribution.
         .. math::
             range(U) = high -low
         """
-        if name == 'range':
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            return high - low
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        return high - low
 
-    def _mean(self, name='mean', low=None, high=None):
+    def _mean(self, low=None, high=None):
         r"""
         .. math::
             MEAN(U) = \fract{low + high}{2}.
         """
-        if name == 'mean':
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            return (low + high) / 2.
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        return (low + high) / 2.
 
-    def _var(self, name='var', low=None, high=None):
+
+    def _var(self, low=None, high=None):
         r"""
         .. math::
             VAR(U) = \fract{(high -low) ^ 2}{12}.
         """
-        if name in self._variance_functions:
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            return self.sq(high - low) / 12.0
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        return self.sq(high - low) / 12.0
 
-    def _entropy(self, name='entropy', low=None, high=None):
+    def _entropy(self, low=None, high=None):
         r"""
         .. math::
             H(U) = \log(high - low).
         """
-        if name == 'entropy':
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            return self.log(high - low)
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        return self.log(high - low)
 
-    def _cross_entropy(self, name, dist, low_b, high_b, low_a=None, high_a=None):
+    def _cross_entropy(self, dist, low_b, high_b, low_a=None, high_a=None):
         """
         Evaluate cross_entropy between Uniform distributoins.
 
         Args:
-            name (str): name of the funtion.
             dist (str): type of the distributions. Should be "Uniform" in this case.
             low_b (Tensor): lower bound of distribution b.
             high_b (Tensor): upper bound of distribution b.
             low_a (Tensor): lower bound of distribution a. Default: self.low.
             high_a (Tensor): upper bound of distribution a. Default: self.high.
         """
-        if name == 'cross_entropy' and dist == 'Uniform':
-            return self._entropy(low=low_a, high=high_a) + self._kl_loss(name, dist, low_b, high_b, low_a, high_a)
+        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
 
-    def _prob(self, name, value, low=None, high=None):
+    def _prob(self, value, low=None, high=None):
         r"""
         pdf of Uniform distribution.
 
         Args:
-            name (str): name of the function.
             value (Tensor): value to be evaluated.
             low (Tensor): lower bound of the distribution. Default: self.low.
             high (Tensor): upper bound of the distribution. Default: self.high.
@@ -218,32 +210,29 @@ class Uniform(Distribution):
             pdf(x) = \fract{1.0}{high -low} if low <= x <= high;
             pdf(x) = 0 if x > high;
         """
-        if name in self._prob_functions:
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            ones = self.fill(self.dtype, self.shape(value), 1.0)
-            prob = ones / (high - low)
-            broadcast_shape = self.shape(prob)
-            zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
-            comp_lo = self.less(value, low)
-            comp_hi = self.lessequal(value, high)
-            less_than_low = self.select(comp_lo, zeros, prob)
-            return self.select(comp_hi, less_than_low, zeros)
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        ones = self.fill(self.dtype, self.shape(value), 1.0)
+        prob = ones / (high - low)
+        broadcast_shape = self.shape(prob)
+        zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
+        comp_lo = self.less(value, low)
+        comp_hi = self.lessequal(value, high)
+        less_than_low = self.select(comp_lo, zeros, prob)
+        return self.select(comp_hi, less_than_low, zeros)
 
-    def _kl_loss(self, name, dist, low_b, high_b, low_a=None, high_a=None):
+    def _kl_loss(self, dist, low_b, high_b, low_a=None, high_a=None):
         """
         Evaluate uniform-uniform kl divergence, i.e. KL(a||b).
 
         Args:
-            name (str): name of the funtion.
             dist (str): type of the distributions. Should be "Uniform" in this case.
             low_b (Tensor): lower bound of distribution b.
             high_b (Tensor): upper bound of distribution b.
             low_a (Tensor): lower bound of distribution a. Default: self.low.
             high_a (Tensor): upper bound of distribution a. Default: self.high.
         """
-        if name in self._divergence_functions and dist == 'Uniform':
+        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)
@@ -251,12 +240,11 @@ class Uniform(Distribution):
             return self.select(comp, kl, self.log(self.zeroslike(kl)))
         return None
 
-    def _cdf(self, name, value, low=None, high=None):
+    def _cdf(self, value, low=None, high=None):
         r"""
         cdf of Uniform distribution.
 
         Args:
-            name (str): name of the function.
             value (Tensor): value to be evaluated.
             low (Tensor): lower bound of the distribution. Default: self.low.
             high (Tensor): upper bound of the distribution. Default: self.high.
@@ -266,25 +254,22 @@ class Uniform(Distribution):
             cdf(x) = \fract{x - low}{high -low} if low <= x <= high;
             cdf(x) = 1 if x > high;
         """
-        if name in self._cdf_survival_functions:
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            prob = (value - low) / (high - low)
-            broadcast_shape = self.shape(prob)
-            zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
-            ones = self.fill(self.dtypeop(prob), broadcast_shape, 1.0)
-            comp_lo = self.less(value, low)
-            comp_hi = self.less(value, high)
-            less_than_low = self.select(comp_lo, zeros, prob)
-            return self.select(comp_hi, less_than_low, ones)
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        prob = (value - low) / (high - low)
+        broadcast_shape = self.shape(prob)
+        zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
+        ones = self.fill(self.dtypeop(prob), broadcast_shape, 1.0)
+        comp_lo = self.less(value, low)
+        comp_hi = self.less(value, high)
+        less_than_low = self.select(comp_lo, zeros, prob)
+        return self.select(comp_hi, less_than_low, ones)
 
-    def _sample(self, name, shape=(), low=None, high=None):
+    def _sample(self, shape=(), low=None, high=None):
         """
         Sampling.
 
         Args:
-            name (str): name of the function. Should always be 'sample' when passed in from construct.
             shape (tuple): shape of the sample. Default: ().
             low (Tensor): lower bound of the distribution. Default: self.low.
             high (Tensor): upper bound of the distribution. Default: self.high.
@@ -292,13 +277,11 @@ class Uniform(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        if name == 'sample':
-            low = self.low if low is None else low
-            high = self.high if high is None else high
-            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 = (high - low) * sample_uniform + low
-            return sample
-        return None
+        low = self.low if low is None else low
+        high = self.high if high is None else high
+        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 = (high - low) * sample_uniform + low
+        return sample

tests/st/ops/ascend/test_distribution/test_bernoulli.py

@@ -19,7 +19,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -32,9 +31,8 @@ class Prob(nn.Cell):
         super(Prob, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('prob', x_)
+        return self.b.prob(x_)
 
 def test_pmf():
     """
@@ -57,9 +55,8 @@ class LogProb(nn.Cell):
         super(LogProb, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('log_prob', x_)
+        return self.b.log_prob(x_)
 
 def test_log_likelihood():
     """
@@ -81,9 +78,8 @@ class KL(nn.Cell):
         super(KL, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('kl_loss', 'Bernoulli', x_)
+        return self.b.kl_loss('Bernoulli', x_)
 
 def test_kl_loss():
     """
@@ -107,9 +103,8 @@ class Basics(nn.Cell):
         super(Basics, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5, 0.7], dtype=dtype.int32)
 
-    @ms_function
     def construct(self):
-        return self.b('mean'), self.b('sd'), self.b('mode')
+        return self.b.mean(), self.b.sd(), self.b.mode()
 
 def test_basics():
     """
@@ -134,9 +129,8 @@ class Sampling(nn.Cell):
         self.b = msd.Bernoulli([0.7, 0.5], seed=seed, dtype=dtype.int32)
         self.shape = shape
 
-    @ms_function
     def construct(self, probs=None):
-        return self.b('sample', self.shape, probs)
+        return self.b.sample(self.shape, probs)
 
 def test_sample():
     """
@@ -155,9 +149,8 @@ class CDF(nn.Cell):
         super(CDF, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('cdf', x_)
+        return self.b.cdf(x_)
 
 def test_cdf():
     """
@@ -171,7 +164,6 @@ def test_cdf():
     tol = 1e-6
     assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
 
-
 class LogCDF(nn.Cell):
     """
     Test class: log cdf of  bernoulli distributions.
@@ -180,9 +172,8 @@ class LogCDF(nn.Cell):
         super(LogCDF, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('log_cdf', x_)
+        return self.b.log_cdf(x_)
 
 def test_logcdf():
     """
@@ -205,9 +196,8 @@ class SF(nn.Cell):
         super(SF, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('survival_function', x_)
+        return self.b.survival_function(x_)
 
 def test_survival():
     """
@@ -230,9 +220,8 @@ class LogSF(nn.Cell):
         super(LogSF, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.b('log_survival', x_)
+        return self.b.log_survival(x_)
 
 def test_log_survival():
     """
@@ -254,9 +243,8 @@ class EntropyH(nn.Cell):
         super(EntropyH, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self):
-        return self.b('entropy')
+        return self.b.entropy()
 
 def test_entropy():
     """
@@ -277,12 +265,11 @@ class CrossEntropy(nn.Cell):
         super(CrossEntropy, self).__init__()
         self.b = msd.Bernoulli(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        entropy = self.b('entropy')
-        kl_loss = self.b('kl_loss', 'Bernoulli', x_)
+        entropy = self.b.entropy()
+        kl_loss = self.b.kl_loss('Bernoulli', x_)
         h_sum_kl = entropy + kl_loss
-        cross_entropy = self.b('cross_entropy', 'Bernoulli', x_)
+        cross_entropy = self.b.cross_entropy('Bernoulli', x_)
         return h_sum_kl - cross_entropy
 
 def test_cross_entropy():

tests/st/ops/ascend/test_distribution/test_exponential.py

@@ -19,7 +19,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -32,9 +31,8 @@ class Prob(nn.Cell):
         super(Prob, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('prob', x_)
+        return self.e.prob(x_)
 
 def test_pdf():
     """
@@ -56,9 +54,8 @@ class LogProb(nn.Cell):
         super(LogProb, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('log_prob', x_)
+        return self.e.log_prob(x_)
 
 def test_log_likelihood():
     """
@@ -80,9 +77,8 @@ class KL(nn.Cell):
         super(KL, self).__init__()
         self.e = msd.Exponential([1.5], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('kl_loss', 'Exponential', x_)
+        return self.e.kl_loss('Exponential', x_)
 
 def test_kl_loss():
     """
@@ -104,9 +100,8 @@ class Basics(nn.Cell):
         super(Basics, self).__init__()
         self.e = msd.Exponential([0.5], dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.e('mean'), self.e('sd'), self.e('mode')
+        return self.e.mean(), self.e.sd(), self.e.mode()
 
 def test_basics():
     """
@@ -131,9 +126,8 @@ class Sampling(nn.Cell):
         self.e = msd.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
         self.shape = shape
 
-    @ms_function
     def construct(self, rate=None):
-        return self.e('sample', self.shape, rate)
+        return self.e.sample(self.shape, rate)
 
 def test_sample():
     """
@@ -154,9 +148,8 @@ class CDF(nn.Cell):
         super(CDF, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('cdf', x_)
+        return self.e.cdf(x_)
 
 def test_cdf():
     """
@@ -178,9 +171,8 @@ class LogCDF(nn.Cell):
         super(LogCDF, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('log_cdf', x_)
+        return self.e.log_cdf(x_)
 
 def test_log_cdf():
     """
@@ -202,9 +194,8 @@ class SF(nn.Cell):
         super(SF, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('survival_function', x_)
+        return self.e.survival_function(x_)
 
 def test_survival():
     """
@@ -226,9 +217,8 @@ class LogSF(nn.Cell):
         super(LogSF, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.e('log_survival', x_)
+        return self.e.log_survival(x_)
 
 def test_log_survival():
     """
@@ -250,9 +240,8 @@ class EntropyH(nn.Cell):
         super(EntropyH, self).__init__()
         self.e = msd.Exponential([[1.0], [0.5]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.e('entropy')
+        return self.e.entropy()
 
 def test_entropy():
     """
@@ -273,12 +262,11 @@ class CrossEntropy(nn.Cell):
         super(CrossEntropy, self).__init__()
         self.e = msd.Exponential([1.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        entropy = self.e('entropy')
-        kl_loss = self.e('kl_loss', 'Exponential', x_)
+        entropy = self.e.entropy()
+        kl_loss = self.e.kl_loss('Exponential', x_)
         h_sum_kl = entropy + kl_loss
-        cross_entropy = self.e('cross_entropy', 'Exponential', x_)
+        cross_entropy = self.e.cross_entropy('Exponential', x_)
         return h_sum_kl - cross_entropy
 
 def test_cross_entropy():

tests/st/ops/ascend/test_distribution/test_geometric.py

@@ -19,7 +19,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -32,9 +31,8 @@ class Prob(nn.Cell):
         super(Prob, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('prob', x_)
+        return self.g.prob(x_)
 
 def test_pmf():
     """
@@ -56,9 +54,8 @@ class LogProb(nn.Cell):
         super(LogProb, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('log_prob', x_)
+        return self.g.log_prob(x_)
 
 def test_log_likelihood():
     """
@@ -80,9 +77,8 @@ class KL(nn.Cell):
         super(KL, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('kl_loss', 'Geometric', x_)
+        return self.g.kl_loss('Geometric', x_)
 
 def test_kl_loss():
     """
@@ -106,9 +102,8 @@ class Basics(nn.Cell):
         super(Basics, self).__init__()
         self.g = msd.Geometric([0.5, 0.5], dtype=dtype.int32)
 
-    @ms_function
     def construct(self):
-        return self.g('mean'), self.g('sd'), self.g('mode')
+        return self.g.mean(), self.g.sd(), self.g.mode()
 
 def test_basics():
     """
@@ -133,9 +128,8 @@ class Sampling(nn.Cell):
         self.g = msd.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
         self.shape = shape
 
-    @ms_function
     def construct(self, probs=None):
-        return self.g('sample', self.shape, probs)
+        return self.g.sample(self.shape, probs)
 
 def test_sample():
     """
@@ -154,9 +148,8 @@ class CDF(nn.Cell):
         super(CDF, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('cdf', x_)
+        return self.g.cdf(x_)
 
 def test_cdf():
     """
@@ -178,9 +171,8 @@ class LogCDF(nn.Cell):
         super(LogCDF, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('log_cdf', x_)
+        return self.g.log_cdf(x_)
 
 def test_logcdf():
     """
@@ -202,9 +194,8 @@ class SF(nn.Cell):
         super(SF, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('survival_function', x_)
+        return self.g.survival_function(x_)
 
 def test_survival():
     """
@@ -226,9 +217,8 @@ class LogSF(nn.Cell):
         super(LogSF, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        return self.g('log_survival', x_)
+        return self.g.log_survival(x_)
 
 def test_log_survival():
     """
@@ -250,9 +240,8 @@ class EntropyH(nn.Cell):
         super(EntropyH, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self):
-        return self.g('entropy')
+        return self.g.entropy()
 
 def test_entropy():
     """
@@ -273,12 +262,11 @@ class CrossEntropy(nn.Cell):
         super(CrossEntropy, self).__init__()
         self.g = msd.Geometric(0.7, dtype=dtype.int32)
 
-    @ms_function
     def construct(self, x_):
-        entropy = self.g('entropy')
-        kl_loss = self.g('kl_loss', 'Geometric', x_)
+        entropy = self.g.entropy()
+        kl_loss = self.g.kl_loss('Geometric', x_)
         h_sum_kl = entropy + kl_loss
-        ans = self.g('cross_entropy', 'Geometric', x_)
+        ans = self.g.cross_entropy('Geometric', x_)
         return h_sum_kl - ans
 
 def test_cross_entropy():

tests/st/ops/ascend/test_distribution/test_normal.py

@@ -19,7 +19,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -32,9 +31,8 @@ class Prob(nn.Cell):
         super(Prob, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('prob', x_)
+        return self.n.prob(x_)
 
 def test_pdf():
     """
@@ -55,9 +53,8 @@ class LogProb(nn.Cell):
         super(LogProb, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('log_prob', x_)
+        return self.n.log_prob(x_)
 
 def test_log_likelihood():
     """
@@ -79,9 +76,8 @@ class KL(nn.Cell):
         super(KL, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_, y_):
-        return self.n('kl_loss', 'Normal', x_, y_)
+        return self.n.kl_loss('Normal', x_, y_)
 
 
 def test_kl_loss():
@@ -113,9 +109,8 @@ class Basics(nn.Cell):
         super(Basics, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.n('mean'), self.n('sd'), self.n('mode')
+        return self.n.mean(), self.n.sd(), self.n.mode()
 
 def test_basics():
     """
@@ -139,9 +134,8 @@ class Sampling(nn.Cell):
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
         self.shape = shape
 
-    @ms_function
     def construct(self, mean=None, sd=None):
-        return self.n('sample', self.shape, mean, sd)
+        return self.n.sample(self.shape, mean, sd)
 
 def test_sample():
     """
@@ -163,9 +157,8 @@ class CDF(nn.Cell):
         super(CDF, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('cdf', x_)
+        return self.n.cdf(x_)
 
 
 def test_cdf():
@@ -187,9 +180,8 @@ class LogCDF(nn.Cell):
         super(LogCDF, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('log_cdf', x_)
+        return self.n.log_cdf(x_)
 
 def test_log_cdf():
     """
@@ -210,9 +202,8 @@ class SF(nn.Cell):
         super(SF, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('survival_function', x_)
+        return self.n.survival_function(x_)
 
 def test_survival():
     """
@@ -233,9 +224,8 @@ class LogSF(nn.Cell):
         super(LogSF, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.n('log_survival', x_)
+        return self.n.log_survival(x_)
 
 def test_log_survival():
     """
@@ -256,9 +246,8 @@ class EntropyH(nn.Cell):
         super(EntropyH, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.n('entropy')
+        return self.n.entropy()
 
 def test_entropy():
     """
@@ -279,12 +268,11 @@ class CrossEntropy(nn.Cell):
         super(CrossEntropy, self).__init__()
         self.n = msd.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_, y_):
-        entropy = self.n('entropy')
-        kl_loss = self.n('kl_loss', 'Normal', x_, y_)
+        entropy = self.n.entropy()
+        kl_loss = self.n.kl_loss('Normal', x_, y_)
         h_sum_kl = entropy + kl_loss
-        cross_entropy = self.n('cross_entropy', 'Normal', x_, y_)
+        cross_entropy = self.n.cross_entropy('Normal', x_, y_)
         return h_sum_kl - cross_entropy
 
 def test_cross_entropy():
@@ -297,3 +285,40 @@ def test_cross_entropy():
     diff = cross_entropy(mean, sd)
     tol = 1e-6
     assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()
+
+class Net(nn.Cell):
+    """
+    Test class: expand single distribution instance to multiple graphs
+    by specifying the attributes.
+    """
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.normal = msd.Normal(0., 1., dtype=dtype.float32)
+
+    def construct(self, x_, y_):
+        kl = self.normal.kl_loss('Normal', x_, y_)
+        prob = self.normal.prob(kl)
+        return prob
+
+def test_multiple_graphs():
+    """
+    Test multiple graphs case.
+    """
+    prob = Net()
+    mean_a = np.array([0.0]).astype(np.float32)
+    sd_a = np.array([1.0]).astype(np.float32)
+    mean_b = np.array([1.0]).astype(np.float32)
+    sd_b = np.array([1.0]).astype(np.float32)
+    ans = prob(Tensor(mean_b), Tensor(sd_b))
+
+    diff_log_scale = np.log(sd_a) - np.log(sd_b)
+    squared_diff = np.square(mean_a / sd_b - mean_b / sd_b)
+    expect_kl_loss = 0.5 * squared_diff + 0.5 * \
+        np.expm1(2 * diff_log_scale) - diff_log_scale
+
+    norm_benchmark = stats.norm(np.array([0.0]), np.array([1.0]))
+    expect_prob = norm_benchmark.pdf(expect_kl_loss).astype(np.float32)
+
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()

tests/st/ops/ascend/test_distribution/test_normal_new_api.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""test cases for new api of normal distribution"""
-import numpy as np
-from scipy import stats
-import mindspore.nn as nn
-import mindspore.nn.probability.distribution as msd
-from mindspore import dtype
-from mindspore import Tensor
-import mindspore.context as context
-
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
-class Net(nn.Cell):
-    """
-    Test class: new api of normal distribution.
-    """
-
-    def __init__(self):
-        super(Net, self).__init__()
-        self.normal = msd.Normal(0., 1., dtype=dtype.float32)
-
-    def construct(self, x_, y_):
-        kl = self.normal.kl_loss('kl_loss', 'Normal', x_, y_)
-        prob = self.normal.prob('prob', kl)
-        return prob
-
-
-def test_new_api():
-    """
-    Test new api of normal distribution.
-    """
-    prob = Net()
-    mean_a = np.array([0.0]).astype(np.float32)
-    sd_a = np.array([1.0]).astype(np.float32)
-    mean_b = np.array([1.0]).astype(np.float32)
-    sd_b = np.array([1.0]).astype(np.float32)
-    ans = prob(Tensor(mean_b), Tensor(sd_b))
-
-    diff_log_scale = np.log(sd_a) - np.log(sd_b)
-    squared_diff = np.square(mean_a / sd_b - mean_b / sd_b)
-    expect_kl_loss = 0.5 * squared_diff + 0.5 * \
-        np.expm1(2 * diff_log_scale) - diff_log_scale
-
-    norm_benchmark = stats.norm(np.array([0.0]), np.array([1.0]))
-    expect_prob = norm_benchmark.pdf(expect_kl_loss).astype(np.float32)
-
-    tol = 1e-6
-    assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()

tests/st/ops/ascend/test_distribution/test_uniform.py

@@ -19,7 +19,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.nn.probability.distribution as msd
 from mindspore import Tensor
-from mindspore.common.api import ms_function
 from mindspore import dtype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -32,9 +31,8 @@ class Prob(nn.Cell):
         super(Prob, self).__init__()
         self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('prob', x_)
+        return self.u.prob(x_)
 
 def test_pdf():
     """
@@ -56,9 +54,8 @@ class LogProb(nn.Cell):
         super(LogProb, self).__init__()
         self.u = msd.Uniform([0.0], [[1.0], [2.0]], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('log_prob', x_)
+        return self.u.log_prob(x_)
 
 def test_log_likelihood():
     """
@@ -80,9 +77,8 @@ class KL(nn.Cell):
         super(KL, self).__init__()
         self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_, y_):
-        return self.u('kl_loss', 'Uniform', x_, y_)
+        return self.u.kl_loss('Uniform', x_, y_)
 
 def test_kl_loss():
     """
@@ -106,9 +102,8 @@ class Basics(nn.Cell):
         super(Basics, self).__init__()
         self.u = msd.Uniform([0.0], [3.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.u('mean'), self.u('sd')
+        return self.u.mean(), self.u.sd()
 
 def test_basics():
     """
@@ -131,9 +126,8 @@ class Sampling(nn.Cell):
         self.u = msd.Uniform([0.0], [[1.0], [2.0]], seed=seed, dtype=dtype.float32)
         self.shape = shape
 
-    @ms_function
     def construct(self, low=None, high=None):
-        return self.u('sample', self.shape, low, high)
+        return self.u.sample(self.shape, low, high)
 
 def test_sample():
     """
@@ -155,9 +149,8 @@ class CDF(nn.Cell):
         super(CDF, self).__init__()
         self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('cdf', x_)
+        return self.u.cdf(x_)
 
 def test_cdf():
     """
@@ -179,9 +172,8 @@ class LogCDF(nn.Cell):
         super(LogCDF, self).__init__()
         self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('log_cdf', x_)
+        return self.u.log_cdf(x_)
 
 class SF(nn.Cell):
     """
@@ -191,9 +183,8 @@ class SF(nn.Cell):
         super(SF, self).__init__()
         self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('survival_function', x_)
+        return self.u.survival_function(x_)
 
 class LogSF(nn.Cell):
     """
@@ -203,9 +194,8 @@ class LogSF(nn.Cell):
         super(LogSF, self).__init__()
         self.u = msd.Uniform([0.0], [1.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_):
-        return self.u('log_survival', x_)
+        return self.u.log_survival(x_)
 
 class EntropyH(nn.Cell):
     """
@@ -215,9 +205,8 @@ class EntropyH(nn.Cell):
         super(EntropyH, self).__init__()
         self.u = msd.Uniform([0.0], [1.0, 2.0], dtype=dtype.float32)
 
-    @ms_function
     def construct(self):
-        return self.u('entropy')
+        return self.u.entropy()
 
 def test_entropy():
     """
@@ -238,12 +227,11 @@ class CrossEntropy(nn.Cell):
         super(CrossEntropy, self).__init__()
         self.u = msd.Uniform([0.0], [1.5], dtype=dtype.float32)
 
-    @ms_function
     def construct(self, x_, y_):
-        entropy = self.u('entropy')
-        kl_loss = self.u('kl_loss', 'Uniform', x_, y_)
+        entropy = self.u.entropy()
+        kl_loss = self.u.kl_loss('Uniform', x_, y_)
         h_sum_kl = entropy + kl_loss
-        cross_entropy = self.u('cross_entropy', 'Uniform', x_, y_)
+        cross_entropy = self.u.cross_entropy('Uniform', x_, y_)
         return h_sum_kl - cross_entropy
 
 def test_log_cdf():

tests/ut/python/nn/distribution/test_bernoulli.py

@@ -49,12 +49,12 @@ class BernoulliProb(nn.Cell):
         self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
 
     def construct(self, value):
-        prob = self.b('prob', value)
-        log_prob = self.b('log_prob', value)
-        cdf = self.b('cdf', value)
-        log_cdf = self.b('log_cdf', value)
-        sf = self.b('survival_function', value)
-        log_sf = self.b('log_survival', value)
+        prob = self.b.prob(value)
+        log_prob = self.b.log_prob(value)
+        cdf = self.b.cdf(value)
+        log_cdf = self.b.log_cdf(value)
+        sf = self.b.survival_function(value)
+        log_sf = self.b.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_bernoulli_prob():
@@ -75,12 +75,12 @@ class BernoulliProb1(nn.Cell):
         self.b = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, value, probs):
-        prob = self.b('prob', value, probs)
-        log_prob = self.b('log_prob', value, probs)
-        cdf = self.b('cdf', value, probs)
-        log_cdf = self.b('log_cdf', value, probs)
-        sf = self.b('survival_function', value, probs)
-        log_sf = self.b('log_survival', value, probs)
+        prob = self.b.prob(value, probs)
+        log_prob = self.b.log_prob(value, probs)
+        cdf = self.b.cdf(value, probs)
+        log_cdf = self.b.log_cdf(value, probs)
+        sf = self.b.survival_function(value, probs)
+        log_sf = self.b.log_survival(value, probs)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_bernoulli_prob1():
@@ -103,8 +103,8 @@ class BernoulliKl(nn.Cell):
         self.b2 = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
-        kl1 = self.b1('kl_loss', 'Bernoulli', probs_b)
-        kl2 = self.b2('kl_loss', 'Bernoulli', probs_b, probs_a)
+        kl1 = self.b1.kl_loss('Bernoulli', probs_b)
+        kl2 = self.b2.kl_loss('Bernoulli', probs_b, probs_a)
         return kl1 + kl2
 
 def test_kl():
@@ -127,8 +127,8 @@ class BernoulliCrossEntropy(nn.Cell):
         self.b2 = msd.Bernoulli(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
-        h1 = self.b1('cross_entropy', 'Bernoulli', probs_b)
-        h2 = self.b2('cross_entropy', 'Bernoulli', probs_b, probs_a)
+        h1 = self.b1.cross_entropy('Bernoulli', probs_b)
+        h2 = self.b2.cross_entropy('Bernoulli', probs_b, probs_a)
         return h1 + h2
 
 def test_cross_entropy():
@@ -150,11 +150,11 @@ class BernoulliBasics(nn.Cell):
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
 
     def construct(self):
-        mean = self.b('mean')
-        sd = self.b('sd')
-        var = self.b('var')
-        mode = self.b('mode')
-        entropy = self.b('entropy')
+        mean = self.b.mean()
+        sd = self.b.sd()
+        var = self.b.var()
+        mode = self.b.mode()
+        entropy = self.b.entropy()
         return mean + sd + var + mode + entropy
 
 def test_bascis():
@@ -164,3 +164,28 @@ def test_bascis():
     net = BernoulliBasics()
     ans = net()
     assert isinstance(ans, Tensor)
+
+class BernoulliConstruct(nn.Cell):
+    """
+    Bernoulli distribution: going through construct.
+    """
+    def __init__(self):
+        super(BernoulliConstruct, self).__init__()
+        self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
+        self.b1 = msd.Bernoulli(dtype=dtype.int32)
+
+    def construct(self, value, probs):
+        prob = self.b('prob', value)
+        prob1 = self.b('prob', value, probs)
+        prob2 = self.b1('prob', value, probs)
+        return prob + prob1 + prob2
+
+def test_bernoulli_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = BernoulliConstruct()
+    value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
+    probs = Tensor([0.5], dtype=dtype.float32)
+    ans = net(value, probs)
+    assert isinstance(ans, Tensor)

tests/ut/python/nn/distribution/test_exponential.py

@@ -50,12 +50,12 @@ class ExponentialProb(nn.Cell):
         self.e = msd.Exponential(0.5, dtype=dtype.float32)
 
     def construct(self, value):
-        prob = self.e('prob', value)
-        log_prob = self.e('log_prob', value)
-        cdf = self.e('cdf', value)
-        log_cdf = self.e('log_cdf', value)
-        sf = self.e('survival_function', value)
-        log_sf = self.e('log_survival', value)
+        prob = self.e.prob(value)
+        log_prob = self.e.log_prob(value)
+        cdf = self.e.cdf(value)
+        log_cdf = self.e.log_cdf(value)
+        sf = self.e.survival_function(value)
+        log_sf = self.e.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_exponential_prob():
@@ -76,12 +76,12 @@ class ExponentialProb1(nn.Cell):
         self.e = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, value, rate):
-        prob = self.e('prob', value, rate)
-        log_prob = self.e('log_prob', value, rate)
-        cdf = self.e('cdf', value, rate)
-        log_cdf = self.e('log_cdf', value, rate)
-        sf = self.e('survival_function', value, rate)
-        log_sf = self.e('log_survival', value, rate)
+        prob = self.e.prob(value, rate)
+        log_prob = self.e.log_prob(value, rate)
+        cdf = self.e.cdf(value, rate)
+        log_cdf = self.e.log_cdf(value, rate)
+        sf = self.e.survival_function(value, rate)
+        log_sf = self.e.log_survival(value, rate)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_exponential_prob1():
@@ -104,8 +104,8 @@ class ExponentialKl(nn.Cell):
         self.e2 = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, rate_b, rate_a):
-        kl1 = self.e1('kl_loss', 'Exponential', rate_b)
-        kl2 = self.e2('kl_loss', 'Exponential', rate_b, rate_a)
+        kl1 = self.e1.kl_loss('Exponential', rate_b)
+        kl2 = self.e2.kl_loss('Exponential', rate_b, rate_a)
         return kl1 + kl2
 
 def test_kl():
@@ -128,8 +128,8 @@ class ExponentialCrossEntropy(nn.Cell):
         self.e2 = msd.Exponential(dtype=dtype.float32)
 
     def construct(self, rate_b, rate_a):
-        h1 = self.e1('cross_entropy', 'Exponential', rate_b)
-        h2 = self.e2('cross_entropy', 'Exponential', rate_b, rate_a)
+        h1 = self.e1.cross_entropy('Exponential', rate_b)
+        h2 = self.e2.cross_entropy('Exponential', rate_b, rate_a)
         return h1 + h2
 
 def test_cross_entropy():
@@ -151,11 +151,11 @@ class ExponentialBasics(nn.Cell):
         self.e = msd.Exponential([0.3, 0.5], dtype=dtype.float32)
 
     def construct(self):
-        mean = self.e('mean')
-        sd = self.e('sd')
-        var = self.e('var')
-        mode = self.e('mode')
-        entropy = self.e('entropy')
+        mean = self.e.mean()
+        sd = self.e.sd()
+        var = self.e.var()
+        mode = self.e.mode()
+        entropy = self.e.entropy()
         return mean + sd + var + mode + entropy
 
 def test_bascis():
@@ -165,3 +165,29 @@ def test_bascis():
     net = ExponentialBasics()
     ans = net()
     assert isinstance(ans, Tensor)
+
+
+class ExpConstruct(nn.Cell):
+    """
+    Exponential distribution: going through construct.
+    """
+    def __init__(self):
+        super(ExpConstruct, self).__init__()
+        self.e = msd.Exponential(0.5, dtype=dtype.float32)
+        self.e1 = msd.Exponential(dtype=dtype.float32)
+
+    def construct(self, value, rate):
+        prob = self.e('prob', value)
+        prob1 = self.e('prob', value, rate)
+        prob2 = self.e1('prob', value, rate)
+        return prob + prob1 + prob2
+
+def test_exp_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = ExpConstruct()
+    value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
+    probs = Tensor([0.5], dtype=dtype.float32)
+    ans = net(value, probs)
+    assert isinstance(ans, Tensor)

tests/ut/python/nn/distribution/test_geometric.py

@@ -50,12 +50,12 @@ class GeometricProb(nn.Cell):
         self.g = msd.Geometric(0.5, dtype=dtype.int32)
 
     def construct(self, value):
-        prob = self.g('prob', value)
-        log_prob = self.g('log_prob', value)
-        cdf = self.g('cdf', value)
-        log_cdf = self.g('log_cdf', value)
-        sf = self.g('survival_function', value)
-        log_sf = self.g('log_survival', value)
+        prob = self.g.prob(value)
+        log_prob = self.g.log_prob(value)
+        cdf = self.g.cdf(value)
+        log_cdf = self.g.log_cdf(value)
+        sf = self.g.survival_function(value)
+        log_sf = self.g.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_geometric_prob():
@@ -76,12 +76,12 @@ class GeometricProb1(nn.Cell):
         self.g = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, value, probs):
-        prob = self.g('prob', value, probs)
-        log_prob = self.g('log_prob', value, probs)
-        cdf = self.g('cdf', value, probs)
-        log_cdf = self.g('log_cdf', value, probs)
-        sf = self.g('survival_function', value, probs)
-        log_sf = self.g('log_survival', value, probs)
+        prob = self.g.prob(value, probs)
+        log_prob = self.g.log_prob(value, probs)
+        cdf = self.g.cdf(value, probs)
+        log_cdf = self.g.log_cdf(value, probs)
+        sf = self.g.survival_function(value, probs)
+        log_sf = self.g.log_survival(value, probs)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_geometric_prob1():
@@ -105,8 +105,8 @@ class GeometricKl(nn.Cell):
         self.g2 = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
-        kl1 = self.g1('kl_loss', 'Geometric', probs_b)
-        kl2 = self.g2('kl_loss', 'Geometric', probs_b, probs_a)
+        kl1 = self.g1.kl_loss('Geometric', probs_b)
+        kl2 = self.g2.kl_loss('Geometric', probs_b, probs_a)
         return kl1 + kl2
 
 def test_kl():
@@ -129,8 +129,8 @@ class GeometricCrossEntropy(nn.Cell):
         self.g2 = msd.Geometric(dtype=dtype.int32)
 
     def construct(self, probs_b, probs_a):
-        h1 = self.g1('cross_entropy', 'Geometric', probs_b)
-        h2 = self.g2('cross_entropy', 'Geometric', probs_b, probs_a)
+        h1 = self.g1.cross_entropy('Geometric', probs_b)
+        h2 = self.g2.cross_entropy('Geometric', probs_b, probs_a)
         return h1 + h2
 
 def test_cross_entropy():
@@ -152,11 +152,11 @@ class GeometricBasics(nn.Cell):
         self.g = msd.Geometric([0.3, 0.5], dtype=dtype.int32)
 
     def construct(self):
-        mean = self.g('mean')
-        sd = self.g('sd')
-        var = self.g('var')
-        mode = self.g('mode')
-        entropy = self.g('entropy')
+        mean = self.g.mean()
+        sd = self.g.sd()
+        var = self.g.var()
+        mode = self.g.mode()
+        entropy = self.g.entropy()
         return mean + sd + var + mode + entropy
 
 def test_bascis():
@@ -166,3 +166,29 @@ def test_bascis():
     net = GeometricBasics()
     ans = net()
     assert isinstance(ans, Tensor)
+
+
+class GeoConstruct(nn.Cell):
+    """
+    Bernoulli distribution: going through construct.
+    """
+    def __init__(self):
+        super(GeoConstruct, self).__init__()
+        self.g = msd.Geometric(0.5, dtype=dtype.int32)
+        self.g1 = msd.Geometric(dtype=dtype.int32)
+
+    def construct(self, value, probs):
+        prob = self.g('prob', value)
+        prob1 = self.g('prob', value, probs)
+        prob2 = self.g1('prob', value, probs)
+        return prob + prob1 + prob2
+
+def test_geo_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = GeoConstruct()
+    value = Tensor([0, 0, 0, 0, 0], dtype=dtype.float32)
+    probs = Tensor([0.5], dtype=dtype.float32)
+    ans = net(value, probs)
+    assert isinstance(ans, Tensor)

tests/ut/python/nn/distribution/test_normal.py

@@ -50,12 +50,12 @@ class NormalProb(nn.Cell):
         self.normal = msd.Normal(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self, value):
-        prob = self.normal('prob', value)
-        log_prob = self.normal('log_prob', value)
-        cdf = self.normal('cdf', value)
-        log_cdf = self.normal('log_cdf', value)
-        sf = self.normal('survival_function', value)
-        log_sf = self.normal('log_survival', value)
+        prob = self.normal.prob(value)
+        log_prob = self.normal.log_prob(value)
+        cdf = self.normal.cdf(value)
+        log_cdf = self.normal.log_cdf(value)
+        sf = self.normal.survival_function(value)
+        log_sf = self.normal.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_normal_prob():
@@ -77,12 +77,12 @@ class NormalProb1(nn.Cell):
         self.normal = msd.Normal()
 
     def construct(self, value, mean, sd):
-        prob = self.normal('prob', value, mean, sd)
-        log_prob = self.normal('log_prob', value, mean, sd)
-        cdf = self.normal('cdf', value, mean, sd)
-        log_cdf = self.normal('log_cdf', value, mean, sd)
-        sf = self.normal('survival_function', value, mean, sd)
-        log_sf = self.normal('log_survival', value, mean, sd)
+        prob = self.normal.prob(value, mean, sd)
+        log_prob = self.normal.log_prob(value, mean, sd)
+        cdf = self.normal.cdf(value, mean, sd)
+        log_cdf = self.normal.log_cdf(value, mean, sd)
+        sf = self.normal.survival_function(value, mean, sd)
+        log_sf = self.normal.log_survival(value, mean, sd)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_normal_prob1():
@@ -106,8 +106,8 @@ class NormalKl(nn.Cell):
         self.n2 = msd.Normal(dtype=dtype.float32)
 
     def construct(self, mean_b, sd_b, mean_a, sd_a):
-        kl1 = self.n1('kl_loss', 'Normal', mean_b, sd_b)
-        kl2 = self.n2('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
+        kl1 = self.n1.kl_loss('Normal', mean_b, sd_b)
+        kl2 = self.n2.kl_loss('Normal', mean_b, sd_b, mean_a, sd_a)
         return kl1 + kl2
 
 def test_kl():
@@ -132,8 +132,8 @@ class NormalCrossEntropy(nn.Cell):
         self.n2 = msd.Normal(dtype=dtype.float32)
 
     def construct(self, mean_b, sd_b, mean_a, sd_a):
-        h1 = self.n1('cross_entropy', 'Normal', mean_b, sd_b)
-        h2 = self.n2('cross_entropy', 'Normal', mean_b, sd_b, mean_a, sd_a)
+        h1 = self.n1.cross_entropy('Normal', mean_b, sd_b)
+        h2 = self.n2.cross_entropy('Normal', mean_b, sd_b, mean_a, sd_a)
         return h1 + h2
 
 def test_cross_entropy():
@@ -157,10 +157,10 @@ class NormalBasics(nn.Cell):
         self.n = msd.Normal(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self):
-        mean = self.n('mean')
-        sd = self.n('sd')
-        mode = self.n('mode')
-        entropy = self.n('entropy')
+        mean = self.n.mean()
+        sd = self.n.sd()
+        mode = self.n.mode()
+        entropy = self.n.entropy()
         return mean + sd + mode + entropy
 
 def test_bascis():
@@ -170,3 +170,30 @@ def test_bascis():
     net = NormalBasics()
     ans = net()
     assert isinstance(ans, Tensor)
+
+
+class NormalConstruct(nn.Cell):
+    """
+    Normal distribution: going through construct.
+    """
+    def __init__(self):
+        super(NormalConstruct, self).__init__()
+        self.normal = msd.Normal(3.0, 4.0)
+        self.normal1 = msd.Normal()
+
+    def construct(self, value, mean, sd):
+        prob = self.normal('prob', value)
+        prob1 = self.normal('prob', value, mean, sd)
+        prob2 = self.normal1('prob', value, mean, sd)
+        return prob + prob1 + prob2
+
+def test_normal_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = NormalConstruct()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    mean = Tensor([0.0], dtype=dtype.float32)
+    sd = Tensor([1.0], dtype=dtype.float32)
+    ans = net(value, mean, sd)
+    assert isinstance(ans, Tensor)

tests/ut/python/nn/distribution/test_uniform.py

@@ -60,12 +60,12 @@ class UniformProb(nn.Cell):
         self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self, value):
-        prob = self.u('prob', value)
-        log_prob = self.u('log_prob', value)
-        cdf = self.u('cdf', value)
-        log_cdf = self.u('log_cdf', value)
-        sf = self.u('survival_function', value)
-        log_sf = self.u('log_survival', value)
+        prob = self.u.prob(value)
+        log_prob = self.u.log_prob(value)
+        cdf = self.u.cdf(value)
+        log_cdf = self.u.log_cdf(value)
+        sf = self.u.survival_function(value)
+        log_sf = self.u.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_uniform_prob():
@@ -86,12 +86,12 @@ class UniformProb1(nn.Cell):
         self.u = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, value, low, high):
-        prob = self.u('prob', value, low, high)
-        log_prob = self.u('log_prob', value, low, high)
-        cdf = self.u('cdf', value, low, high)
-        log_cdf = self.u('log_cdf', value, low, high)
-        sf = self.u('survival_function', value, low, high)
-        log_sf = self.u('log_survival', value, low, high)
+        prob = self.u.prob(value, low, high)
+        log_prob = self.u.log_prob(value, low, high)
+        cdf = self.u.cdf(value, low, high)
+        log_cdf = self.u.log_cdf(value, low, high)
+        sf = self.u.survival_function(value, low, high)
+        log_sf = self.u.log_survival(value, low, high)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
 def test_uniform_prob1():
@@ -115,8 +115,8 @@ class UniformKl(nn.Cell):
         self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
-        kl1 = self.u1('kl_loss', 'Uniform', low_b, high_b)
-        kl2 = self.u2('kl_loss', 'Uniform', low_b, high_b, low_a, high_a)
+        kl1 = self.u1.kl_loss('Uniform', low_b, high_b)
+        kl2 = self.u2.kl_loss('Uniform', low_b, high_b, low_a, high_a)
         return kl1 + kl2
 
 def test_kl():
@@ -141,8 +141,8 @@ class UniformCrossEntropy(nn.Cell):
         self.u2 = msd.Uniform(dtype=dtype.float32)
 
     def construct(self, low_b, high_b, low_a, high_a):
-        h1 = self.u1('cross_entropy', 'Uniform', low_b, high_b)
-        h2 = self.u2('cross_entropy', 'Uniform', low_b, high_b, low_a, high_a)
+        h1 = self.u1.cross_entropy('Uniform', low_b, high_b)
+        h2 = self.u2.cross_entropy('Uniform', low_b, high_b, low_a, high_a)
         return h1 + h2
 
 def test_cross_entropy():
@@ -166,10 +166,10 @@ class UniformBasics(nn.Cell):
         self.u = msd.Uniform(3.0, 4.0, dtype=dtype.float32)
 
     def construct(self):
-        mean = self.u('mean')
-        sd = self.u('sd')
-        var = self.u('var')
-        entropy = self.u('entropy')
+        mean = self.u.mean()
+        sd = self.u.sd()
+        var = self.u.var()
+        entropy = self.u.entropy()
         return mean + sd + var + entropy
 
 def test_bascis():
@@ -179,3 +179,30 @@ def test_bascis():
     net = UniformBasics()
     ans = net()
     assert isinstance(ans, Tensor)
+
+
+class UniConstruct(nn.Cell):
+    """
+    Unifrom distribution: going through construct.
+    """
+    def __init__(self):
+        super(UniConstruct, self).__init__()
+        self.u = msd.Uniform(-4.0, 4.0)
+        self.u1 = msd.Uniform()
+
+    def construct(self, value, low, high):
+        prob = self.u('prob', value)
+        prob1 = self.u('prob', value, low, high)
+        prob2 = self.u1('prob', value, low, high)
+        return prob + prob1 + prob2
+
+def test_uniform_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = UniConstruct()
+    value = Tensor([-5.0, 0.0, 1.0, 5.0], dtype=dtype.float32)
+    low = Tensor([-1.0], dtype=dtype.float32)
+    high = Tensor([1.0], dtype=dtype.float32)
+    ans = net(value, low, high)
+    assert isinstance(ans, Tensor)