add sample functions in normal and bermoulli distributions

mindspore/nn/distribution/_utils/utils.py

@@ -15,9 +15,9 @@
 # ============================================================================
 """Utitly functions to help distribution class."""
 import numpy as np
-from mindspore.ops import operations as P
 from mindspore.ops import _utils as utils
-from ....common.tensor import Tensor
+from ....common.tensor import Tensor, MetaTensor
+from ....common.parameter import Parameter
 from ....common import dtype as mstype
 
 
@@ -33,15 +33,17 @@ def cast_to_tensor(t, dtype=mstype.float32):
     Cast an user input value into a Tensor of dtype.
 
     Args:
-        t (int/float/list/numpy.ndarray/Tensor).
-        dtype (mindspore.dtype).
+        t (int, float, list, numpy.ndarray, Tensor, Parameter): object to be cast to Tensor.
+        dtype (mindspore.dtype): dtype of the Tensor. Default: mstype.float32.
 
     Raises:
         RuntimeError: if t cannot be cast to Tensor.
 
-    Outputs:
+    Returns:
         Tensor.
     """
+    if isinstance(t, Parameter):
+        return t
     if isinstance(t, Tensor):
         #check if the Tensor in shape of Tensor(4)
         if t.dim() == 0:
@@ -61,9 +63,9 @@ def calc_batch_size(batch_shape):
     Calculate the size of a given batch_shape.
 
     Args:
-        batch_shape (tuple)
+        batch_shape (tuple): batch shape to be calculated.
 
-    Outputs:
+    Returns:
         int.
     """
     return int(np.prod(batch_shape))
@@ -73,23 +75,26 @@ def convert_to_batch(t, batch_shape, dtype):
     Convert a Tensor to a given batch shape.
 
     Args:
-        t (Tensor)
-        batch_shape (tuple)
-        dtype (mindspore.dtype)
+        t (Tensor, Parameter): Tensor to be converted.
+        batch_shape (tuple): desired batch shape.
+        dtype (mindspore.dtype): desired dtype.
+
     Raises:
         RuntimeError: if the converison cannot be done.
 
-    Outputs:
+    Returns:
         Tensor, with shape of batch_shape.
     """
+    if isinstance(t, Parameter):
+        return t
     t = cast_to_tensor(t, dtype)
-    reshape = P.Reshape()
     if t.shape != batch_shape:
         mul = calc_batch_size(batch_shape) // t.size()
         if (calc_batch_size(batch_shape) % t.size()) != 0:
             raise RuntimeError("Cannot cast the tensor to the given batch shape.")
         temp = list(t.asnumpy()) * mul
-        return reshape(Tensor(temp), batch_shape)
+        temp = np.reshape(temp, batch_shape)
+        return Tensor(temp, dtype)
     return t
 
 def check_scalar_from_param(params):
@@ -97,7 +102,7 @@ def check_scalar_from_param(params):
     Check if params are all scalars.
 
     Args:
-        params (dict): parameters used to initialized distribution.
+        params (dict): parameters used to initialize distribution.
 
     Notes: String parameters are excluded.
     """
@@ -116,9 +121,9 @@ def calc_broadcast_shape_from_param(params):
     Calculate the broadcast shape from params.
 
     Args:
-        params (dict): parameters used to initialized distribution.
+        params (dict): parameters used to initialize distribution.
 
-    Outputs:
+    Returns:
         tuple.
     """
     broadcast_shape = []
@@ -127,7 +132,10 @@ def calc_broadcast_shape_from_param(params):
             continue
         if value is None:
             return None
-        value_t = cast_to_tensor(value, params['dtype'])
+        if isinstance(value, Parameter):
+            value_t = value.default_input
+        else:
+            value_t = cast_to_tensor(value, params['dtype'])
         broadcast_shape = utils.get_broadcast_shape(broadcast_shape, list(value_t.shape), params['name'])
     return tuple(broadcast_shape)
 
@@ -136,36 +144,37 @@ def check_greater_equal_zero(value, name):
     Check if the given Tensor is greater zero.
 
     Args:
-        value (Tensor)
+        value (Tensor, Parameter): value to be checked.
         name (str) : name of the value.
 
     Raises:
         ValueError: if the input value is less than zero.
 
     """
-    less = P.Less()
-    zeros = Tensor([0.0], dtype=value.dtype)
-    value = less(value, zeros)
-    if value.asnumpy().any():
-        raise ValueError('{} should be greater than zero.'.format(name))
+    if isinstance(value, Parameter):
+        if isinstance(value.default_input, MetaTensor):
+            return
+        value = value.default_input
+    comp = np.less(value.asnumpy(), np.zeros(value.shape))
+    if comp.any():
+        raise ValueError(f'{name} should be greater than zero.')
 
 def check_greater(a, b, name_a, name_b):
     """
     Check if Tensor b is strictly greater than Tensor a.
 
     Args:
-        a (Tensor)
-        b (Tensor)
+        a (Tensor): input tensor a.
+        b (Tensor): input tensor b.
         name_a (str): name of Tensor_a.
         name_b (str): name of Tensor_b.
 
     Raises:
         ValueError: if b is less than or equal to a
     """
-    less = P.Less()
-    value = less(a, b)
-    if not value.asnumpy().all():
-        raise ValueError('{} should be less than {}'.format(name_a, name_b))
+    comp = np.less(a.asnumpy(), b.asnumpy())
+    if not comp.all():
+        raise ValueError(f'{name_a} should be less than {name_b}')
 
 
 def check_prob(p):
@@ -173,18 +182,18 @@ def check_prob(p):
     Check if p is a proper probability, i.e. 0 <= p <=1.
 
     Args:
-        p (Tensor): value to check.
+        p (Tensor, Parameter): value to be checked.
 
     Raises:
         ValueError: if p is not a proper probability.
     """
-    less = P.Less()
-    greater = P.Greater()
-    zeros = Tensor([0.0], dtype=p.dtype)
-    ones = Tensor([1.0], dtype=p.dtype)
-    comp = less(p, zeros)
-    if comp.asnumpy().any():
+    if isinstance(p, Parameter):
+        if isinstance(p.default_input, MetaTensor):
+            return
+        p = p.default_input
+    comp = np.less(p.asnumpy(), np.zeros(p.shape))
+    if comp.any():
         raise ValueError('Probabilities should be greater than or equal to zero')
-    comp = greater(p, ones)
-    if comp.asnumpy().any():
+    comp = np.greater(p.asnumpy(), np.ones(p.shape))
+    if comp.any():
         raise ValueError('Probabilities should be less than or equal to one')

mindspore/nn/distribution/bernoulli.py

@@ -23,21 +23,24 @@ class Bernoulli(Distribution):
     Example class: Bernoulli Distribution.
 
     Args:
-        probs (int/float/list/numpy.ndarray/Tensor): probability of 1 as outcome.
-        dtype (mindspore.dtype): type of the distribution, default to int32.
+        probs (int, float, list, numpy.ndarray, Tensor, Parameter): probability of 1 as outcome.
+        seed (int): seed to use in sampling. Default: 0.
+        dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
+        name (str): name of the distribution. Default: Bernoulli.
 
     Note:
         probs should be proper probabilities (0 <= p <= 1).
 
     Examples:
         >>>    # To initialize a Bernoulli distribution which has equal probability of getting 1 and 0
-        >>>    b = nn.Bernoulli(0.5, dtype = dtype.int32)
+        >>>    b = nn.Bernoulli(0.5, dtype = mstype.int32)
         >>>    # The following create two independent Bernoulli distributions
-        >>>    b = nn.Bernoulli([0.7, 0.2], dtype = dtype.int32)
+        >>>    b = nn.Bernoulli([0.7, 0.2], dtype = mstype.int32)
     """
 
     def __init__(self,
                  probs=None,
+                 seed=0,
                  dtype=mstype.int32,
                  name="Bernoulli"):
         """
@@ -47,7 +50,6 @@ class Bernoulli(Distribution):
         super(Bernoulli, self).__init__(dtype, name, param)
         if probs is not None:
             self._probs = cast_to_tensor(probs)
-            # check if the input probability is valid
             check_prob(self._probs)
         else:
             self._probs = probs
@@ -58,7 +60,17 @@ class Bernoulli(Distribution):
         self.mul = P.Mul()
         self.sqrt = P.Sqrt()
         self.realdiv = P.RealDiv()
+        self.shape = P.Shape()
+        self.const = P.ScalarToArray()
+        self.less = P.Less()
+        self.cast = P.Cast()
+        self.normal = P.Normal(seed=seed)
+        self.erf = P.Erf()
+        self.sqrt = P.Sqrt()
 
+    def extend_repr(self):
+        str_info = f'probs = {self._probs}'
+        return str_info
 
     def probs(self):
         """
@@ -66,21 +78,25 @@ class Bernoulli(Distribution):
         """
         return self._probs
 
-    def _mean(self):
+    def _mean(self, name='mean', probs1=None):
         r"""
         .. math::
             MEAN(B) = probs1
         """
+        if name == 'mean':
+            return self._probs if probs1 is None else probs1
+        return None
 
-        return self._probs
-
-    def _var(self):
+    def _var(self, name='var', probs1=None):
         r"""
         .. math::
             VAR(B) = probs1 * probs0
         """
-        probs0 = self.add(1, -1 * self._probs)
-        return self.mul(probs0, self._probs)
+        if name in ('sd', 'var'):
+            probs1 = self._probs if probs1 is None else probs1
+            probs0 = self.add(1, -1 * probs1)
+            return self.mul(probs0, probs1)
+        return None
 
     def _prob(self, name, value, probs=None):
         r"""
@@ -89,18 +105,20 @@ class 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 to self._probs.
+            probs (Tensor): probability of outcome is 1. Default: self._probs.
 
         .. math::
             pmf(k) = probs1 if k = 1;
             pmf(k) = probs0 if k = 0;
         """
-        probs1 = self._probs if probs is None else probs
-        probs0 = self.add(1, -1 * probs1)
-        return self.add(self.mul(probs1, value),
-                        self.mul(probs0, self.add(1, -1 * value)))
+        if name in ('prob', 'log_prob'):
+            probs1 = self._probs if probs is None else probs
+            probs0 = self.add(1, -1 * probs1)
+            return self.add(self.mul(probs1, value),
+                            self.mul(probs0, self.add(1, -1 * value)))
+        return None
 
-    def _kl_loss(self, name, dist, probs1_b):
+    def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
         r"""
         Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
 
@@ -108,19 +126,42 @@ class Bernoulli(Distribution):
             name (str): name of the funtion. Should always be "kl_loss" when passed in from construct.
             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.
 
         .. math::
             KL(a||b) = probs1_a * \log(\fract{probs1_a}{probs1_b}) +
                        probs0_a * \log(\fract{probs0_a}{probs0_b})
         """
-        if dist == 'Bernoulli':
-            probs1_a = self._probs
+        if name == 'kl_loss' and dist == 'Bernoulli':
+            probs1_a = self._probs if probs1_a is None else probs1_a
             probs0_a = self.add(1, -1 * probs1_a)
             probs0_b = self.add(1, -1 * probs1_b)
             return self.add(probs1_a * self.log(self.realdiv(probs1_a, probs1_b)),
                             probs0_a * self.log(self.realdiv(probs0_a, probs0_b)))
         return None
 
-    def extend_repr(self):
-        str_info = 'probs={}'.format(self._probs)
-        return str_info
+    def _sample(self, name, 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
+            batch_shape = self.shape(probs1)
+            sample_shape = shape + batch_shape
+            mean_zero = self.const(0.0)
+            sd_one = self.const(1.0)
+            sqrt_two = self.sqrt(self.const(2.0))
+            sample_norm = self.normal(sample_shape, mean_zero, sd_one)
+            sample_uniform = 0.5 * (1 + self.erf(self.realdiv(sample_norm, sqrt_two)))
+            sample = self.less(sample_uniform, probs1)
+            sample = self.cast(sample, self._dtype)
+            return sample
+        return None

mindspore/nn/distribution/distribution.py

@@ -21,6 +21,11 @@ class Distribution(Cell):
     """
     Base class for all mathematical distributions.
 
+    Args:
+        dtype (mindspore.dtype): type of the distribution.
+        name (str): name of the distribution.
+        param (dict): parameters used to initialize the distribution.
+
     Note:
         Derived class should override operations such as ,_mean, _prob,
         and _log_prob. Functions should be called through construct when
@@ -97,14 +102,8 @@ class Distribution(Cell):
         Note:
             value is casted to Tensor for further calculation.
 
-        Args:
-            name (str): name of the calling function.
-            value (Tensor): values to be evaluated.
-            mean (Tensor): mean of the distirbution. Default: self.mean.
-            sd (Tensor): standard deviation of the distribution. Default: self.sd.
-
-        Outputs:
-            Tensor, shape: broadcast_shape of the distribution.
+        Returns:
+            Tensor, shape is the broadcast_shape of the distribution.
         """
         return self._call_log_prob(*args)
 
@@ -114,36 +113,9 @@ class Distribution(Cell):
 
         .. math::
             probability(x) = \exp(log_likehood(x))
-
-        Args:
-            name (str): name of the calling function.
-            value (Tensor): values to be evaluated.
-            mean (Tensor): mean of the distribution. Default: self.mean.
-            sd (Tensor): standard deviation of the distritbuion. Default: self.sd.
         """
         return self.exp(self._log_likelihood(*args))
 
-    def _call_prob(self, *args):
-        """
-        Raises:
-            NotImplementedError when derived class didn't override _prob or _log_likelihood.
-        """
-        raise NotImplementedError('pdf/pmf is not implemented: {}'.format(type(self).__name__))
-
-    def _call_log_prob(self, *args):
-        """
-        Raises:
-            NotImplementedError when derived class didn't override _prob or _log_likelihood.
-        """
-        raise NotImplementedError('log_probability is not implemented: {}'.format(type(self).__name__))
-
-    def _call_sd(self):
-        """
-        Raises:
-            NotImplementedError when derived class didn't override _sd or _var.
-        """
-        raise NotImplementedError('standard deviation is not implemented: {}'.format(type(self).__name__))
-
     def prob(self, *args):
         """
         Evaluate the prob (pdf or pmf) at given value.
@@ -151,14 +123,8 @@ class Distribution(Cell):
         Note:
             value is casted to Tensor for further calculation.
 
-        Args:
-            name (str): name of the calling function.
-            value (Tensor): values to be evaluated.
-            mean (Tensor): mean of the distribution.
-            sd (Tensor): standard deviation of the distritbuion.
-
-        Outputs:
-            Tensor, shape: broadcast_shape of the distribution.
+        Returns:
+            Tensor, shape is the broadcast_shape of the distribution.
         """
         return self._call_prob(*args)
 
@@ -176,8 +142,8 @@ class Distribution(Cell):
         Evaluate the KL divergence. Parameters of the second distribution should be
         passed in through **kwargs.
 
-        Outputs:
-            Tensor, shape: broadcast_shape of the distribution and input distribution.
+        Returns:
+            Tensor, shape is the broadcast_shape of the distribution and input distribution.
         """
         return self._kl_loss(**kwargs)
 
@@ -185,8 +151,8 @@ class Distribution(Cell):
         """
         Evaluate the mean.
 
-        Outputs:
-            Tensor, shape: broadcast_shape of the distribution.
+        Returns:
+            Tensor, shape is the broadcast_shape of the distribution.
         """
         return self._mean(**kwargs)
 
@@ -194,19 +160,19 @@ class Distribution(Cell):
         """
         Evaluate the standard deviation.
 
-        Outputs:
-            Tensor, with shape of broadcast_shape of the distribution.
+        Returns:
+            Tensor, shape is the broadcast_shape of the distribution.
         """
         return self._call_sd(**kwargs)
 
-    def _calc_sd_from_var(self, **kwargs):
+    def _calc_sd_from_var(self, *args):
         r"""
         Evaluate log probability from probability.
 
         .. math::
             STD(x) = \sqrt(VAR(x))
         """
-        return self.sqrt(self._var(**kwargs))
+        return self.sqrt(self._var(*args))
 
     def construct(self, *inputs):
         """
@@ -226,7 +192,9 @@ class Distribution(Cell):
         if inputs[0] == 'kl_loss':
             return self._kl_loss(*inputs)
         if inputs[0] == 'mean':
-            return self._mean()
+            return self._mean(*inputs)
         if inputs[0] == 'sd':
-            return self._call_sd()
+            return self._call_sd(*inputs)
+        if inputs[0] == 'sample':
+            return self._sample(*inputs)
         return None

mindspore/nn/distribution/normal.py

@@ -25,23 +25,27 @@ class Normal(Distribution):
     Example class: Normal distribution.
 
     Args:
-        mean (int/float/list/numpy.ndarray/Tensor): mean of the Gaussian distribution
-        standard deviation (int/float/list/numpy.ndarray/Tensor): vairance of the Gaussian distribution
-        dtype (mindspore.dtype): type of the distribution
+        mean (int, float, list, numpy.ndarray, Tensor, Parameter): mean of the Gaussian distribution.
+        sd (int, float, list, numpy.ndarray, Tensor, Parameter): stddev of the Gaussian distribution.
+        seed (int): seed to use in sampling. Default: 0.
+        dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
+        name (str): name of the distribution. Default: Normal.
+
 
     Note:
         Standard deviation should be greater than zero.
 
     Examples:
         >>>    # To initialize a normal distribution of mean 3.0 and standard deviation 4.0
-        >>>    n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
+        >>>    n = nn.Normal(3.0, 4.0, dtype=mstype.float32)
         >>>    # The following create two independent normal distributions
-        >>>    n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=dtype.float32)
+        >>>    n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
     """
 
     def __init__(self,
                  mean=None,
                  sd=None,
+                 seed=0,
                  dtype=mstype.float32,
                  name="Normal"):
         """
@@ -52,7 +56,6 @@ class Normal(Distribution):
         if  mean is not None and sd is not None:
             self._mean_value = convert_to_batch(mean, self._broadcast_shape, dtype)
             self._sd_value = convert_to_batch(sd, self._broadcast_shape, dtype)
-            #check validity of standard deviation
             check_greater_equal_zero(self._sd_value, "Standard deviation")
         else:
             self._mean_value = mean
@@ -61,11 +64,20 @@ class Normal(Distribution):
         #ops needed for the class
         self.exp = P.Exp()
         self.add = P.TensorAdd()
+        self.mul = P.Mul()
         self.sq = P.Square()
         self.log = P.Log()
         self.sqrt = P.Sqrt()
         self.realdiv = P.RealDiv()
         self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
+        self.normal = P.Normal(seed=seed)
+        self.shape = P.Shape()
+        self.zeroslike = P.ZerosLike()
+        self.const = P.ScalarToArray()
+
+    def extend_repr(self):
+        str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
+        return str_info
 
     def _expm1_by_step(self, x):
         """
@@ -73,17 +85,23 @@ class Normal(Distribution):
         """
         return self.add(self.exp(x), -1)
 
-    def _mean(self):
+    def _mean(self, name='mean', mean=None, sd=None):
         """
         Mean of the distribution.
         """
-        return self._mean_value
+        if name == 'mean':
+            mean = self._mean_value if mean is None or sd is None else mean
+            return mean
+        return None
 
-    def _sd(self):
+    def _sd(self, name='sd', mean=None, sd=None):
         """
         Standard deviation of the distribution.
         """
-        return self._sd_value
+        if name in ('sd', 'var'):
+            sd = self._sd_value if mean is None or sd is None else sd
+            return sd
+        return None
 
     def _log_likelihood(self, name, value, mean=None, sd=None):
         r"""
@@ -92,33 +110,60 @@ class Normal(Distribution):
         .. math::
             L(x) = -1* \fract{(x - \mu)^2}{2. * \sigma^2} - \log(\sqrt(2* \pi * \sigma^2))
         """
-        mean = self._mean_value if mean is None else mean
-        sd = self._sd_value if sd is None else sd
-        unnormalized_log_prob = -1. * self.realdiv(self.sq(self.add(value, -1. * mean)),
-                                                   2. * self.sq(sd))
-        neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
-        return self.add(unnormalized_log_prob, neg_normalization)
-
-    def _kl_loss(self, name, dist, mean, sd):
+        if name in ('prob', 'log_prob'):
+            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.realdiv(self.sq(self.add(value, -1. * mean)),
+                                                       2. * self.sq(sd))
+            neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
+            return self.add(unnormalized_log_prob, neg_normalization)
+        return None
+
+    def _kl_loss(self, name, 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. Should always be "kl_loss".
             dist (str): type of the distributions. Should be "Normal" in this case.
-            mean (Tensor): mean of distribution b.
-            sd (Tensor): standard deviation distribution b.
+            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.
 
         .. math::
             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 dist == 'Normal':
-            diff_log_scale = self.add(self.log(self._sd_value), - self.log(sd))
-            squared_diff = self.sq(self.add(self.realdiv(self._mean_value, sd), - self.realdiv(mean, sd)))
+        if name == 'kl_loss' and 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.add(self.log(sd_a), - self.log(sd_b))
+            squared_diff = self.sq(self.add(self.realdiv(mean_a, sd_b), - self.realdiv(mean_b, sd_b)))
             return self.add(self.add(0.5 * squared_diff, 0.5 * self.expm1(2 * diff_log_scale)), - diff_log_scale)
         return None
 
-    def extend_repr(self):
-        str_info = 'mean={}, standard deviation={}'.format(self._mean_value, self._sd_value)
-        return str_info
+    def _sample(self, name, 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.
+
+        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.add(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 = self.normal(sample_shape, mean_zero, sd_one)
+            sample = self.add(mean, self.mul(sample_norm, sd))
+            return sample
+        return None

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

@@ -65,12 +65,25 @@ class Net3(nn.Cell):
     """
     def __init__(self):
         super(Net3, self).__init__()
-        self.b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
+        self.b = nn.Bernoulli([0.5, 0.5], dtype=dtype.int32)
 
     @ms_function
     def construct(self):
         return self.b('mean'), self.b('sd')
 
+class Net4(nn.Cell):
+    """
+    Test class: log probability of bernoulli distribution.
+    """
+    def __init__(self, shape, seed=0):
+        super(Net4, self).__init__()
+        self.b = nn.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)
+
 def test_pmf():
     """
     Test pmf.
@@ -80,10 +93,8 @@ def test_pmf():
     pdf = Net()
     x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
     output = pdf(x_)
-    print("expected_pmf: ", expect_pmf)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_pmf < tol).all()
+    assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()
 
 def test_log_likelihood():
     """
@@ -94,10 +105,8 @@ def test_log_likelihood():
     logprob = Net1()
     x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
     output = logprob(x_)
-    print("expected_log_probability: ", expect_logpmf)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_logpmf < tol).all()
+    assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()
 
 def test_kl_loss():
     """
@@ -110,10 +119,8 @@ def test_kl_loss():
     expect_kl_loss = probs1_a * np.log(probs1_a / probs1_b) + probs0_a * np.log(probs0_a / probs0_b)
     kl_loss = Net2()
     output = kl_loss(Tensor([probs1_b], dtype=dtype.float32))
-    print("expected_kl_loss: ", expect_kl_loss)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_kl_loss < tol).all()
+    assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
 
 def test_basics():
     """
@@ -121,8 +128,20 @@ def test_basics():
     """
     basics = Net3()
     mean, sd = basics()
-    print("mean : ", mean)
-    print("sd : ", sd)
+    expect_mean = [0.5, 0.5]
+    assert (mean.asnumpy() == expect_mean).all()
+    assert (sd.asnumpy() == expect_mean).all()
     b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
     probs = b.probs()
-    print("probs is ", probs)
+    expect_probs = [0.7, 0.5]
+    tol = 1e-6
+    assert (np.abs(probs.asnumpy() - expect_probs) < tol).all()
+
+def test_sample():
+    """
+    Test sample.
+    """
+    shape = (2, 3)
+    sample = Net4(shape)
+    output = sample()
+    assert output.shape == (2, 3, 2)

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

@@ -65,12 +65,25 @@ class Net3(nn.Cell):
     """
     def __init__(self):
         super(Net3, self).__init__()
-        self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+        self.n = nn.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')
 
+class Net4(nn.Cell):
+    """
+    Test class: mean/sd of normal distribution.
+    """
+    def __init__(self, shape, seed=0):
+        super(Net4, self).__init__()
+        self.n = nn.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)
+
 def test_pdf():
     """
     Test pdf.
@@ -79,10 +92,8 @@ def test_pdf():
     expect_pdf = norm_benchmark.pdf([1.0, 2.0]).astype(np.float32)
     pdf = Net()
     output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
-    print("expected_pdf: ", expect_pdf)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_pdf < tol).all()
+    assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
 
 def test_log_likelihood():
     """
@@ -92,10 +103,8 @@ def test_log_likelihood():
     expect_logpdf = norm_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
     logprob = Net1()
     output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
-    print("expected_log_probability: ", expect_logpdf)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_logpdf < tol).all()
+    assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
 
 def test_kl_loss():
     """
@@ -115,10 +124,8 @@ def test_kl_loss():
     mean = Tensor(mean_b, dtype=dtype.float32)
     sd = Tensor(sd_b, dtype=dtype.float32)
     output = kl_loss(mean, sd)
-    print("expected_kl_loss: ", expect_kl_loss)
-    print("ans: ", output.asnumpy())
     tol = 1e-6
-    assert (output.asnumpy() - expect_kl_loss < tol).all()
+    assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
 
 def test_basics():
     """
@@ -126,5 +133,20 @@ def test_basics():
     """
     basics = Net3()
     mean, sd = basics()
-    print("mean is ", mean)
-    print("sd is ", sd)
+    expect_mean = [3.0, 3.0]
+    expect_sd = [2.0, 4.0]
+    tol = 1e-6
+    assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
+    assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
+
+def test_sample():
+    """
+    Test sample.
+    """
+    shape = (2, 3)
+    seed = 10
+    mean = Tensor([2.0], dtype=dtype.float32)
+    sd = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
+    sample = Net4(shape, seed=seed)
+    output = sample(mean, sd)
+    assert output.shape == (2, 3, 3)