edited common_dtype and check_param dtype logic

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

@@ -110,6 +110,8 @@ def check_scalar_from_param(params):
     Notes: String parameters are excluded.
     """
     for value in params.values():
+        if value is None:
+            continue
         if isinstance(value, (msp.bijector.Bijector, msp.distribution.Distribution)):
             return params['distribution'].is_scalar_batch
         if isinstance(value, Parameter):
@@ -358,23 +360,29 @@ class CheckTensor(PrimitiveWithInfer):
             return x
         raise TypeError(f"For {name}, input type should be a Tensor or Parameter.")
 
-def common_dtype(arg_a, name_a, arg_b, name_b, hint_type):
+def set_param_type(args, hint_type):
     """
-    check if arg_a and arg_b have the same dtype.
+    Find the common type among arguments.
+
+    Args:
+        args (dict): dictionary of arguments, {'name':value}.
+        hint_type (mindspore.dtype): hint type to return.
+
+    Raises:
+        TypeError: if tensors in args are not the same dtype.
     """
-    if hasattr(arg_a, 'dtype') and hasattr(arg_b, 'dtype'):
-        if isinstance(arg_a, np.ndarray):
-            a_dtype = mstype.pytype_to_dtype(arg_a.dtype)
-        else:
-            a_dtype = arg_a.dtype
-        if isinstance(arg_b, np.ndarray):
-            b_dtype = mstype.pytype_to_dtype(arg_b.dtype)
-        else:
-            b_dtype = arg_b.dtype
-        if a_dtype != b_dtype:
-            raise TypeError(f"{name_a} and {name_b} should have the same dtype.")
-        int_type = mstype.int_type + mstype.uint_type
-        if a_dtype in int_type or a_dtype == mstype.float64:
-            return mstype.float32
-        return a_dtype
-    return hint_type
+    common_dtype = None
+    for name, arg in args.items():
+        if hasattr(arg, 'dtype'):
+            if isinstance(arg, np.ndarray):
+                cur_dtype = mstype.pytype_to_dtype(arg.dtype)
+            else:
+                cur_dtype = arg.dtype
+            if common_dtype is None:
+                common_dtype = cur_dtype
+            elif cur_dtype != common_dtype:
+                raise TypeError(f"{name} should have the same dtype as other arguments.")
+    int_type = mstype.int_type + mstype.uint_type
+    if common_dtype in int_type or common_dtype == mstype.float64:
+        return mstype.float32
+    return hint_type if common_dtype is None else common_dtype

mindspore/nn/probability/distribution/bernoulli.py

@@ -17,7 +17,7 @@ from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from .distribution import Distribution
-from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, raise_none_error
+from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, set_param_type
 from ._utils.custom_ops import exp_generic, log_generic, erf_generic
 
 
@@ -119,13 +119,16 @@ class Bernoulli(Distribution):
         valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Bernoulli, self).__init__(seed, dtype, name, param)
-        self.parameter_type = mstype.float32
+        self.parameter_type = set_param_type({'probs1': probs}, mstype.float32)
         if probs is not None:
-            self._probs = cast_to_tensor(probs, mstype.float32)
+            self._probs = cast_to_tensor(probs, self.parameter_type)
             check_prob(self.probs)
         else:
             self._probs = probs
 
+        self.default_parameters = [self.probs]
+        self.parameter_names = ['probs1']
+
         # ops needed for the class
         self.exp = exp_generic
         self.log = log_generic
@@ -157,24 +160,12 @@ class Bernoulli(Distribution):
         """
         return self._probs
 
-    def _check_param(self, probs1):
-        """
-        Check availablity of distribution specific args `probs1`.
-        """
-        if probs1 is not None:
-            if self.context_mode == 0:
-                self.checktensor(probs1, 'probs1')
-            else:
-                probs1 = self.checktensor(probs1, 'probs1')
-            return self.cast(probs1, self.parameter_type)
-        return self.probs if self.probs is not None else raise_none_error('probs1')
-
     def _mean(self, probs1=None):
         r"""
         .. math::
             MEAN(B) = probs1
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         return probs1
 
     def _mode(self, probs1=None):
@@ -182,7 +173,7 @@ class Bernoulli(Distribution):
         .. math::
             MODE(B) = 1 if probs1 > 0.5 else = 0
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(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)
@@ -194,7 +185,7 @@ class Bernoulli(Distribution):
         .. math::
             VAR(B) = probs1 * probs0
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         probs0 = 1.0 - probs1
         return self.exp(self.log(probs0) + self.log(probs1))
 
@@ -203,11 +194,11 @@ class Bernoulli(Distribution):
         .. math::
             H(B) = -probs0 * \log(probs0) - probs1 * \log(probs1)
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         probs0 = 1.0 - probs1
         return -(probs0 * self.log(probs0)) - (probs1 * self.log(probs1))
 
-    def _cross_entropy(self, dist, probs1_b, probs1_a=None):
+    def _cross_entropy(self, dist, probs1_b, probs1=None):
         """
         Evaluate cross_entropy between Bernoulli distributions.
 
@@ -217,7 +208,7 @@ class Bernoulli(Distribution):
             probs1_a (Tensor): `probs1` of distribution a. Default: self.probs.
         """
         check_distribution_name(dist, 'Bernoulli')
-        return self._entropy(probs1_a) + self._kl_loss(dist, probs1_b, probs1_a)
+        return self._entropy(probs1) + self._kl_loss(dist, probs1_b, probs1)
 
     def _log_prob(self, value, probs1=None):
         r"""
@@ -233,7 +224,7 @@ class Bernoulli(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, mstype.float32)
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         probs0 = 1.0 - probs1
         return self.log(probs1) * value + self.log(probs0) * (1.0 - value)
 
@@ -253,7 +244,7 @@ class Bernoulli(Distribution):
         value = self._check_value(value, 'value')
         value = self.cast(value, mstype.float32)
         value = self.floor(value)
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         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)
@@ -264,7 +255,7 @@ class Bernoulli(Distribution):
         less_than_zero = self.select(comp_zero, zeros, probs0)
         return self.select(comp_one, less_than_zero, ones)
 
-    def _kl_loss(self, dist, probs1_b, probs1_a=None):
+    def _kl_loss(self, dist, probs1_b, probs1=None):
         r"""
         Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
 
@@ -280,7 +271,7 @@ class Bernoulli(Distribution):
         check_distribution_name(dist, 'Bernoulli')
         probs1_b = self._check_value(probs1_b, 'probs1_b')
         probs1_b = self.cast(probs1_b, self.parameter_type)
-        probs1_a = self._check_param(probs1_a)
+        probs1_a = self._check_param_type(probs1)
         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)
@@ -297,7 +288,7 @@ class Bernoulli(Distribution):
             Tensor, shape is shape + batch_shape.
         """
         shape = self.checktuple(shape, 'shape')
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         origin_shape = shape + self.shape(probs1)
         if origin_shape == ():
             sample_shape = (1,)

mindspore/nn/probability/distribution/distribution.py

@@ -18,7 +18,8 @@ from mindspore.nn.cell import Cell
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
 from mindspore.common import get_seed
-from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param, cast_type_for_device
+from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param, cast_type_for_device,\
+                          raise_none_error
 from ._utils.utils import CheckTuple, CheckTensor
 
 
@@ -115,6 +116,51 @@ class Distribution(Cell):
     def broadcast_shape(self):
         return self._broadcast_shape
 
+    def _check_param_type(self, *args):
+        """
+        Check the availability and validity of default parameters and dist_spec_args.
+        dist_spec_args passed in must be tensors. If default parameter of the distribution
+        is None, its parameter must be passed in through `args`.
+        """
+        broadcast_shape = None
+        common_dtype = None
+        out = []
+
+        for arg, name, default in zip(args, self.parameter_names, self.default_parameters):
+            # check if the argument is a Tensor
+            if arg is not None:
+                if self.context_mode == 0:
+                    self.checktensor(arg, name)
+                else:
+                    arg = self.checktensor(arg, name)
+            else:
+                arg = default if default is not None else raise_none_error(name)
+
+            # broadcast if the number of args > 1
+            if broadcast_shape is None:
+                broadcast_shape = self.shape(arg)
+                common_dtype = self.dtypeop(arg)
+            else:
+                ones = self.fill(self.dtypeop(arg), broadcast_shape, 1.0)
+                broadcast_shape = self.shape(arg + ones)
+
+                # check if the arguments have the same dtype
+                arg = arg * self.fill(self.dtypeop(arg), broadcast_shape, 1.0)
+                dtype_tensor = self.fill(common_dtype, broadcast_shape, 1.0)
+                self.sametypeshape(arg, dtype_tensor)
+            arg = self.cast(arg, self.parameter_type)
+            out.append(arg)
+
+        if len(out) == 1:
+            return out[0]
+
+        # broadcast all args to broadcast_shape
+        result = ()
+        for arg in out:
+            arg = arg * self.fill(self.dtypeop(arg), broadcast_shape, 1.0)
+            result = result + (arg,)
+        return result
+
     def _check_value(self, value, name):
         """
         Check availability of `value` as a Tensor.
@@ -211,163 +257,203 @@ class Distribution(Cell):
         if hasattr(self, '_cross_entropy'):
             self._call_cross_entropy = self._cross_entropy
 
-    def log_prob(self, *args, **kwargs):
+    def log_prob(self, value, *args, **kwargs):
         """
         Evaluate the log probability(pdf or pmf) at the given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_log_prob(*args, **kwargs)
+        return self._call_log_prob(value, *args, **kwargs)
 
-    def _calc_prob_from_log_prob(self, *args, **kwargs):
+    def _calc_prob_from_log_prob(self, value, *args, **kwargs):
         r"""
         Evaluate prob from log probability.
 
         .. math::
             probability(x) = \exp(log_likehood(x))
         """
-        return self.exp(self._log_prob(*args, **kwargs))
+        return self.exp(self._log_prob(value, *args, **kwargs))
 
-    def prob(self, *args, **kwargs):
+    def prob(self, value, *args, **kwargs):
         """
         Evaluate the probability (pdf or pmf) at given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_prob(*args, **kwargs)
+        return self._call_prob(value, *args, **kwargs)
 
-    def _calc_log_prob_from_prob(self, *args, **kwargs):
+    def _calc_log_prob_from_prob(self, value, *args, **kwargs):
         r"""
         Evaluate log probability from probability.
 
         .. math::
             log_prob(x) = \log(prob(x))
         """
-        return self.log(self._prob(*args, **kwargs))
+        return self.log(self._prob(value, *args, **kwargs))
 
-    def cdf(self, *args, **kwargs):
+    def cdf(self, value, *args, **kwargs):
         """
         Evaluate the cdf at given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_cdf(*args, **kwargs)
+        return self._call_cdf(value, *args, **kwargs)
 
-    def _calc_cdf_from_log_cdf(self, *args, **kwargs):
+    def _calc_cdf_from_log_cdf(self, value, *args, **kwargs):
         r"""
         Evaluate cdf from log_cdf.
 
         .. math::
             cdf(x) = \exp(log_cdf(x))
         """
-        return self.exp(self._log_cdf(*args, **kwargs))
+        return self.exp(self._log_cdf(value, *args, **kwargs))
 
-    def _calc_cdf_from_survival(self, *args, **kwargs):
+    def _calc_cdf_from_survival(self, value, *args, **kwargs):
         r"""
         Evaluate cdf from survival function.
 
         .. math::
             cdf(x) =  1 - (survival_function(x))
         """
-        return 1.0 - self._survival_function(*args, **kwargs)
+        return 1.0 - self._survival_function(value, *args, **kwargs)
 
-    def _calc_cdf_from_log_survival(self, *args, **kwargs):
+    def _calc_cdf_from_log_survival(self, value, *args, **kwargs):
         r"""
         Evaluate cdf from log survival function.
 
         .. math::
             cdf(x) =  1 - (\exp(log_survival(x)))
         """
-        return 1.0 - self.exp(self._log_survival(*args, **kwargs))
+        return 1.0 - self.exp(self._log_survival(value, *args, **kwargs))
 
-    def log_cdf(self, *args, **kwargs):
+    def log_cdf(self, value, *args, **kwargs):
         """
         Evaluate the log cdf at given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_log_cdf(*args, **kwargs)
+        return self._call_log_cdf(value, *args, **kwargs)
 
-    def _calc_log_cdf_from_call_cdf(self, *args, **kwargs):
+    def _calc_log_cdf_from_call_cdf(self, value, *args, **kwargs):
         r"""
         Evaluate log cdf from cdf.
 
         .. math::
             log_cdf(x) = \log(cdf(x))
         """
-        return self.log(self._call_cdf(*args, **kwargs))
+        return self.log(self._call_cdf(value, *args, **kwargs))
 
-    def survival_function(self, *args, **kwargs):
+    def survival_function(self, value, *args, **kwargs):
         """
         Evaluate the survival function at given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_survival(*args, **kwargs)
+        return self._call_survival(value, *args, **kwargs)
 
-    def _calc_survival_from_call_cdf(self, *args, **kwargs):
+    def _calc_survival_from_call_cdf(self, value, *args, **kwargs):
         r"""
         Evaluate survival function from cdf.
 
         .. math::
             survival_function(x) =  1 - (cdf(x))
         """
-        return 1.0 - self._call_cdf(*args, **kwargs)
+        return 1.0 - self._call_cdf(value, *args, **kwargs)
 
-    def _calc_survival_from_log_survival(self, *args, **kwargs):
+    def _calc_survival_from_log_survival(self, value, *args, **kwargs):
         r"""
         Evaluate survival function from log survival function.
 
         .. math::
             survival(x) = \exp(survival_function(x))
         """
-        return self.exp(self._log_survival(*args, **kwargs))
+        return self.exp(self._log_survival(value, *args, **kwargs))
 
-    def log_survival(self, *args, **kwargs):
+    def log_survival(self, value, *args, **kwargs):
         """
         Evaluate the log survival function at given value.
 
+        Args:
+            value (Tensor): value to be evaluated.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The arguments `args` must include `value`.
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its dist_spec_args through
+            `args` or `kwargs`.
         """
-        return self._call_log_survival(*args, **kwargs)
+        return self._call_log_survival(value, *args, **kwargs)
 
-    def _calc_log_survival_from_call_survival(self, *args, **kwargs):
+    def _calc_log_survival_from_call_survival(self, value, *args, **kwargs):
         r"""
         Evaluate log survival function from survival function.
 
         .. math::
             log_survival(x) = \log(survival_function(x))
         """
-        return self.log(self._call_survival(*args, **kwargs))
+        return self.log(self._call_survival(value, *args, **kwargs))
 
-    def kl_loss(self, *args, **kwargs):
+    def kl_loss(self, dist, *args, **kwargs):
         """
         Evaluate the KL divergence, i.e. KL(a||b).
 
+        Args:
+            dist (str): type of the distribution.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include the type of the distribution, parameters of distribution b.
-            Parameters for distribution a are optional.
+            dist_spec_args of distribution b must be passed to the function through `args` or `kwargs`.
+            Passing in dist_spec_args of distribution a is optional.
         """
-        return self._kl_loss(*args, **kwargs)
+        return self._kl_loss(dist, *args, **kwargs)
 
     def mean(self, *args, **kwargs):
         """
         Evaluate the mean.
 
+        Args:
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._mean(*args, **kwargs)
 
@@ -375,8 +461,13 @@ class Distribution(Cell):
         """
         Evaluate the mode.
 
+        Args:
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._mode(*args, **kwargs)
 
@@ -384,8 +475,13 @@ class Distribution(Cell):
         """
         Evaluate the standard deviation.
 
+        Args:
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._call_sd(*args, **kwargs)
 
@@ -393,8 +489,13 @@ class Distribution(Cell):
         """
         Evaluate the variance.
 
+        Args:
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._call_var(*args, **kwargs)
 
@@ -420,37 +521,52 @@ class Distribution(Cell):
         """
         Evaluate the entropy.
 
+        Args:
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._entropy(*args, **kwargs)
 
-    def cross_entropy(self, *args, **kwargs):
+    def cross_entropy(self, dist, *args, **kwargs):
         """
         Evaluate the cross_entropy between distribution a and b.
 
+        Args:
+            dist (str): type of the distribution.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            The argument `args` must include the type of the distribution, parameters of distribution b.
-            Parameters for distribution a are optional.
+            dist_spec_args of distribution b must be passed to the function through `args` or `kwargs`.
+            Passing in dist_spec_args of distribution a is optional.
         """
-        return self._call_cross_entropy(*args, **kwargs)
+        return self._call_cross_entropy(dist, *args, **kwargs)
 
-    def _calc_cross_entropy(self, *args, **kwargs):
+    def _calc_cross_entropy(self, dist, *args, **kwargs):
         r"""
         Evaluate cross_entropy from entropy and kl divergence.
 
         .. math::
             H(X, Y) = H(X) + KL(X||Y)
         """
-        return self._entropy(*args, **kwargs) + self._kl_loss(*args, **kwargs)
+        return self._entropy(*args, **kwargs) + self._kl_loss(dist, *args, **kwargs)
 
     def sample(self, *args, **kwargs):
         """
         Sampling function.
 
+        Args:
+            shape (tuple): shape of the sample.
+            *args (list): the list of positional arguments forwarded to subclasses.
+            **kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
+
         Note:
-            Shape of the sample is default to ().
-            dist_spec_args are optional.
+            A distribution can be optionally passed to the function by passing its *dist_spec_args* through
+            *args* or *kwargs*.
         """
         return self._sample(*args, **kwargs)
 

mindspore/nn/probability/distribution/exponential.py

@@ -18,8 +18,7 @@ from mindspore.ops import operations as P
 from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
-from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name,\
-                          raise_none_error
+from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name, set_param_type
 from ._utils.custom_ops import exp_generic, log_generic
 
 class Exponential(Distribution):
@@ -121,15 +120,19 @@ class Exponential(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Exponential, self).__init__(seed, dtype, name, param)
-        self.parameter_type = dtype
+        self.parameter_type = set_param_type({'rate': rate}, self.dtype)
         if rate is not None:
             self._rate = cast_to_tensor(rate, self.parameter_type)
             check_greater_zero(self._rate, "rate")
         else:
             self._rate = rate
 
+        self.default_parameters = [self.rate]
+        self.parameter_names = ['rate']
+
         self.minval = np.finfo(np.float).tiny
 
+
         # ops needed for the class
         self.exp = exp_generic
         self.log = log_generic
@@ -156,28 +159,16 @@ class Exponential(Distribution):
     @property
     def rate(self):
         """
-        Return rate of the distribution.
+        Return `rate` of the distribution.
         """
         return self._rate
 
-    def _check_param(self, rate):
-        """
-        Check availablity of distribution specific argument `rate`.
-        """
-        if rate is not None:
-            if self.context_mode == 0:
-                self.checktensor(rate, 'rate')
-            else:
-                rate = self.checktensor(rate, 'rate')
-            return self.cast(rate, self.parameter_type)
-        return self.rate if self.rate is not None else raise_none_error('rate')
-
     def _mean(self, rate=None):
         r"""
         .. math::
             MEAN(EXP) = \frac{1.0}{\lambda}.
         """
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         return 1.0 / rate
 
     def _mode(self, rate=None):
@@ -185,7 +176,7 @@ class Exponential(Distribution):
         .. math::
             MODE(EXP) = 0.
         """
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         return self.fill(self.dtype, self.shape(rate), 0.)
 
     def _sd(self, rate=None):
@@ -193,7 +184,7 @@ class Exponential(Distribution):
         .. math::
             sd(EXP) = \frac{1.0}{\lambda}.
         """
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         return 1.0 / rate
 
     def _entropy(self, rate=None):
@@ -201,7 +192,7 @@ class Exponential(Distribution):
         .. math::
             H(Exp) = 1 - \log(\lambda).
         """
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         return 1.0 - self.log(rate)
 
     def _cross_entropy(self, dist, rate_b, rate=None):
@@ -234,7 +225,7 @@ class Exponential(Distribution):
         """
         value = self._check_value(value, "value")
         value = self.cast(value, self.dtype)
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         prob = self.log(rate) - rate * value
         zeros = self.fill(self.dtypeop(prob), self.shape(prob), 0.0)
         neginf = self.fill(self.dtypeop(prob), self.shape(prob), -np.inf)
@@ -257,7 +248,7 @@ class Exponential(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        rate = self._check_param(rate)
+        rate = self._check_param_type(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)
@@ -279,7 +270,7 @@ class Exponential(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         sf = -1. * rate * value
         zeros = self.fill(self.dtypeop(sf), self.shape(sf), 0.0)
         comp = self.less(value, zeros)
@@ -297,7 +288,7 @@ class Exponential(Distribution):
         check_distribution_name(dist, 'Exponential')
         rate_b = self._check_value(rate_b, 'rate_b')
         rate_b = self.cast(rate_b, self.parameter_type)
-        rate_a = self._check_param(rate)
+        rate_a = self._check_param_type(rate)
         return self.log(rate_a) - self.log(rate_b) + rate_b / rate_a - 1.0
 
     def _sample(self, shape=(), rate=None):
@@ -312,7 +303,7 @@ class Exponential(Distribution):
             Tensor, shape is shape + batch_shape.
         """
         shape = self.checktuple(shape, 'shape')
-        rate = self._check_param(rate)
+        rate = self._check_param_type(rate)
         origin_shape = shape + self.shape(rate)
         if origin_shape == ():
             sample_shape = (1,)

mindspore/nn/probability/distribution/geometric.py

@@ -19,7 +19,7 @@ from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name,\
-    raise_none_error
+                          set_param_type
 from ._utils.custom_ops import exp_generic, log_generic
 
 
@@ -123,13 +123,16 @@ class Geometric(Distribution):
         valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Geometric, self).__init__(seed, dtype, name, param)
-        self.parameter_type = mstype.float32
+        self.parameter_type = set_param_type({'probs1': probs}, mstype.float32)
         if probs is not None:
             self._probs = cast_to_tensor(probs, self.parameter_type)
             check_prob(self._probs)
         else:
             self._probs = probs
 
+        self.default_parameters = [self.probs]
+        self.parameter_names = ['probs1']
+
         self.minval = np.finfo(np.float).tiny
 
         # ops needed for the class
@@ -164,24 +167,12 @@ class Geometric(Distribution):
         """
         return self._probs
 
-    def _check_param(self, probs1):
-        """
-        Check availablity of distribution specific args probs1.
-        """
-        if probs1 is not None:
-            if self.context_mode == 0:
-                self.checktensor(probs1, 'probs1')
-            else:
-                probs1 = self.checktensor(probs1, 'probs1')
-            return self.cast(probs1, self.parameter_type)
-        return self.probs if self.probs is not None else raise_none_error('probs1')
-
     def _mean(self, probs1=None):
         r"""
         .. math::
             MEAN(Geo) = \fratc{1 - probs1}{probs1}
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         return (1. - probs1) / probs1
 
     def _mode(self, probs1=None):
@@ -189,7 +180,7 @@ class Geometric(Distribution):
         .. math::
             MODE(Geo) = 0
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         return self.fill(self.dtypeop(probs1), self.shape(probs1), 0.)
 
     def _var(self, probs1=None):
@@ -197,7 +188,7 @@ class Geometric(Distribution):
         .. math::
             VAR(Geo) = \frac{1 - probs1}{probs1 ^ {2}}
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         return (1.0 - probs1) / self.sq(probs1)
 
     def _entropy(self, probs1=None):
@@ -205,7 +196,7 @@ class Geometric(Distribution):
         .. math::
             H(Geo) = \frac{-1 * probs0 \log_2 (1-probs0)\ - prob1 * \log_2 (1-probs1)\ }{probs1}
         """
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         probs0 = 1.0 - probs1
         return (-probs0 * self.log(probs0) - probs1 * self.log(probs1)) / probs1
 
@@ -236,7 +227,7 @@ class Geometric(Distribution):
         value = self._check_value(value, 'value')
         value = self.cast(value, mstype.float32)
         value = self.floor(value)
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         pmf = self.exp(self.log(1.0 - probs1) * value + self.log(probs1))
         zeros = self.fill(self.dtypeop(probs1), self.shape(pmf), 0.0)
         comp = self.less(value, zeros)
@@ -258,7 +249,7 @@ class Geometric(Distribution):
         value = self._check_value(value, 'value')
         value = self.cast(value, mstype.float32)
         value = self.floor(value)
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         probs0 = 1.0 - probs1
         cdf = 1.0 - self.pow(probs0, value + 1.0)
         zeros = self.fill(self.dtypeop(probs1), self.shape(cdf), 0.0)
@@ -280,7 +271,7 @@ class Geometric(Distribution):
         check_distribution_name(dist, 'Geometric')
         probs1_b = self._check_value(probs1_b, 'probs1_b')
         probs1_b = self.cast(probs1_b, self.parameter_type)
-        probs1_a = self._check_param(probs1)
+        probs1_a = self._check_param_type(probs1)
         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)
@@ -297,7 +288,7 @@ class Geometric(Distribution):
             Tensor, shape is shape + batch_shape.
         """
         shape = self.checktuple(shape, 'shape')
-        probs1 = self._check_param(probs1)
+        probs1 = self._check_param_type(probs1)
         origin_shape = shape + self.shape(probs1)
         if origin_shape == ():
             sample_shape = (1,)

mindspore/nn/probability/distribution/normal.py

@@ -19,7 +19,7 @@ from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name,\
-                          raise_none_error, common_dtype
+                          set_param_type
 from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, erf_generic
 
 class Normal(Distribution):
@@ -127,14 +127,17 @@ class Normal(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Normal, self).__init__(seed, dtype, name, param)
-        self.parameter_type = common_dtype(mean, 'mean', sd, 'sd', self.dtype)
+        self.parameter_type = set_param_type({'mean': mean, 'sd': sd}, self.dtype)
         if  mean is not None and sd is not None:
             self._mean_value = cast_to_tensor(mean, self.parameter_type)
             self._sd_value = cast_to_tensor(sd, self.parameter_type)
             check_greater_zero(self._sd_value, "Standard deviation")
         else:
-            self._mean_value = mean
-            self._sd_value = sd
+            self._mean_value = mean if mean is None else cast_to_tensor(mean, self.parameter_type)
+            self._sd_value = sd if sd is None else cast_to_tensor(sd, self.parameter_type)
+
+        self.default_parameters = [self._mean_value, self._sd_value]
+        self.parameter_names = ['mean', 'sd']
 
         #ops needed for the class
         self.exp = exp_generic
@@ -159,51 +162,25 @@ class Normal(Distribution):
             str_info = f'batch_shape = {self._broadcast_shape}'
         return str_info
 
-    def _check_param(self, mean, sd):
-        """
-        Check availablity of distribution specific args `mean` and `sd`.
-        """
-        if mean is not None:
-            if self.context_mode == 0:
-                self.checktensor(mean, 'mean')
-            else:
-                mean = self.checktensor(mean, 'mean')
-        else:
-            mean = self._mean_value if self._mean_value is not None else raise_none_error('mean')
-        if sd is not None:
-            if self.context_mode == 0:
-                self.checktensor(sd, 'sd')
-            else:
-                sd = self.checktensor(sd, 'sd')
-        else:
-            sd = self._sd_value if self._sd_value is not None else raise_none_error('sd')
-        batch_shape = self.shape(mean + sd)
-        mean = mean * self.fill(self.dtypeop(mean), batch_shape, 1.0)
-        sd = sd * self.fill(self.dtypeop(sd), batch_shape, 1.0)
-        self.sametypeshape(mean, sd)
-        mean = self.cast(mean, self.parameter_type)
-        sd = self.cast(sd, self.parameter_type)
-        return mean, sd
-
     def _mean(self, mean=None, sd=None):
         """
         The mean of the distribution.
         """
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         return mean
 
     def _mode(self, mean=None, sd=None):
         """
         The mode of the distribution.
         """
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         return mean
 
     def _sd(self, mean=None, sd=None):
         """
         The standard deviation of the distribution.
         """
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         return sd
 
     def _entropy(self, mean=None, sd=None):
@@ -213,7 +190,7 @@ class Normal(Distribution):
         .. math::
             H(X) = \log(\sqrt(numpy.e * 2. * numpy.pi * \sq(\sigma)))
         """
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         return self.log(self.sqrt(self.const(np.e * 2. * np.pi))) + self.log(sd)
 
     def _cross_entropy(self, dist, mean_b, sd_b, mean=None, sd=None):
@@ -244,7 +221,7 @@ class Normal(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         unnormalized_log_prob = -1. * (self.sq(value - mean)) / (2. * self.sq(sd))
         neg_normalization = -1. * self.log(self.const(2. * np.pi)) / 2. - self.log(sd)
         return unnormalized_log_prob + neg_normalization
@@ -263,7 +240,7 @@ class Normal(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         sqrt2 = self.sqrt(self.const(2.0))
         adjusted = (value - mean) / (sd * sqrt2)
         return 0.5 * (1.0 + self.erf(adjusted))
@@ -288,7 +265,7 @@ class Normal(Distribution):
         sd_b = self._check_value(sd_b, 'sd_b')
         mean_b = self.cast(mean_b, self.parameter_type)
         sd_b = self.cast(sd_b, self.parameter_type)
-        mean_a, sd_a = self._check_param(mean, sd)
+        mean_a, sd_a = self._check_param_type(mean, sd)
         diff_log_scale = self.log(sd_a) - self.log(sd_b)
         squared_diff = self.sq(mean_a / sd_b - mean_b / sd_b)
         return 0.5 * squared_diff + 0.5 * self.expm1(2 * diff_log_scale) - diff_log_scale
@@ -306,7 +283,7 @@ class Normal(Distribution):
             Tensor, shape is shape + batch_shape.
         """
         shape = self.checktuple(shape, 'shape')
-        mean, sd = self._check_param(mean, sd)
+        mean, sd = self._check_param_type(mean, sd)
         batch_shape = self.shape(mean + sd)
         origin_shape = shape + batch_shape
         if origin_shape == ():

mindspore/nn/probability/distribution/uniform.py

@@ -18,7 +18,7 @@ from mindspore.ops import composite as C
 from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_greater, check_type, check_distribution_name,\
-                          raise_none_error, common_dtype
+                          set_param_type
 from ._utils.custom_ops import exp_generic, log_generic
 
 class Uniform(Distribution):
@@ -126,14 +126,17 @@ class Uniform(Distribution):
         valid_dtype = mstype.float_type
         check_type(dtype, valid_dtype, type(self).__name__)
         super(Uniform, self).__init__(seed, dtype, name, param)
-        self.parameter_type = common_dtype(low, 'low', high, 'high', self.dtype)
+        self.parameter_type = set_param_type({'low': low, 'high': high}, self.dtype)
         if low is not None and high is not None:
-            self._low = cast_to_tensor(low, dtype)
-            self._high = cast_to_tensor(high, dtype)
+            self._low = cast_to_tensor(low, self.parameter_type)
+            self._high = cast_to_tensor(high, self.parameter_type)
             check_greater(self.low, self.high, "low value", "high value")
         else:
-            self._low = low
-            self._high = high
+            self._low = low if low is None else cast_to_tensor(low, self.parameter_type)
+            self._high = high if high is None else cast_to_tensor(high, self.parameter_type)
+
+        self.default_parameters = [self.low, self.high]
+        self.parameter_names = ['low', 'high']
 
         # ops needed for the class
         self.exp = exp_generic
@@ -162,32 +165,6 @@ class Uniform(Distribution):
             str_info = f'batch_shape = {self._broadcast_shape}'
         return str_info
 
-    def _check_param(self, low, high):
-        """
-        Check availablity of distribution specific args `low` and `high`.
-        """
-        if low is not None:
-            if self.context_mode == 0:
-                self.checktensor(low, 'low')
-            else:
-                low = self.checktensor(low, 'low')
-        else:
-            low = self.low if self.low is not None else raise_none_error('low')
-        if high is not None:
-            if self.context_mode == 0:
-                self.checktensor(high, 'high')
-            else:
-                high = self.checktensor(high, 'high')
-        else:
-            high = self.high if self.high is not None else raise_none_error('high')
-        batch_shape = self.shape(high - low)
-        high = high * self.fill(self.dtypeop(high), batch_shape, 1.0)
-        low = low * self.fill(self.dtypeop(low), batch_shape, 1.0)
-        self.sametypeshape(high, low)
-        low = self.cast(low, self.parameter_type)
-        high = self.cast(high, self.parameter_type)
-        return low, high
-
     @property
     def low(self):
         """
@@ -209,7 +186,7 @@ class Uniform(Distribution):
         .. math::
             range(U) = high -low
         """
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         return high - low
 
     def _mean(self, low=None, high=None):
@@ -217,7 +194,7 @@ class Uniform(Distribution):
         .. math::
             MEAN(U) = \frac{low + high}{2}.
         """
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         return (low + high) / 2.
 
     def _var(self, low=None, high=None):
@@ -225,7 +202,7 @@ class Uniform(Distribution):
         .. math::
             VAR(U) = \frac{(high -low) ^ 2}{12}.
         """
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         return self.sq(high - low) / 12.0
 
     def _entropy(self, low=None, high=None):
@@ -233,7 +210,7 @@ class Uniform(Distribution):
         .. math::
             H(U) = \log(high - low).
         """
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         return self.log(high - low)
 
     def _cross_entropy(self, dist, low_b, high_b, low=None, high=None):
@@ -266,7 +243,7 @@ class Uniform(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         neg_ones = self.fill(self.dtype, self.shape(value), -1.0)
         prob = self.exp(neg_ones * self.log(high - low))
         broadcast_shape = self.shape(prob)
@@ -292,7 +269,7 @@ class Uniform(Distribution):
         low_b = self.cast(low_b, self.parameter_type)
         high_b = self._check_value(high_b, 'high_b')
         high_b = self.cast(high_b, self.parameter_type)
-        low_a, high_a = self._check_param(low, high)
+        low_a, high_a = self._check_param_type(low, high)
         kl = self.log(high_b - low_b) - self.log(high_a - low_a)
         comp = self.logicaland(self.lessequal(low_b, low_a), self.lessequal(high_a, high_b))
         return self.select(comp, kl, self.log(self.zeroslike(kl)))
@@ -313,7 +290,7 @@ class Uniform(Distribution):
         """
         value = self._check_value(value, 'value')
         value = self.cast(value, self.dtype)
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         prob = (value - low) / (high - low)
         broadcast_shape = self.shape(prob)
         zeros = self.fill(self.dtypeop(prob), broadcast_shape, 0.0)
@@ -336,7 +313,7 @@ class Uniform(Distribution):
             Tensor, shape is shape + batch_shape.
         """
         shape = self.checktuple(shape, 'shape')
-        low, high = self._check_param(low, high)
+        low, high = self._check_param_type(low, high)
         broadcast_shape = self.shape(low + high)
         origin_shape = shape + broadcast_shape
         if origin_shape == ():

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

@@ -0,0 +1,182 @@
+# 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 util functions used in distribution classes.
+"""
+import numpy as np
+import pytest
+
+from mindspore.nn.cell import Cell
+from mindspore import context
+from mindspore import dtype
+from mindspore import Tensor
+from mindspore.common.parameter import Parameter
+from mindspore.nn.probability.distribution._utils.utils import set_param_type, \
+    cast_to_tensor, CheckTuple, CheckTensor
+
+def test_set_param_type():
+    """
+    Test set_param_type function.
+    """
+    tensor_fp16 = Tensor(0.1, dtype=dtype.float16)
+    tensor_fp32 = Tensor(0.1, dtype=dtype.float32)
+    tensor_fp64 = Tensor(0.1, dtype=dtype.float64)
+    tensor_int32 = Tensor(0.1, dtype=dtype.int32)
+    array_fp32 = np.array(1.0).astype(np.float32)
+    array_fp64 = np.array(1.0).astype(np.float64)
+    array_int32 = np.array(1.0).astype(np.int32)
+
+    dict1 = {'a': tensor_fp32, 'b': 1.0, 'c': tensor_fp32}
+    dict2 = {'a': tensor_fp32, 'b': 1.0, 'c': tensor_fp64}
+    dict3 = {'a': tensor_int32, 'b': 1.0, 'c': tensor_int32}
+    dict4 = {'a': array_fp32, 'b': 1.0, 'c': tensor_fp32}
+    dict5 = {'a': array_fp32, 'b': 1.0, 'c': array_fp64}
+    dict6 = {'a': array_fp32, 'b': 1.0, 'c': array_int32}
+    dict7 = {'a': 1.0}
+    dict8 = {'a': 1.0, 'b': 1.0, 'c': 1.0}
+    dict9 = {'a': tensor_fp16, 'b': tensor_fp16, 'c': tensor_fp16}
+    dict10 = {'a': tensor_fp64, 'b': tensor_fp64, 'c': tensor_fp64}
+    dict11 = {'a': array_fp64, 'b': array_fp64, 'c': tensor_fp64}
+
+    ans1 = set_param_type(dict1, dtype.float16)
+    assert ans1 == dtype.float32
+
+    with pytest.raises(TypeError):
+        set_param_type(dict2, dtype.float32)
+
+    ans3 = set_param_type(dict3, dtype.float16)
+    assert ans3 == dtype.float32
+    ans4 = set_param_type(dict4, dtype.float16)
+    assert ans4 == dtype.float32
+
+    with pytest.raises(TypeError):
+        set_param_type(dict5, dtype.float32)
+    with pytest.raises(TypeError):
+        set_param_type(dict6, dtype.float32)
+
+    ans7 = set_param_type(dict7, dtype.float32)
+    assert ans7 == dtype.float32
+    ans8 = set_param_type(dict8, dtype.float32)
+    assert ans8 == dtype.float32
+    ans9 = set_param_type(dict9, dtype.float32)
+    assert ans9 == dtype.float16
+    ans10 = set_param_type(dict10, dtype.float32)
+    assert ans10 == dtype.float32
+    ans11 = set_param_type(dict11, dtype.float32)
+    assert ans11 == dtype.float32
+
+def test_cast_to_tensor():
+    """
+    Test cast_to_tensor.
+    """
+    with pytest.raises(ValueError):
+        cast_to_tensor(None, dtype.float32)
+    with pytest.raises(TypeError):
+        cast_to_tensor(True, dtype.float32)
+    with pytest.raises(TypeError):
+        cast_to_tensor({'a': 1, 'b': 2}, dtype.float32)
+    with pytest.raises(TypeError):
+        cast_to_tensor('tensor', dtype.float32)
+
+    ans1 = cast_to_tensor(Parameter(Tensor(0.1, dtype=dtype.float32), 'param'))
+    assert isinstance(ans1, Parameter)
+    ans2 = cast_to_tensor(np.array(1.0).astype(np.float32))
+    assert isinstance(ans2, Tensor)
+    ans3 = cast_to_tensor([1.0, 2.0])
+    assert isinstance(ans3, Tensor)
+    ans4 = cast_to_tensor(Tensor(0.1, dtype=dtype.float32), dtype.float32)
+    assert isinstance(ans4, Tensor)
+    ans5 = cast_to_tensor(0.1, dtype.float32)
+    assert isinstance(ans5, Tensor)
+    ans6 = cast_to_tensor(1, dtype.float32)
+    assert isinstance(ans6, Tensor)
+
+class Net(Cell):
+    """
+    Test class: CheckTuple.
+    """
+    def __init__(self, value):
+        super(Net, self).__init__()
+        self.checktuple = CheckTuple()
+        self.value = value
+
+    def construct(self, value=None):
+        if value is None:
+            return self.checktuple(self.value, 'input')
+        return self.checktuple(value, 'input')
+
+def test_check_tuple():
+    """
+    Test CheckTuple.
+    """
+    net1 = Net((1, 2, 3))
+    ans1 = net1()
+    assert isinstance(ans1, tuple)
+
+    with pytest.raises(TypeError):
+        net2 = Net('tuple')
+        net2()
+
+    context.set_context(mode=context.GRAPH_MODE)
+    net3 = Net((1, 2, 3))
+    ans3 = net3()
+    assert isinstance(ans3, tuple)
+
+    with pytest.raises(TypeError):
+        net4 = Net('tuple')
+        net4()
+
+class Net1(Cell):
+    """
+    Test class: CheckTensor.
+    """
+    def __init__(self, value):
+        super(Net1, self).__init__()
+        self.checktensor = CheckTensor()
+        self.value = value
+        self.context = context.get_context('mode')
+
+    def construct(self, value=None):
+        value = self.value if value is None else value
+        if self.context == 0:
+            self.checktensor(value, 'input')
+            return value
+        return self.checktensor(value, 'input')
+
+def test_check_tensor():
+    """
+    Test CheckTensor.
+    """
+    value = Tensor(0.1, dtype=dtype.float32)
+    net1 = Net1(value)
+    ans1 = net1()
+    assert isinstance(ans1, Tensor)
+    ans1 = net1(value)
+    assert isinstance(ans1, Tensor)
+
+    with pytest.raises(TypeError):
+        net2 = Net1('tuple')
+        net2()
+
+    context.set_context(mode=context.GRAPH_MODE)
+    net3 = Net1(value)
+    ans3 = net3()
+    assert isinstance(ans3, Tensor)
+    ans3 = net3(value)
+    assert isinstance(ans3, Tensor)
+
+    with pytest.raises(TypeError):
+        net4 = Net1('tuple')
+        net4()