Fixed CheckTuple issues and error message

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

@@ -22,6 +22,7 @@ from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 from mindspore.ops import composite as C
+from mindspore import context
 import mindspore.nn as nn
 import mindspore.nn.probability as msp
 
@@ -273,7 +274,8 @@ def check_type(data_type, value_type, name):
 
 @constexpr
 def raise_none_error(name):
-    raise ValueError(f"{name} should be specified. Value cannot be None")
+    raise TypeError(f"the type {name} should be subclass of Tensor."
+                    f" It should not be None since it is not specified during initialization.")
 
 @constexpr
 def raise_not_impl_error(name):
@@ -298,15 +300,20 @@ class CheckTuple(PrimitiveWithInfer):
 
     def __infer__(self, x, name):
         if not isinstance(x['dtype'], tuple):
-            raise TypeError("Input type should be a tuple: " + name["value"])
+            raise TypeError(f"For {name['value']}, Input type should b a tuple.")
 
         out = {'shape': None,
                'dtype': None,
-               'value': None}
+               'value': x["value"]}
         return out
 
-    def __call__(self, *args):
-        return
+    def __call__(self, x, name):
+        if context.get_context("mode") == 0:
+            return x["value"]
+        #Pynative mode
+        if isinstance(x, tuple):
+            return x
+        raise TypeError(f"For {name['value']}, Input type should b a tuple.")
 
 class CheckTensor(PrimitiveWithInfer):
     """
@@ -327,5 +334,5 @@ class CheckTensor(PrimitiveWithInfer):
                'value': None}
         return out
 
-    def __call__(self, *args):
+    def __call__(self, x, name):
         return

mindspore/nn/probability/distribution/bernoulli.py

@@ -18,7 +18,6 @@ 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 CheckTensor, CheckTuple
 from ._utils.custom_ops import log_by_step
 
 class Bernoulli(Distribution):
@@ -125,9 +124,6 @@ class Bernoulli(Distribution):
         self.sqrt = P.Sqrt()
         self.uniform = C.uniform
 
-        self.checktensor = CheckTensor()
-        self.checktuple = CheckTuple()
-
     def extend_repr(self):
         if self.is_scalar_batch:
             str_info = f'probs = {self.probs}'
@@ -279,7 +275,7 @@ class Bernoulli(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        self.checktuple(shape, 'shape')
+        shape = self.checktuple(shape, 'shape')
         probs1 = self._check_param(probs1)
         origin_shape = shape + self.shape(probs1)
         if origin_shape == ():

mindspore/nn/probability/distribution/distribution.py

@@ -17,6 +17,7 @@ from mindspore.nn.cell import Cell
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
 from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param
+from ._utils.utils import CheckTuple, CheckTensor
 
 class Distribution(Cell):
     """
@@ -79,6 +80,9 @@ class Distribution(Cell):
         self._set_log_survival()
         self._set_cross_entropy()
 
+        self.checktuple = CheckTuple()
+        self.checktensor = CheckTensor()
+
     @property
     def name(self):
         return self._name

mindspore/nn/probability/distribution/exponential.py

@@ -20,7 +20,6 @@ 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 CheckTensor, CheckTuple
 from ._utils.custom_ops import log_by_step
 
 class Exponential(Distribution):
@@ -127,8 +126,6 @@ class Exponential(Distribution):
         self.sq = P.Square()
         self.uniform = C.uniform
 
-        self.checktensor = CheckTensor()
-        self.checktuple = CheckTuple()
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -270,7 +267,7 @@ class Exponential(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        self.checktuple(shape, 'shape')
+        shape = self.checktuple(shape, 'shape')
         rate = self._check_param(rate)
         origin_shape = shape + self.shape(rate)
         if origin_shape == ():

mindspore/nn/probability/distribution/geometric.py

@@ -20,7 +20,6 @@ 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
-from ._utils.utils import CheckTensor, CheckTuple
 from ._utils.custom_ops import log_by_step
 
 class Geometric(Distribution):
@@ -131,8 +130,6 @@ class Geometric(Distribution):
         self.sqrt = P.Sqrt()
         self.uniform = C.uniform
 
-        self.checktensor = CheckTensor()
-        self.checktuple = CheckTuple()
 
     def extend_repr(self):
         if self.is_scalar_batch:
@@ -278,7 +275,7 @@ class Geometric(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        self.checktuple(shape, 'shape')
+        shape = self.checktuple(shape, 'shape')
         probs1 = self._check_param(probs1)
         origin_shape = shape + self.shape(probs1)
         if origin_shape == ():

mindspore/nn/probability/distribution/normal.py

@@ -20,7 +20,6 @@ from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import convert_to_batch, check_greater_zero, check_type, check_distribution_name,\
                           raise_none_error
-from ._utils.utils import CheckTensor, CheckTuple
 from ._utils.custom_ops import log_by_step, expm1_by_step
 
 class Normal(Distribution):
@@ -128,9 +127,6 @@ class Normal(Distribution):
         self.sqrt = P.Sqrt()
         self.zeroslike = P.ZerosLike()
 
-        self.checktensor = CheckTensor()
-        self.checktuple = CheckTuple()
-
     def extend_repr(self):
         if self.is_scalar_batch:
             str_info = f'mean = {self._mean_value}, standard deviation = {self._sd_value}'
@@ -277,7 +273,7 @@ class Normal(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        self.checktuple(shape, 'shape')
+        shape = self.checktuple(shape, 'shape')
         mean, sd = self._check_param(mean, sd)
         batch_shape = self.shape(mean + sd)
         origin_shape = shape + batch_shape

mindspore/nn/probability/distribution/transformed_distribution.py

@@ -116,4 +116,4 @@ class TransformedDistribution(Distribution):
         if not self.is_linear_transformation:
             raise_not_impl_error("mean")
 
-        return self.bijector("forward", self.distribution("mean"))
+        return self.bijector("forward", self.distribution("mean", *args, **kwargs))

mindspore/nn/probability/distribution/uniform.py

@@ -19,7 +19,6 @@ from mindspore.common import dtype as mstype
 from .distribution import Distribution
 from ._utils.utils import convert_to_batch, check_greater, check_type, check_distribution_name,\
                           raise_none_error
-from ._utils.utils import CheckTensor, CheckTuple
 from ._utils.custom_ops import log_by_step
 
 class Uniform(Distribution):
@@ -131,9 +130,6 @@ class Uniform(Distribution):
         self.zeroslike = P.ZerosLike()
         self.uniform = C.uniform
 
-        self.checktensor = CheckTensor()
-        self.checktuple = CheckTuple()
-
     def extend_repr(self):
         if self.is_scalar_batch:
             str_info = f'low = {self.low}, high = {self.high}'
@@ -306,7 +302,7 @@ class Uniform(Distribution):
         Returns:
             Tensor, shape is shape + batch_shape.
         """
-        self.checktuple(shape, 'shape')
+        shape = self.checktuple(shape, 'shape')
         low, high = self._check_param(low, high)
         broadcast_shape = self.shape(low + high)
         origin_shape = shape + broadcast_shape