!5005 Fix the bug in the formula in Bernoulli log_probs

Merge pull request !5005 from zichun_ye/fix_bernoulli_probs

mindspore/nn/probability/bijector/power_transform.py

@@ -20,6 +20,7 @@ from ..distribution._utils.utils import CheckTensor
 from ..distribution._utils.custom_ops import exp_by_step, expm1_by_step, log_by_step, log1p_by_step
 from .bijector import Bijector
 
+
 class PowerTransform(Bijector):
     r"""
     Power Bijector.
@@ -49,6 +50,7 @@ class PowerTransform(Bijector):
         >>>         # by replacing 'forward' with the name of the function
         >>>         ans = self.p1.forward(, value)
     """
+
     def __init__(self,
                  power=0,
                  name='PowerTransform',
@@ -78,13 +80,13 @@ class PowerTransform(Bijector):
         return shape
 
     def _forward(self, x):
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         if self.power == 0:
             return self.exp(x)
         return self.exp(self.log1p(x * self.power) / self.power)
 
     def _inverse(self, y):
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         if self.power == 0:
             return self.log(y)
         return self.expm1(self.log(y) * self.power) / self.power
@@ -101,7 +103,7 @@ class PowerTransform(Bijector):
                 f'(x) = e^\frac{\log(xc + 1)}{c} * \frac{1}{xc + 1}
                 \log(f'(x)) =  (\frac{1}{c} - 1) * \log(xc + 1)
         """
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         if self.power == 0:
             return x
         return (1. / self.power - 1) * self.log1p(x * self.power)
@@ -118,5 +120,5 @@ class PowerTransform(Bijector):
                 f'(x) = \frac{e^c\log(y)}{y}
                 \log(f'(x)) =  \log(\frac{e^c\log(y)}{y}) = (c-1) * \log(y)
         """
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         return (self.power - 1) * self.log(y)

mindspore/nn/probability/bijector/scalar_affine.py

@@ -19,6 +19,7 @@ from ..distribution._utils.utils import cast_to_tensor, CheckTensor
 from ..distribution._utils.custom_ops import log_by_step
 from .bijector import Bijector
 
+
 class ScalarAffine(Bijector):
     """
     Scalar Affine Bijector.
@@ -47,6 +48,7 @@ class ScalarAffine(Bijector):
         >>>         ans = self.s1.forward_log_jacobian(value)
         >>>         ans = self.s1.inverse_log_jacobian(value)
     """
+
     def __init__(self,
                  scale=1.0,
                  shift=0.0,
@@ -91,7 +93,7 @@ class ScalarAffine(Bijector):
         .. math::
             f(x) = a * x + b
         """
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         return self.scale * x + self.shift
 
     def _inverse(self, y):
@@ -99,7 +101,7 @@ class ScalarAffine(Bijector):
         .. math::
             f(y) = \frac{y - b}{a}
         """
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         return (y - self.shift) / self.scale
 
     def _forward_log_jacobian(self, x):
@@ -109,7 +111,7 @@ class ScalarAffine(Bijector):
             f'(x) = a
             \log(f'(x)) = \log(a)
         """
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         return self.log(self.abs(self.scale))
 
     def _inverse_log_jacobian(self, y):
@@ -119,5 +121,5 @@ class ScalarAffine(Bijector):
             f'(x) = \frac{1.0}{a}
             \log(f'(x)) = - \log(a)
         """
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         return -1. * self.log(self.abs(self.scale))

mindspore/nn/probability/bijector/softplus.py

@@ -22,6 +22,7 @@ from ..distribution._utils.utils import cast_to_tensor, CheckTensor
 from ..distribution._utils.custom_ops import exp_by_step, expm1_by_step, log_by_step
 from .bijector import Bijector
 
+
 class Softplus(Bijector):
     r"""
     Softplus Bijector.
@@ -51,6 +52,7 @@ class Softplus(Bijector):
         >>>         ans = self.sp1.forward_log_jacobian(value)
         >>>         ans = self.sp1.inverse_log_jacobian(value)
     """
+
     def __init__(self,
                  sharpness=1.0,
                  name='Softplus'):
@@ -76,6 +78,7 @@ class Softplus(Bijector):
 
         self.checktensor = CheckTensor()
         self.threshold = np.log(np.finfo(np.float32).eps) + 1
+        self.tiny = np.exp(self.threshold)
 
     def _softplus(self, x):
         too_small = self.less(x, self.threshold)
@@ -94,7 +97,7 @@ class Softplus(Bijector):
             f(x) = \frac{\log(1 + e^{x}))}
             f^{-1}(y) = \frac{\log(e^{y} - 1)}
         """
-        too_small = self.less(x, self.threshold)
+        too_small = self.less(x, self.tiny)
         too_large = self.greater(x, -self.threshold)
         too_small_value = self.log(x)
         too_large_value = x
@@ -116,7 +119,7 @@ class Softplus(Bijector):
         return shape
 
     def _forward(self, x):
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         scaled_value = self.sharpness * x
         return self.softplus(scaled_value) / self.sharpness
 
@@ -126,7 +129,7 @@ class Softplus(Bijector):
             f(x) = \frac{\log(1 + e^{kx}))}{k}
             f^{-1}(y) = \frac{\log(e^{ky} - 1)}{k}
         """
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         scaled_value = self.sharpness * y
         return self.inverse_softplus(scaled_value) / self.sharpness
 
@@ -137,7 +140,7 @@ class Softplus(Bijector):
             f'(x) = \frac{e^{kx}}{ 1 + e^{kx}}
             \log(f'(x)) =  kx - \log(1 + e^{kx}) = kx - f(kx)
         """
-        self.checktensor(x, 'x')
+        self.checktensor(x, 'value')
         scaled_value = self.sharpness * x
         return self.log_sigmoid(scaled_value)
 
@@ -148,6 +151,6 @@ class Softplus(Bijector):
             f'(y) = \frac{e^{ky}}{e^{ky} - 1}
             \log(f'(y)) = ky - \log(e^{ky} - 1) = ky - f(ky)
         """
-        self.checktensor(y, 'y')
+        self.checktensor(y, 'value')
         scaled_value = self.sharpness * y
         return scaled_value - self.inverse_softplus(scaled_value)

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

@@ -26,6 +26,7 @@ from mindspore import context
 import mindspore.nn as nn
 import mindspore.nn.probability as msp
 
+
 def cast_to_tensor(t, hint_type=mstype.float32):
     """
     Cast an user input value into a Tensor of dtype.
@@ -47,7 +48,7 @@ def cast_to_tensor(t, hint_type=mstype.float32):
         return t
     t_type = hint_type
     if isinstance(t, Tensor):
-        #convert the type of tensor to dtype
+        # convert the type of tensor to dtype
         return Tensor(t.asnumpy(), dtype=t_type)
     if isinstance(t, (list, np.ndarray)):
         return Tensor(t, dtype=t_type)
@@ -56,7 +57,8 @@ def cast_to_tensor(t, hint_type=mstype.float32):
     if isinstance(t, (int, float)):
         return Tensor(t, dtype=t_type)
     invalid_type = type(t)
-    raise TypeError(f"Unable to convert input of type {invalid_type} to a Tensor of type {t_type}")
+    raise TypeError(
+        f"Unable to convert input of type {invalid_type} to a Tensor of type {t_type}")
 
 
 def convert_to_batch(t, batch_shape, required_type):
@@ -79,6 +81,7 @@ def convert_to_batch(t, batch_shape, required_type):
     t = cast_to_tensor(t, required_type)
     return Tensor(np.broadcast_to(t.asnumpy(), batch_shape), dtype=required_type)
 
+
 def check_scalar_from_param(params):
     """
     Check if params are all scalars.
@@ -93,11 +96,7 @@ def check_scalar_from_param(params):
             return params['distribution'].is_scalar_batch
         if isinstance(value, Parameter):
             return False
-        if isinstance(value, (str, type(params['dtype']))):
-            continue
-        elif isinstance(value, (int, float)):
-            continue
-        else:
+        if not isinstance(value, (int, float, str, type(params['dtype']))):
             return False
     return True
 
@@ -124,7 +123,8 @@ def calc_broadcast_shape_from_param(params):
             value_t = value.default_input
         else:
             value_t = cast_to_tensor(value, mstype.float32)
-        broadcast_shape = utils.get_broadcast_shape(broadcast_shape, list(value_t.shape), params['name'])
+        broadcast_shape = utils.get_broadcast_shape(
+            broadcast_shape, list(value_t.shape), params['name'])
     return tuple(broadcast_shape)
 
 
@@ -148,6 +148,7 @@ def check_greater_equal_zero(value, name):
     if comp.any():
         raise ValueError(f'{name} should be greater than ot equal to zero.')
 
+
 def check_greater_zero(value, name):
     """
     Check if the given Tensor is strictly greater than zero.
@@ -251,6 +252,7 @@ def probs_to_logits(probs, is_binary=False):
         return P.Log()(ps_clamped) - P.Log()(1-ps_clamped)
     return P.Log()(ps_clamped)
 
+
 def check_tensor_type(name, inputs, valid_type):
     """
    Check if inputs is proper.
@@ -268,25 +270,34 @@ def check_tensor_type(name, inputs, valid_type):
     if input_type not in valid_type:
         raise TypeError(f"{name} dtype is invalid")
 
+
 def check_type(data_type, value_type, name):
     if not data_type in value_type:
-        raise TypeError(f"For {name}, valid type include {value_type}, {data_type} is invalid")
+        raise TypeError(
+            f"For {name}, valid type include {value_type}, {data_type} is invalid")
+
 
 @constexpr
 def raise_none_error(name):
     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):
-    raise ValueError(f"{name} function should be implemented for non-linear transformation")
+    raise ValueError(
+        f"{name} function should be implemented for non-linear transformation")
+
 
 @constexpr
 def check_distribution_name(name, expected_name):
     if name is None:
-        raise ValueError(f"Distribution should be a constant which is not None.")
+        raise ValueError(
+            f"Distribution should be a constant which is not None.")
     if name != expected_name:
-        raise ValueError(f"Expected distribution name is {expected_name}, but got {name}.")
+        raise ValueError(
+            f"Expected distribution name is {expected_name}, but got {name}.")
+
 
 class CheckTuple(PrimitiveWithInfer):
     """
@@ -294,13 +305,13 @@ class CheckTuple(PrimitiveWithInfer):
     """
     @prim_attr_register
     def __init__(self):
-        """init Cast"""
         super(CheckTuple, self).__init__("CheckTuple")
-        self.init_prim_io_names(inputs=['x'], outputs=['dummy_output'])
+        self.init_prim_io_names(inputs=['x', 'name'], outputs=['dummy_output'])
 
     def __infer__(self, x, name):
         if not isinstance(x['dtype'], tuple):
-            raise TypeError(f"For {name['value']}, Input type should b a tuple.")
+            raise TypeError(
+                f"For {name['value']}, Input type should b a tuple.")
 
         out = {'shape': None,
                'dtype': None,
@@ -310,24 +321,25 @@ class CheckTuple(PrimitiveWithInfer):
     def __call__(self, x, name):
         if context.get_context("mode") == 0:
             return x["value"]
-        #Pynative mode
+        # Pynative mode
         if isinstance(x, tuple):
             return x
         raise TypeError(f"For {name['value']}, Input type should b a tuple.")
 
+
 class CheckTensor(PrimitiveWithInfer):
     """
     Check if input is a Tensor.
     """
     @prim_attr_register
     def __init__(self):
-        """init Cast"""
         super(CheckTensor, self).__init__("CheckTensor")
-        self.init_prim_io_names(inputs=['x'], outputs=['dummy_output'])
+        self.init_prim_io_names(inputs=['x', 'name'], outputs=['dummy_output'])
 
     def __infer__(self, x, name):
         src_type = x['dtype']
-        validator.check_subclass("input", src_type, [mstype.tensor], name["value"])
+        validator.check_subclass(
+            "input", src_type, [mstype.tensor], name["value"])
 
         out = {'shape': None,
                'dtype': None,

mindspore/nn/probability/distribution/bernoulli.py

@@ -20,6 +20,7 @@ from .distribution import Distribution
 from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, raise_none_error
 from ._utils.custom_ops import exp_by_step, log_by_step
 
+
 class Bernoulli(Distribution):
     """
     Bernoulli Distribution.
@@ -97,7 +98,7 @@ class Bernoulli(Distribution):
         Constructor of Bernoulli distribution.
         """
         param = dict(locals())
-        valid_dtype = mstype.int_type + mstype.uint_type
+        valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
         check_type(dtype, valid_dtype, "Bernoulli")
         super(Bernoulli, self).__init__(seed, dtype, name, param)
         self.parameter_type = mstype.float32
@@ -211,7 +212,6 @@ class Bernoulli(Distribution):
         """
         self.checktensor(value, 'value')
         value = self.cast(value, mstype.float32)
-        value = self.floor(value)
         probs1 = self._check_param(probs1)
         probs0 = 1.0 - probs1
         return self.log(probs1) * value + self.log(probs0) * (1.0 - value)

mindspore/nn/probability/distribution/geometric.py

@@ -19,9 +19,10 @@ 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
+    raise_none_error
 from ._utils.custom_ops import exp_by_step, log_by_step
 
+
 class Geometric(Distribution):
     """
     Geometric Distribution.
@@ -100,7 +101,7 @@ class Geometric(Distribution):
         Constructor of Geometric distribution.
         """
         param = dict(locals())
-        valid_dtype = mstype.int_type + mstype.uint_type
+        valid_dtype = mstype.int_type + mstype.uint_type + mstype.float_type
         check_type(dtype, valid_dtype, "Geometric")
         super(Geometric, self).__init__(seed, dtype, name, param)
         self.parameter_type = mstype.float32
@@ -130,7 +131,6 @@ class Geometric(Distribution):
         self.sqrt = P.Sqrt()
         self.uniform = C.uniform
 
-
     def extend_repr(self):
         if self.is_scalar_batch:
             str_info = f'probs = {self.probs}'
@@ -243,7 +243,6 @@ class Geometric(Distribution):
         comp = self.less(value, zeros)
         return self.select(comp, zeros, cdf)
 
-
     def _kl_loss(self, dist, probs1_b, probs1=None):
         r"""
         Evaluate Geometric-Geometric kl divergence, i.e. KL(a||b).

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

@@ -22,6 +22,7 @@ import mindspore.nn.probability.distribution as msd
 from mindspore import dtype
 from mindspore import Tensor
 
+
 def test_arguments():
     """
     Args passing during initialization.
@@ -31,18 +32,22 @@ def test_arguments():
     b = msd.Bernoulli([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
     assert isinstance(b, msd.Distribution)
 
+
 def test_type():
     with pytest.raises(TypeError):
-        msd.Bernoulli([0.1], dtype=dtype.float32)
+        msd.Bernoulli([0.1], dtype=dtype.bool_)
+
 
 def test_name():
     with pytest.raises(TypeError):
         msd.Bernoulli([0.1], name=1.0)
 
+
 def test_seed():
     with pytest.raises(TypeError):
         msd.Bernoulli([0.1], seed='seed')
 
+
 def test_prob():
     """
     Invalid probability.
@@ -56,10 +61,12 @@ def test_prob():
     with pytest.raises(ValueError):
         msd.Bernoulli([1.0], dtype=dtype.int32)
 
+
 class BernoulliProb(nn.Cell):
     """
     Bernoulli distribution: initialize with probs.
     """
+
     def __init__(self):
         super(BernoulliProb, self).__init__()
         self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
@@ -73,6 +80,7 @@ class BernoulliProb(nn.Cell):
         log_sf = self.b.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
+
 def test_bernoulli_prob():
     """
     Test probability functions: passing value through construct.
@@ -82,10 +90,12 @@ def test_bernoulli_prob():
     ans = net(value)
     assert isinstance(ans, Tensor)
 
+
 class BernoulliProb1(nn.Cell):
     """
     Bernoulli distribution: initialize without probs.
     """
+
     def __init__(self):
         super(BernoulliProb1, self).__init__()
         self.b = msd.Bernoulli(dtype=dtype.int32)
@@ -99,6 +109,7 @@ class BernoulliProb1(nn.Cell):
         log_sf = self.b.log_survival(value, probs)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
+
 def test_bernoulli_prob1():
     """
     Test probability functions: passing value/probs through construct.
@@ -109,10 +120,12 @@ def test_bernoulli_prob1():
     ans = net(value, probs)
     assert isinstance(ans, Tensor)
 
+
 class BernoulliKl(nn.Cell):
     """
     Test class: kl_loss between Bernoulli distributions.
     """
+
     def __init__(self):
         super(BernoulliKl, self).__init__()
         self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
@@ -123,6 +136,7 @@ class BernoulliKl(nn.Cell):
         kl2 = self.b2.kl_loss('Bernoulli', probs_b, probs_a)
         return kl1 + kl2
 
+
 def test_kl():
     """
     Test kl_loss function.
@@ -133,10 +147,12 @@ def test_kl():
     ans = ber_net(probs_b, probs_a)
     assert isinstance(ans, Tensor)
 
+
 class BernoulliCrossEntropy(nn.Cell):
     """
     Test class: cross_entropy of Bernoulli distribution.
     """
+
     def __init__(self):
         super(BernoulliCrossEntropy, self).__init__()
         self.b1 = msd.Bernoulli(0.7, dtype=dtype.int32)
@@ -147,6 +163,7 @@ class BernoulliCrossEntropy(nn.Cell):
         h2 = self.b2.cross_entropy('Bernoulli', probs_b, probs_a)
         return h1 + h2
 
+
 def test_cross_entropy():
     """
     Test cross_entropy between Bernoulli distributions.
@@ -157,10 +174,12 @@ def test_cross_entropy():
     ans = net(probs_b, probs_a)
     assert isinstance(ans, Tensor)
 
+
 class BernoulliConstruct(nn.Cell):
     """
     Bernoulli distribution: going through construct.
     """
+
     def __init__(self):
         super(BernoulliConstruct, self).__init__()
         self.b = msd.Bernoulli(0.5, dtype=dtype.int32)
@@ -172,6 +191,7 @@ class BernoulliConstruct(nn.Cell):
         prob2 = self.b1('prob', value, probs)
         return prob + prob1 + prob2
 
+
 def test_bernoulli_construct():
     """
     Test probability function going through construct.
@@ -182,10 +202,12 @@ def test_bernoulli_construct():
     ans = net(value, probs)
     assert isinstance(ans, Tensor)
 
+
 class BernoulliMean(nn.Cell):
     """
     Test class: basic mean/sd/var/mode/entropy function.
     """
+
     def __init__(self):
         super(BernoulliMean, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
@@ -194,6 +216,7 @@ class BernoulliMean(nn.Cell):
         mean = self.b.mean()
         return mean
 
+
 def test_mean():
     """
     Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
@@ -202,10 +225,12 @@ def test_mean():
     ans = net()
     assert isinstance(ans, Tensor)
 
+
 class BernoulliSd(nn.Cell):
     """
     Test class: basic mean/sd/var/mode/entropy function.
     """
+
     def __init__(self):
         super(BernoulliSd, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
@@ -214,6 +239,7 @@ class BernoulliSd(nn.Cell):
         sd = self.b.sd()
         return sd
 
+
 def test_sd():
     """
     Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
@@ -222,10 +248,12 @@ def test_sd():
     ans = net()
     assert isinstance(ans, Tensor)
 
+
 class BernoulliVar(nn.Cell):
     """
     Test class: basic mean/sd/var/mode/entropy function.
     """
+
     def __init__(self):
         super(BernoulliVar, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
@@ -234,6 +262,7 @@ class BernoulliVar(nn.Cell):
         var = self.b.var()
         return var
 
+
 def test_var():
     """
     Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
@@ -242,10 +271,12 @@ def test_var():
     ans = net()
     assert isinstance(ans, Tensor)
 
+
 class BernoulliMode(nn.Cell):
     """
     Test class: basic mean/sd/var/mode/entropy function.
     """
+
     def __init__(self):
         super(BernoulliMode, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
@@ -254,6 +285,7 @@ class BernoulliMode(nn.Cell):
         mode = self.b.mode()
         return mode
 
+
 def test_mode():
     """
     Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.
@@ -262,10 +294,12 @@ def test_mode():
     ans = net()
     assert isinstance(ans, Tensor)
 
+
 class BernoulliEntropy(nn.Cell):
     """
     Test class: basic mean/sd/var/mode/entropy function.
     """
+
     def __init__(self):
         super(BernoulliEntropy, self).__init__()
         self.b = msd.Bernoulli([0.3, 0.5], dtype=dtype.int32)
@@ -274,6 +308,7 @@ class BernoulliEntropy(nn.Cell):
         entropy = self.b.entropy()
         return entropy
 
+
 def test_entropy():
     """
     Test mean/sd/var/mode/entropy functionality of Bernoulli distribution.

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

@@ -32,18 +32,22 @@ def test_arguments():
     g = msd.Geometric([0.1, 0.3, 0.5, 0.9], dtype=dtype.int32)
     assert isinstance(g, msd.Distribution)
 
+
 def test_type():
     with pytest.raises(TypeError):
-        msd.Geometric([0.1], dtype=dtype.float32)
+        msd.Geometric([0.1], dtype=dtype.bool_)
+
 
 def test_name():
     with pytest.raises(TypeError):
         msd.Geometric([0.1], name=1.0)
 
+
 def test_seed():
     with pytest.raises(TypeError):
         msd.Geometric([0.1], seed='seed')
 
+
 def test_prob():
     """
     Invalid probability.
@@ -57,10 +61,12 @@ def test_prob():
     with pytest.raises(ValueError):
         msd.Geometric([1.0], dtype=dtype.int32)
 
+
 class GeometricProb(nn.Cell):
     """
     Geometric distribution: initialize with probs.
     """
+
     def __init__(self):
         super(GeometricProb, self).__init__()
         self.g = msd.Geometric(0.5, dtype=dtype.int32)
@@ -74,6 +80,7 @@ class GeometricProb(nn.Cell):
         log_sf = self.g.log_survival(value)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
+
 def test_geometric_prob():
     """
     Test probability functions: passing value through construct.
@@ -83,10 +90,12 @@ def test_geometric_prob():
     ans = net(value)
     assert isinstance(ans, Tensor)
 
+
 class GeometricProb1(nn.Cell):
     """
     Geometric distribution: initialize without probs.
     """
+
     def __init__(self):
         super(GeometricProb1, self).__init__()
         self.g = msd.Geometric(dtype=dtype.int32)
@@ -100,6 +109,7 @@ class GeometricProb1(nn.Cell):
         log_sf = self.g.log_survival(value, probs)
         return prob + log_prob + cdf + log_cdf + sf + log_sf
 
+
 def test_geometric_prob1():
     """
     Test probability functions: passing value/probs through construct.
@@ -115,6 +125,7 @@ class GeometricKl(nn.Cell):
     """
     Test class: kl_loss between Geometric distributions.
     """
+
     def __init__(self):
         super(GeometricKl, self).__init__()
         self.g1 = msd.Geometric(0.7, dtype=dtype.int32)
@@ -125,6 +136,7 @@ class GeometricKl(nn.Cell):
         kl2 = self.g2.kl_loss('Geometric', probs_b, probs_a)
         return kl1 + kl2
 
+
 def test_kl():
     """
     Test kl_loss function.
@@ -135,10 +147,12 @@ def test_kl():
     ans = ber_net(probs_b, probs_a)
     assert isinstance(ans, Tensor)
 
+
 class GeometricCrossEntropy(nn.Cell):
     """
     Test class: cross_entropy of Geometric distribution.
     """
+
     def __init__(self):
         super(GeometricCrossEntropy, self).__init__()
         self.g1 = msd.Geometric(0.3, dtype=dtype.int32)
@@ -149,6 +163,7 @@ class GeometricCrossEntropy(nn.Cell):
         h2 = self.g2.cross_entropy('Geometric', probs_b, probs_a)
         return h1 + h2
 
+
 def test_cross_entropy():
     """
     Test cross_entropy between Geometric distributions.
@@ -159,10 +174,12 @@ def test_cross_entropy():
     ans = net(probs_b, probs_a)
     assert isinstance(ans, Tensor)
 
+
 class GeometricBasics(nn.Cell):
     """
     Test class: basic mean/sd/mode/entropy function.
     """
+
     def __init__(self):
         super(GeometricBasics, self).__init__()
         self.g = msd.Geometric([0.3, 0.5], dtype=dtype.int32)
@@ -175,6 +192,7 @@ class GeometricBasics(nn.Cell):
         entropy = self.g.entropy()
         return mean + sd + var + mode + entropy
 
+
 def test_bascis():
     """
     Test mean/sd/mode/entropy functionality of Geometric distribution.
@@ -188,6 +206,7 @@ class GeoConstruct(nn.Cell):
     """
     Bernoulli distribution: going through construct.
     """
+
     def __init__(self):
         super(GeoConstruct, self).__init__()
         self.g = msd.Geometric(0.5, dtype=dtype.int32)
@@ -199,6 +218,7 @@ class GeoConstruct(nn.Cell):
         prob2 = self.g1('prob', value, probs)
         return prob + prob1 + prob2
 
+
 def test_geo_construct():
     """
     Test probability function going through construct.