!4021 Add ScalarAffine and Softplus bijector

Merge pull request !4021 from XunDeng/scalar_affine_softplus

mindspore/nn/probability/bijector/__init__.py

@@ -21,7 +21,13 @@ The high-level components(Bijectors) used to construct the probabilistic network
 from .bijector import Bijector
 from .power_transform import PowerTransform
 from .exp import Exp
+from .scalar_affine import ScalarAffine
+from .softplus import Softplus
 
-__all__ = ['Bijector',
-           'PowerTransform',
-           'Exp']
+__all__ = [
+    'Bijector',
+    'PowerTransform',
+    'Exp',
+    'ScalarAffine',
+    'Softplus',
+]

mindspore/nn/probability/bijector/bijector.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """Bijector"""
 from mindspore.nn.cell import Cell
+from mindspore._checkparam import Validator as validator
 from ..distribution import Distribution
 from ..distribution import TransformedDistribution
 
@@ -39,6 +40,9 @@ class Bijector(Cell):
         Constructor of bijector class.
         """
         super(Bijector, self).__init__()
+        validator.check_value_type('name', name, [str], 'Bijector')
+        validator.check_value_type('is_constant_jacobian', is_constant_jacobian, [bool], name)
+        validator.check_value_type('is_injective', is_injective, [bool], name)
         self._name = name
         self._dtype = dtype
         self._parameters = {}

mindspore/nn/probability/bijector/scalar_affine.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Scalar Affine Bijector"""
+from mindspore.ops import operations as P
+from mindspore._checkparam import Validator as validator
+from ..distribution._utils.utils import cast_to_tensor
+from .bijector import Bijector
+
+class ScalarAffine(Bijector):
+    """
+    Scalar Affine Bijector.
+    This Bijector performs the operation: Y = a * X + b, where a is the scale
+    factor and b is the shift factor.
+
+    Args:
+        scale (float): scale factor. Default: 1.0.
+        shift (float): shift factor. Default: 0.0.
+
+    Examples:
+        >>> # To initialize a ScalarAffine bijector of scale 1 and shift 2
+        >>> scalaraffine = nn.probability.bijector.ScalarAffine(1, 2)
+        >>>
+        >>> # To use ScalarAffine bijector in a network
+        >>> class net(Cell):
+        >>>     def __init__(self):
+        >>>         super(net, self).__init__():
+        >>>         self.s1 = nn.probability.bijector.ScalarAffine(1, 2)
+        >>>
+        >>>     def construct(self, value):
+        >>>         # Similar calls can be made to other probability functions
+        >>>         # by replacing 'forward' with the name of the function
+        >>>         ans = self.s1.forward(value)
+        >>>         ans = self.s1.inverse(value)
+        >>>         ans = self.s1.forward_log_jacobian(value)
+        >>>         ans = self.s1.inverse_log_jacobian(value)
+    """
+    def __init__(self,
+                 scale=1.0,
+                 shift=0.0,
+                 name='ScalarAffine'):
+        """
+        Constructor of scalar affine bijector.
+        """
+        param = dict(locals())
+        validator.check_value_type('scale', scale, [float], name)
+        validator.check_value_type('shift', shift, [float], name)
+        self._scale = cast_to_tensor(scale)
+        self._shift = cast_to_tensor(shift)
+        super(ScalarAffine, self).__init__(
+            is_constant_jacobian=True,
+            is_injective=True,
+            name=name,
+            dtype=None,
+            param=param)
+
+        self.log = P.Log()
+        self.oneslike = P.OnesLike()
+
+    @property
+    def scale(self):
+        return self._scale
+
+    @property
+    def shift(self):
+        return self._shift
+
+    def extend_repr(self):
+        str_info = f'scale = {self.scale}, shift = {self.shift}'
+        return str_info
+
+    def shape_mapping(self, shape):
+        return shape
+
+    def _forward(self, x):
+        r"""
+        .. math::
+            f(x) = a * x + b
+        """
+        return self.scale * x + self.shift
+
+    def _inverse(self, y):
+        r"""
+        .. math::
+            f(y) = \frac{y - b}{a}
+        """
+        return (y - self.shift) / self.scale
+
+    def _forward_log_jacobian(self, value):
+        r"""
+        .. math::
+            f(x) = a * x + b
+            f'(x) = a
+            \log(f'(x)) = \log(a)
+        """
+        return self.log(self.scale) * self.oneslike(value)
+
+    def _inverse_log_jacobian(self, value):
+        r"""
+        .. math::
+            f(y) = \frac{(y - b)}{a}
+            f'(x) = \frac{1.0}{a}
+            \log(f'(x)) = - \log(a)
+        """
+        return -1. * self.log(self.scale) * self.oneslike(value)

mindspore/nn/probability/bijector/softplus.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Softplus Bijector"""
+from mindspore.ops import operations as P
+from mindspore.nn.layer.activation import LogSigmoid
+from mindspore._checkparam import Validator as validator
+from ..distribution._utils.utils import cast_to_tensor
+from .bijector import Bijector
+
+class Softplus(Bijector):
+    r"""
+    Softplus Bijector.
+    This Bijector performs the operation: Y = \frac{\log(1 + e ^ {kX})}{k}, where k is the sharpness factor.
+
+    Args:
+        sharpness (float): scale factor. Default: 1.0.
+
+    Examples:
+        >>> # To initialize a Softplus bijector of sharpness 2
+        >>> softplus = nn.probability.bijector.Softfplus(2)
+        >>>
+        >>> # To use ScalarAffine bijector in a network
+        >>> class net(Cell):
+        >>>     def __init__(self):
+        >>>         super(net, self).__init__():
+        >>>         self.sp1 = nn.probability.bijector.Softflus(2)
+        >>>
+        >>>     def construct(self, value):
+        >>>         # Similar calls can be made to other probability functions
+        >>>         # by replacing 'forward' with the name of the function
+        >>>         ans = self.sp1.forward(value)
+        >>>         ans = self.sp1.inverse(value)
+        >>>         ans = self.sp1.forward_log_jacobian(value)
+        >>>         ans = self.sp1.inverse_log_jacobian(value)
+    """
+    def __init__(self,
+                 sharpness=1.0,
+                 name='Softplus'):
+        param = dict(locals())
+        validator.check_value_type('sharpness', sharpness, [float], name)
+        super(Softplus, self).__init__(name=name, param=param)
+        self._sharpness = cast_to_tensor(sharpness)
+
+        self.exp = P.Exp()
+        self.expm1 = self._expm1_by_step
+        self.log_sigmoid = LogSigmoid()
+        self.log = P.Log()
+        self.sigmoid = P.Sigmoid()
+
+        self.softplus = self._softplus
+        self.inverse_softplus = self._inverse_softplus
+
+    def _expm1_by_step(self, x):
+        """
+        Expm1 ops under GPU context.
+        """
+        return self.exp(x) - 1.0
+
+    def _softplus(self, x):
+        return self.log(self.exp(x) + 1.0)
+
+    def _inverse_softplus(self, x):
+        r"""
+        .. math::
+            f(x) = \frac{\log(1 + e^{x}))}
+            f^{-1}(y) = \frac{\log(e^{y} - 1)}
+        """
+        return self.log(self.expm1(x))
+
+    @property
+    def sharpness(self):
+        return self._sharpness
+
+    def extend_repr(self):
+        str_info = f'sharpness = {self.sharpness}'
+        return str_info
+
+    def shape_mapping(self, shape):
+        return shape
+
+    def _forward(self, x):
+        scaled_value = self.sharpness * x
+        return self.softplus(scaled_value) / self.sharpness
+
+    def _inverse(self, y):
+        r"""
+        .. math::
+            f(x) = \frac{\log(1 + e^{kx}))}{k}
+            f^{-1}(y) = \frac{\log(e^{ky} - 1)}{k}
+        """
+        scaled_value = self.sharpness * y
+        return self.inverse_softplus(scaled_value) / self.sharpness
+
+    def _forward_log_jacobian(self, x):
+        r"""
+        .. math:
+            f(x) = \log(1 + e^{kx}) / k
+            f'(x) = \frac{e^{kx}}{ 1 + e^{kx}}
+            \log(f'(x)) =  kx - \log(1 + e^{kx}) = kx - f(kx)
+        """
+        scaled_value = self.sharpness * x
+        return self.log_sigmoid(scaled_value)
+
+    def _inverse_log_jacobian(self, y):
+        r"""
+        .. math:
+            f(y) = \frac{\log(e^{ky} - 1)}{k}
+            f'(y) = \frac{e^{ky}}{e^{ky} - 1}
+            \log(f'(y)) = ky - \log(e^{ky} - 1) = ky - f(ky)
+        """
+        scaled_value = self.sharpness * y
+        return scaled_value - self.inverse_softplus(scaled_value)

tests/st/ops/ascend/test_bijector/test_scalar_affine.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test cases for scalar affine"""
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.bijector as msb
+from mindspore import Tensor
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Net(nn.Cell):
+    """
+    Test class: forward pass of bijector.
+    """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.bijector = msb.ScalarAffine(scale=2.0, shift=1.0)
+
+    def construct(self, x_):
+        return self.bijector.forward(x_)
+
+def test_forward():
+    forward = Net()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = forward(Tensor(x, dtype=dtype.float32))
+    tol = 1e-6
+    expected = 2 * x + 1
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net1(nn.Cell):
+    """
+    Test class: backward pass of bijector.
+    """
+    def __init__(self):
+        super(Net1, self).__init__()
+        self.bijector = msb.ScalarAffine(shift=1.0, scale=2.0)
+
+    def construct(self, x_):
+        return self.bijector.inverse(x_)
+
+def test_backward():
+    backward = Net1()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = backward(Tensor(x, dtype=dtype.float32))
+    tol = 1e-6
+    expected = 0.5 * (x - 1.0)
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net2(nn.Cell):
+    """
+    Test class: Forward Jacobian.
+    """
+    def __init__(self):
+        super(Net2, self).__init__()
+        self.bijector = msb.ScalarAffine(shift=1.0, scale=2.0)
+
+    def construct(self, x_):
+        return self.bijector.forward_log_jacobian(x_)
+
+def test_forward_jacobian():
+    forward_jacobian = Net2()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = forward_jacobian(x)
+    expected = np.log([2.0, 2.0, 2.0, 2.0])
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net3(nn.Cell):
+    """
+    Test class: Backward Jacobian.
+    """
+    def __init__(self):
+        super(Net3, self).__init__()
+        self.bijector = msb.ScalarAffine(shift=1.0, scale=2.0)
+
+    def construct(self, x_):
+        return self.bijector.inverse_log_jacobian(x_)
+
+def test_backward_jacobian():
+    backward_jacobian = Net3()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = backward_jacobian(x)
+    expected = np.log([0.5, 0.5, 0.5, 0.5])
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()

tests/st/ops/ascend/test_bijector/test_softplus.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test cases for scalar affine"""
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.bijector as msb
+from mindspore import Tensor
+from mindspore import dtype
+
+context.set_context(device_target="Ascend")
+
+class Net(nn.Cell):
+    """
+    Test class: forward pass of bijector.
+    """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.bijector = msb.Softplus(sharpness=2.0)
+
+    def construct(self, x_):
+        return self.bijector.forward(x_)
+
+def test_forward():
+    forward = Net()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = forward(Tensor(x, dtype=dtype.float32))
+    expected = np.log(1 + np.exp(2 * x)) * 0.5
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net1(nn.Cell):
+    """
+    Test class: backward pass of bijector.
+    """
+    def __init__(self):
+        super(Net1, self).__init__()
+        self.bijector = msb.Softplus(sharpness=2.0)
+
+    def construct(self, x_):
+        return self.bijector.inverse(x_)
+
+def test_backward():
+    backward = Net1()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = backward(Tensor(x, dtype=dtype.float32))
+    expected = np.log(np.exp(2 * x) - 1) * 0.5
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net2(nn.Cell):
+    """
+    Test class: Forward Jacobian.
+    """
+    def __init__(self):
+        super(Net2, self).__init__()
+        self.bijector = msb.Softplus(sharpness=2.0)
+
+    def construct(self, x_):
+        return self.bijector.forward_log_jacobian(x_)
+
+def test_forward_jacobian():
+    forward_jacobian = Net2()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = forward_jacobian(Tensor(x, dtype=dtype.float32))
+    expected = np.log(np.exp(2 * x) / (1 + np.exp(2.0 * x)))
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()
+
+class Net3(nn.Cell):
+    """
+    Test class: Backward Jacobian.
+    """
+    def __init__(self):
+        super(Net3, self).__init__()
+        self.bijector = msb.Softplus(sharpness=2.0)
+
+    def construct(self, x_):
+        return self.bijector.inverse_log_jacobian(x_)
+
+def test_backward_jacobian():
+    backward_jacobian = Net3()
+    x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
+    ans = backward_jacobian(Tensor(x, dtype=dtype.float32))
+    expected = np.log(np.exp(2.0 * x) / np.expm1(2.0 * x))
+    tol = 1e-6
+    assert (np.abs(ans.asnumpy() - expected) < tol).all()

tests/ut/python/nn/bijector/test_exp.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """test cases for exp"""
+import pytest
 import mindspore.nn as nn
 import mindspore.nn.probability.bijector as msb
 from mindspore import Tensor
@@ -21,8 +22,10 @@ from mindspore import dtype
 def test_init():
     b = msb.Exp()
     assert isinstance(b, msb.Bijector)
-    b = msb.Exp(1.0)
-    assert isinstance(b, msb.Bijector)
+
+def test_type():
+    with pytest.raises(TypeError):
+        msb.Exp(name=0.1)
 
 class Net(nn.Cell):
     """

tests/ut/python/nn/bijector/test_power_transform.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """test cases for powertransform"""
+import pytest
 import mindspore.nn as nn
 import mindspore.nn.probability.bijector as msb
 from mindspore import Tensor
@@ -24,6 +25,12 @@ def test_init():
     b = msb.PowerTransform(1)
     assert isinstance(b, msb.Bijector)
 
+def test_type():
+    with pytest.raises(TypeError):
+        msb.PowerTransform(power='power')
+    with pytest.raises(TypeError):
+        msb.PowerTransform(name=0.1)
+
 class Net(nn.Cell):
     """
     Test class: forward and inverse pass of bijector.

tests/ut/python/nn/bijector/test_scalar_affine.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test cases for scalar affine"""
+import pytest
+import mindspore.nn as nn
+import mindspore.nn.probability.bijector as msb
+from mindspore import Tensor
+from mindspore import dtype
+
+def test_init():
+    """
+    Test initializations.
+    """
+    b = msb.ScalarAffine()
+    assert isinstance(b, msb.Bijector)
+    b = msb.ScalarAffine(scale=1.0)
+    assert isinstance(b, msb.Bijector)
+    b = msb.ScalarAffine(shift=2.0)
+    assert isinstance(b, msb.Bijector)
+    b = msb.ScalarAffine(3.0, 4.0)
+    assert isinstance(b, msb.Bijector)
+
+def test_type():
+    with pytest.raises(TypeError):
+        msb.ScalarAffine(scale='scale')
+    with pytest.raises(TypeError):
+        msb.ScalarAffine(shift='shift')
+    with pytest.raises(TypeError):
+        msb.ScalarAffine(name=0.1)
+
+class ForwardBackward(nn.Cell):
+    """
+    Test class: forward and backward pass.
+    """
+    def __init__(self):
+        super(ForwardBackward, self).__init__()
+        self.b1 = msb.ScalarAffine(2.0, 1.0)
+        self.b2 = msb.ScalarAffine()
+
+    def construct(self, x_):
+        ans1 = self.b1.inverse(self.b1.forward(x_))
+        ans2 = self.b2.inverse(self.b2.forward(x_))
+        return ans1 + ans2
+
+def test_forward_and_backward_pass():
+    """
+    Test forward and backward pass of ScalarAffine bijector.
+    """
+    net = ForwardBackward()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+class ForwardJacobian(nn.Cell):
+    """
+    Test class: Forward log Jacobian.
+    """
+    def __init__(self):
+        super(ForwardJacobian, self).__init__()
+        self.b1 = msb.ScalarAffine(2.0, 1.0)
+        self.b2 = msb.ScalarAffine()
+
+    def construct(self, x_):
+        ans1 = self.b1.forward_log_jacobian(x_)
+        ans2 = self.b2.forward_log_jacobian(x_)
+        return ans1 + ans2
+
+def test_forward_jacobian():
+    """
+    Test forward log jacobian of ScalarAffine bijector.
+    """
+    net = ForwardJacobian()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+
+class BackwardJacobian(nn.Cell):
+    """
+    Test class: Backward log Jacobian.
+    """
+    def __init__(self):
+        super(BackwardJacobian, self).__init__()
+        self.b1 = msb.ScalarAffine(2.0, 1.0)
+        self.b2 = msb.ScalarAffine()
+
+    def construct(self, x_):
+        ans1 = self.b1.inverse_log_jacobian(x_)
+        ans2 = self.b2.inverse_log_jacobian(x_)
+        return ans1 + ans2
+
+def test_backward_jacobian():
+    """
+    Test backward log jacobian of ScalarAffine bijector.
+    """
+    net = BackwardJacobian()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+
+class Net(nn.Cell):
+    """
+    Test class: function calls going through construct.
+    """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.b1 = msb.ScalarAffine(1.0, 0.0)
+        self.b2 = msb.ScalarAffine()
+
+    def construct(self, x_):
+        ans1 = self.b1('inverse', self.b1('forward', x_))
+        ans2 = self.b2('inverse', self.b2('forward', x_))
+        ans3 = self.b1('forward_log_jacobian', x_)
+        ans4 = self.b2('forward_log_jacobian', x_)
+        ans5 = self.b1('inverse_log_jacobian', x_)
+        ans6 = self.b2('inverse_log_jacobian', x_)
+        return ans1 - ans2 + ans3 -ans4 + ans5 - ans6
+
+def test_old_api():
+    """
+    Test old api which goes through construct.
+    """
+    net = Net()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)

tests/ut/python/nn/bijector/test_softplus.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test cases for scalar affine"""
+import pytest
+import mindspore.nn as nn
+import mindspore.nn.probability.bijector as msb
+from mindspore import Tensor
+from mindspore import dtype
+
+def test_init():
+    """
+    Test initializations.
+    """
+    b = msb.Softplus()
+    assert isinstance(b, msb.Bijector)
+    b = msb.Softplus(1.0)
+    assert isinstance(b, msb.Bijector)
+
+def test_type():
+    with pytest.raises(TypeError):
+        msb.Softplus(sharpness='sharpness')
+    with pytest.raises(TypeError):
+        msb.Softplus(name=0.1)
+
+class ForwardBackward(nn.Cell):
+    """
+    Test class: forward and backward pass.
+    """
+    def __init__(self):
+        super(ForwardBackward, self).__init__()
+        self.b1 = msb.Softplus(2.0)
+        self.b2 = msb.Softplus()
+
+    def construct(self, x_):
+        ans1 = self.b1.inverse(self.b1.forward(x_))
+        ans2 = self.b2.inverse(self.b2.forward(x_))
+        return ans1 + ans2
+
+def test_forward_and_backward_pass():
+    """
+    Test forward and backward pass of Softplus bijector.
+    """
+    net = ForwardBackward()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+class ForwardJacobian(nn.Cell):
+    """
+    Test class: Forward log Jacobian.
+    """
+    def __init__(self):
+        super(ForwardJacobian, self).__init__()
+        self.b1 = msb.Softplus(2.0)
+        self.b2 = msb.Softplus()
+
+    def construct(self, x_):
+        ans1 = self.b1.forward_log_jacobian(x_)
+        ans2 = self.b2.forward_log_jacobian(x_)
+        return ans1 + ans2
+
+def test_forward_jacobian():
+    """
+    Test forward log jacobian of Softplus bijector.
+    """
+    net = ForwardJacobian()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+
+class BackwardJacobian(nn.Cell):
+    """
+    Test class: Backward log Jacobian.
+    """
+    def __init__(self):
+        super(BackwardJacobian, self).__init__()
+        self.b1 = msb.Softplus(2.0)
+        self.b2 = msb.Softplus()
+
+    def construct(self, x_):
+        ans1 = self.b1.inverse_log_jacobian(x_)
+        ans2 = self.b2.inverse_log_jacobian(x_)
+        return ans1 + ans2
+
+def test_backward_jacobian():
+    """
+    Test backward log jacobian of Softplus bijector.
+    """
+    net = BackwardJacobian()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)
+
+
+class Net(nn.Cell):
+    """
+    Test class: function calls going through construct.
+    """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.b1 = msb.Softplus(1.0)
+        self.b2 = msb.Softplus()
+
+    def construct(self, x_):
+        ans1 = self.b1('inverse', self.b1('forward', x_))
+        ans2 = self.b2('inverse', self.b2('forward', x_))
+        ans3 = self.b1('forward_log_jacobian', x_)
+        ans4 = self.b2('forward_log_jacobian', x_)
+        ans5 = self.b1('inverse_log_jacobian', x_)
+        ans6 = self.b2('inverse_log_jacobian', x_)
+        return ans1 - ans2 + ans3 -ans4 + ans5 - ans6
+
+def test_old_api():
+    """
+    Test old api which goes through construct.
+    """
+    net = Net()
+    x = Tensor([2.0, 3.0, 4.0, 5.0], dtype=dtype.float32)
+    ans = net(x)
+    assert isinstance(ans, Tensor)