[fix][assistant][I3PYDB] fix bug in the Ascend operator SoftShrink and SoftShrinkGrad

mindspore/core/ops/soft_shrink.h

@@ -37,7 +37,6 @@ AbstractBasePtr SoftShrinkInfer(const abstract::AnalysisEnginePtr &, const Primi
                                 const std::vector<AbstractBasePtr> &input_args);
 
 using PrimSoftShrinkPtr = std::shared_ptr<SoftShrink>;
-
 }  // namespace ops
 }  // namespace mindspore
 #endif  // MINDSPORE_CORE_OPS_SOFTSHRINK_H_

mindspore/nn/layer/activation.py

@@ -39,6 +39,7 @@ __all__ = ['Softmax',
            'HSwish',
            'ELU',
            'LogSigmoid',
+           'SoftShrink',
            ]
 
 
@@ -754,6 +755,53 @@ class LogSigmoid(Cell):
         ret = self.log(rec_exp_neg_input_1)
         return ret
 
+class SoftShrink(Cell):
+    r"""
+    Applies the soft shrinkage function elementwise.
+
+    .. math::
+        \text{SoftShrink}(x) =
+        \begin{cases}
+        x - \lambda, & \text{ if } x > \lambda \\
+        x + \lambda, & \text{ if } x < -\lambda \\
+        0, & \text{ otherwise }
+        \end{cases}
+
+    Args:
+        lambd: the :math:`\lambda` must be no less than zero value for the Softshrink formulation. Default: 0.5.
+
+    Inputs:
+        - **input_x** (Tensor) - The input of SoftShrink with data type of float16 or float32.
+          Any number of additional dimensions.
+
+    Outputs:
+        Tensor, has the same shape and data type as `input_x`.
+
+    Raises:
+        TypeError: If lambd is not a float.
+        TypeError: If input_x is not a Tensor.
+        TypeError: If dtype of input_x is neither float16 nor float32.
+        ValueError: If lambd is less than 0.
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> input_x = Tensor(np.array([[ 0.5297,  0.7871,  1.1754], [ 0.7836,  0.6218, -1.1542]]), mstype.float16)
+        >>> softshrink = nn.SoftShrink()
+        >>> output = softshrink(input_x)
+        >>> print(output)
+        [[ 0.02979  0.287    0.676  ]
+         [ 0.2837   0.1216  -0.6543 ]]
+    """
+
+    def __init__(self, lambd=0.5):
+        super(SoftShrink, self).__init__()
+        self.softshrink = P.SoftShrink(lambd)
+
+    def construct(self, input_x):
+        output = self.softshrink(input_x)
+        return output
 
 _activation = {
     'softmax': Softmax,
@@ -770,6 +818,7 @@ _activation = {
     'hswish': HSwish,
     'hsigmoid': HSigmoid,
     'logsigmoid': LogSigmoid,
+    'softshrink': SoftShrink,
 }
 
 

mindspore/ops/_grad_experimental/grad_nn_ops.py

@@ -33,7 +33,6 @@ def get_bprop_ctc_loss_v2(self):
 
     return bprop
 
-"""nn_ops"""
 
 @bprop_getters.register(P.SoftShrink)
 def get_bprop_softshrink(self):

mindspore/ops/operations/_grad_ops.py

@@ -2206,8 +2206,9 @@ class SoftShrinkGrad(Primitive):
           Supported Platforms:
               ``Ascend``
       """
+
     @prim_attr_register
     def __init__(self, lambd=0.5):
         self.init_prim_io_names(inputs=['input_grad', 'input_x'], outputs=['output'])
         validator.check_value_type("lambd", lambd, [float], self.name)
-        validator.check_number("lambd", lambd, 0, Rel.GE, self.name)
+        validator.check_number("lambd", lambd, 0, Rel.GE, self.name)

mindspore/ops/operations/nn_ops.py

@@ -8670,6 +8670,7 @@ class Conv3DTranspose(PrimitiveWithInfer):
         }
         return out
 
+
 class SoftShrink(Primitive):
     r"""
     Applies the soft shrinkage function elementwise.