(fluid清理)remove swish in nn.py under fluid (#47891)

* remove swish in nn.py under fluid

* fix tswish test case

python/paddle/fluid/layers/nn.py

@@ -134,7 +134,6 @@ __all__ = [
     'relu6',
     'pow',
     'hard_sigmoid',
-    'swish',
     'prelu',
     'brelu',
     'leaky_relu',
@@ -9979,92 +9978,6 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
     return out
 
 
-@templatedoc()
-def swish(x, beta=1.0, name=None):
-    r"""
-    :alias_main: paddle.nn.functional.swish
-        :alias: paddle.nn.functional.swish,paddle.nn.functional.activation.swish
-        :old_api: paddle.fluid.layers.swish
-
-    Elementwise swish activation function. See `Searching for Activation Functions <https://arxiv.org/abs/1710.05941>`_ for more details.
-
-    Equation:
-
-    .. math::
-        out = \\frac{x}{1 + e^{- beta * x}}
-
-    Args:
-        x(Variable): Tensor or LoDTensor, dtype: float32 or float64, the input of swish activation.
-
-        beta(float): Constant beta of swish operator, default 1.0.
-
-        name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
-
-    Returns:
-
-        Variable: Output of the swish activation, Tensor or LoDTensor, with the same dtype and shape with the input x.
-
-    Examples:
-
-        .. code-block:: python
-
-            # declarative mode
-            import numpy as np
-            from paddle import fluid
-
-            x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
-            y = fluid.layers.swish(x, beta=2.0)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            start = fluid.default_startup_program()
-            main = fluid.default_main_program()
-
-            data = np.random.randn(2, 3).astype("float32")
-            exe.run(start)
-            y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
-
-            data
-            # array([[-1.1239197 ,  1.3391294 ,  0.03921051],
-            #        [ 1.1970421 ,  0.02440812,  1.2055548 ]], dtype=float32)
-            y_np
-            # array([[-0.2756806 ,  1.0610548 ,  0.01998957],
-            #        [ 0.9193261 ,  0.01235299,  0.9276883 ]], dtype=float32)
-
-
-        .. code-block:: python
-
-            # imperative mode
-            import numpy as np
-            from paddle import fluid
-            import paddle.fluid.dygraph as dg
-
-            data = np.random.randn(2, 3).astype("float32")
-            place = fluid.CPUPlace()
-            with dg.guard(place) as g:
-                x = dg.to_variable(data)
-                y = fluid.layers.swish(x)
-                y_np = y.numpy()
-            data
-            # array([[-0.0816701 ,  1.1603649 , -0.88325626],
-            #        [ 0.7522361 ,  1.0978601 ,  0.12987892]], dtype=float32)
-            y_np
-            # array([[-0.03916847,  0.8835007 , -0.25835553],
-            #        [ 0.51126915,  0.82324016,  0.06915068]], dtype=float32)
-    """
-    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'swish')
-
-    helper = LayerHelper('swish', **locals())
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    helper.append_op(
-        type='swish',
-        inputs={'X': x},
-        outputs={'Out': out},
-        attrs={'slope': beta},
-    )
-    return out
-
-
 @deprecated(since="2.0.0", update_to="paddle.static.nn.prelu")
 def prelu(x, mode, param_attr=None, data_format="NCHW", name=None):
     r"""

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_elt_act_fuse_pass.py

@@ -92,8 +92,7 @@ class ElementwiseActivationMkldnnFusePassTest_Add_Swish(
 ):
     def set_params(self):
         self.operand = fluid.layers.elementwise_add
-        self.act_alpha = 4
-        self.act = fluid.layers.swish
+        self.act = paddle.nn.functional.swish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Add_HardSwish(
@@ -194,7 +193,7 @@ class ElementwiseActivationMkldnnFusePassTest_Sub_Swish(
 ):
     def set_params(self):
         self.operand = fluid.layers.elementwise_sub
-        self.act = fluid.layers.swish
+        self.act = paddle.nn.functional.swish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Sub_HardSwish(
@@ -287,7 +286,7 @@ class ElementwiseActivationMkldnnFusePassTest_Mul_Swish(
 ):
     def set_params(self):
         self.operand = fluid.layers.elementwise_mul
-        self.act = fluid.layers.swish
+        self.act = paddle.nn.functional.swish
 
 
 class ElementwiseActivationMkldnnFusePassTest_Mul_HardSwish(

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

@@ -21,6 +21,7 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import PassVersionChecker
 from paddle.fluid.core import AnalysisConfig
+import paddle
 
 
 class TensorRTSubgraphPassActivationTest(InferencePassTest):
@@ -118,7 +119,7 @@ class TensorRTSubgraphPassSwishTest(TensorRTSubgraphPassActivationTest):
         )
 
     def append_act(self, x):
-        return fluid.layers.swish(x)
+        return paddle.nn.functional.swish(x)
 
 
 class TensorRTSubgraphPassSwishFp16SerializeTest(
@@ -131,7 +132,7 @@ class TensorRTSubgraphPassSwishFp16SerializeTest(
         )
 
     def append_act(self, x):
-        return fluid.layers.swish(x)
+        return paddle.nn.functional.swish(x)
 
 
 class TensorRTSubgraphPassDynamicSwishFp16SerializeTest(
@@ -152,7 +153,7 @@ class TensorRTSubgraphPassDynamicSwishFp16SerializeTest(
         )
 
     def append_act(self, x):
-        return fluid.layers.swish(x)
+        return paddle.nn.functional.swish(x)
 
 
 class TensorRTSubgraphPassMishTest(TensorRTSubgraphPassActivationTest):

python/paddle/fluid/tests/unittests/test_activation_op.py

@@ -3551,7 +3551,7 @@ class TestSwishAPI(unittest.TestCase):
         paddle.enable_static()
         with fluid.program_guard(fluid.Program()):
             x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
-            out = fluid.layers.swish(x)
+            out = paddle.nn.functional.swish(x)
             exe = fluid.Executor(self.place)
             res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
         out_ref = ref_swish(self.x_np)