remove pow in nn.py under fluid (#47878)

python/paddle/fluid/contrib/slim/quantization/adaround.py

@@ -19,6 +19,7 @@ import logging
 
 import paddle
 import paddle.fluid as fluid
+import paddle
 
 from ....log_helper import get_logger
 from .utils import (
@@ -76,7 +77,7 @@ class AdaRoundLoss:
             # calculate regularization term - which ensures parameter to converge to exactly zeros and ones
             # at the end of optimization
             reg_term = fluid.layers.reduce_sum(
-                -fluid.layers.pow(paddle.abs(2 * h_v - 1), factor=beta) + 1
+                -paddle.pow(paddle.abs(2 * h_v - 1), beta) + 1
             )
 
             # calculate the rounding loss

python/paddle/fluid/layers/nn.py

@@ -118,7 +118,6 @@ __all__ = [
     'relu',
     'log',
     'crop_tensor',
-    'pow',
     'prelu',
     'brelu',
     'flatten',
@@ -8032,59 +8031,6 @@ def pad2d(
     return out
 
 
-@templatedoc()
-def pow(x, factor=1.0, name=None):
-    """
-    This is Pow Activation Operator.
-
-    :math:`out = x^{factor}`
-
-    Args:
-        x(Variable): A ``Tensor`` or ``LoDTensor`` . The data type is ``float32`` or ``float64``.
-        factor(float32|Variable, optional): A scalar with type ``float32`` or a ``Tensor`` with shape [1] and type ``float32``.  The exponential factor of Pow. 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: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``.
-
-    Examples:
-
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-
-            x = fluid.data(name="x", shape=[32,32], dtype="float32")
-
-            # example 1: argument factor is float
-            y_1 = fluid.layers.pow(x, factor=2.0)
-            # y_1 is x^{2.0}
-
-            # example 2: argument factor is Variable
-            factor_tensor = fluid.layers.fill_constant([1], "float32", 3.0)
-            y_2 = fluid.layers.pow(x, factor=factor_tensor)
-            # y_2 is x^{3.0}
-    """
-    check_variable_and_dtype(
-        x, 'x', ['int32', 'int64', 'float16', 'float32', 'float64'], 'pow'
-    )
-
-    helper = LayerHelper('pow', **locals())
-    inputs = {'X': x}
-    attrs = {}
-    if isinstance(factor, Variable):
-        check_variable_and_dtype(factor, 'factor', ['float32'], 'pow')
-        factor.stop_gradient = True
-        inputs['FactorTensor'] = factor
-    else:
-        attrs['factor'] = factor
-
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    helper.append_op(
-        type='pow', inputs=inputs, outputs={'Out': out}, attrs=attrs
-    )
-    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/dygraph_to_static/ifelse_simple_func.py

@@ -60,7 +60,7 @@ def dyfunc_with_if_else2(x, col=100):
     if fluid.layers.reduce_mean(x).numpy()[0] > x.numpy()[row][col]:
         y = fluid.layers.relu(x)
     else:
-        x_pow = fluid.layers.pow(x, 2)
+        x_pow = paddle.pow(x, 2)
         y = paddle.tanh(x_pow)
     return y
 

python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py

@@ -46,7 +46,7 @@ class TestBase(IPUOpTest):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32'
         )
-        out = paddle.fluid.layers.pow(x, **self.attrs)
+        out = paddle.pow(x, **self.attrs)
         self.fetch_list = [out.name]
 
     def run_model(self, exec_mode):
@@ -85,7 +85,7 @@ class TestCase1(TestBase):
         factor = paddle.static.data(
             name=self.feed_list[1], shape=self.feed_shape[1], dtype='float32'
         )
-        out = paddle.fluid.layers.pow(x, factor=factor, **self.attrs)
+        out = paddle.pow(x, factor, **self.attrs)
         self.fetch_list = [out.name]
 
 

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

@@ -2783,8 +2783,8 @@ class TestPow_factor_tensor(TestActivation):
 
         factor_1 = 2.0
         factor_2 = fluid.layers.fill_constant([1], "float32", 3.0)
-        out_1 = fluid.layers.pow(x, factor=factor_1)
-        out_2 = fluid.layers.pow(x, factor=factor_2)
+        out_1 = paddle.pow(x, factor_1)
+        out_2 = paddle.pow(x, factor_2)
         out_4 = paddle.pow(x, factor_1, name='pow_res')
         out_6 = paddle.pow(x, factor_2)
         self.assertEqual(('pow_res' in out_4.name), True)
@@ -2800,27 +2800,6 @@ class TestPow_factor_tensor(TestActivation):
         assert np.allclose(res_2, np.power(input, 3))
         assert np.allclose(res_6, np.power(input, 3))
 
-    def test_error(self):
-        in1 = fluid.layers.data(
-            name="in1", shape=[11, 17], append_batch_size=False, dtype="int32"
-        )
-        in2 = fluid.layers.data(
-            name="in2", shape=[11, 17], append_batch_size=False, dtype="int64"
-        )
-        in3 = fluid.layers.data(
-            name="in3", shape=[11, 17], append_batch_size=False, dtype="float32"
-        )
-        in4 = fluid.layers.data(
-            name="in4", shape=[11, 17], append_batch_size=False, dtype="float64"
-        )
-
-        factor_1 = fluid.layers.fill_constant([1], "float64", 3.0)
-
-        self.assertRaises(TypeError, fluid.layers.pow, x=in1, factor=factor_1)
-        self.assertRaises(TypeError, fluid.layers.pow, x=in2, factor=factor_1)
-        self.assertRaises(TypeError, fluid.layers.pow, x=in3, factor=factor_1)
-        self.assertRaises(TypeError, fluid.layers.pow, x=in4, factor=factor_1)
-
 
 def ref_stanh(x, scale_a=0.67, scale_b=1.7159):
     out = scale_b * np.tanh(x * scale_a)

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

@@ -14,6 +14,7 @@
 
 import unittest
 import paddle.fluid as fluid
+import paddle
 
 
 class TestNameScope(unittest.TestCase):
@@ -26,7 +27,7 @@ class TestNameScope(unittest.TestCase):
             with fluid.name_scope("s3"):
                 d = c / 1
         with fluid.name_scope("s1"):
-            f = fluid.layers.pow(d, 2.0)
+            f = paddle.pow(d, 2.0)
         with fluid.name_scope("s4"):
             g = f - 1