Enhance fused_elementwise_activation op and add python api in contrib.layers (#17236)
* Enhance fused_elementwise_activation op. test=develop * Move the api fused_elementwise_activation to contrib. test=develop * Add including files. test=develop * Add the support of sigmoid in fused_elementwise_activetion op. * Update API.spec. test=developdependabot/pip/python/requests-2.20.0
Yiqun Liu
6 years ago
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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from . import nn
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from .nn import *
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__all__ = []
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__all__ += nn.__all__
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Contrib layers just related to the neural network.
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"""
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from __future__ import print_function
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import numpy as np
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import six
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import os
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import inspect
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from paddle.fluid.layer_helper import LayerHelper
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__all__ = ['fused_elemwise_activation', ]
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def fused_elemwise_activation(x,
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y,
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functor_list,
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axis=-1,
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scale=0.0,
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save_intermediate_out=True):
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"""
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**Fused elementwise_add/mul and activation layers**
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This function computes an elementwise_add/mul cooperated with an activation.
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.. math::
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out = Unary(Binary(x, y))
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or
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.. math::
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out = Binary(x, Unary(y))
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Unary operators can be: `scale`, `relu`, `tanh`. Binary operators can be:
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`elementwise_add`, `elementwise_mul`.
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Args:
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x (Variable): left operation of the binary operator.
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y (Variable): right operator of the binary operator.
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functor_list (list of str): types of operator which will be executed
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by this layer. For example, ['elementwise_add', 'relu']
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(out = elementwise_add(x, relu(y))),
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or ['relu', 'elemmentwise_add'] (out = relu(elementwise_add(x, y))).
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axis (int32, default -1): axis of elementwise op.
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scale (float32, default 0): parameter of scale op.
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save_intermediate_out (bool, default True): whether to save the
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intermediate result, Unary(y) or Binary(x, y).
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Returns:
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Variable: The computation result.
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"""
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if isinstance(functor_list, str):
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functor_list = functor_list.split(',')
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if not isinstance(functor_list, list) or len(functor_list) != 2:
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raise ValueError(
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'functor_list should be a list of str, and the length should be 2.')
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helper = LayerHelper('fused_elemwise_activation', **locals())
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out = helper.create_variable_for_type_inference(dtype=x.dtype)
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intermediate_out = helper.create_variable_for_type_inference(dtype=x.dtype)
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helper.append_op(
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type='fused_elemwise_activation',
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inputs={'X': x,
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'Y': y},
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outputs={'Out': out,
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'IntermediateOut': intermediate_out},
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attrs={
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'axis': axis,
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'scale': scale,
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'save_intermediate_out': save_intermediate_out,
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'functor_list': functor_list
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})
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return out
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