Merge branch 'develop' of https://github.com/baidu/Paddle into inference
hedaoyuan
8 years ago
commit
71261be901
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#define EIGEN_USE_GPU
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#include "paddle/operators/adam_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(adam,
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ops::AdamOpKernel<paddle::platform::GPUPlace, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class AdamOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
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auto moment1_out_tensor = ctx.Output<framework::Tensor>("Moment1Out");
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auto moment2_out_tensor = ctx.Output<framework::Tensor>("Moment2Out");
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auto beta1_pow_out_tensor = ctx.Output<framework::Tensor>("Beta1PowOut");
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auto beta2_pow_out_tensor = ctx.Output<framework::Tensor>("Beta2PowOut");
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param_out_tensor->mutable_data<T>(ctx.GetPlace());
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moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
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moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
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beta1_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
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beta2_pow_out_tensor->mutable_data<T>(ctx.GetPlace());
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float beta1 = ctx.Attr<float>("beta1");
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float beta2 = ctx.Attr<float>("beta2");
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float epsilon = ctx.Attr<float>("epsilon");
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auto param = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Param"));
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auto grad = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Grad"));
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auto moment1 = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Moment1"));
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auto moment2 = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Moment2"));
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auto lr = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("LearningRate"));
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auto beta1_pow = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Beta1Pow"));
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auto beta2_pow = framework::EigenVector<T>::Flatten(
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*ctx.Input<framework::Tensor>("Beta2Pow"));
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auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
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auto moment1_out = framework::EigenVector<T>::Flatten(*moment1_out_tensor);
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auto moment2_out = framework::EigenVector<T>::Flatten(*moment2_out_tensor);
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auto beta1_pow_out =
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framework::EigenVector<T>::Flatten(*beta1_pow_out_tensor);
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auto beta2_pow_out =
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framework::EigenVector<T>::Flatten(*beta2_pow_out_tensor);
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auto place = ctx.GetEigenDevice<Place>();
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moment1_out.device(place) = beta1 * moment1 + (1 - beta1) * grad;
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moment2_out.device(place) = beta2 * moment2 + (1 - beta2) * grad.square();
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beta1_pow_out.device(place) = beta1_pow * beta1;
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beta2_pow_out.device(place) = beta2_pow * beta2;
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// All of these are tensors of 1 element
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auto lr_t = lr * (1 - beta2_pow_out).sqrt() / (1 - beta1_pow_out);
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// Eigen does not support automatic broadcast
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// Get dimensions of moment vector to broadcast lr_t
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Eigen::DSizes<int, 1> m_dsize(moment1_out_tensor->numel());
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param_out.device(place) =
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param -
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lr_t.broadcast(m_dsize) *
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(moment1_out / (moment2_out.sqrt() + epsilon));
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}
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};
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} // namespace operators
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} // namespace paddle
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestAdamOp1(OpTest):
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def setUp(self):
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'''Test Adam Op with supplied attributes
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'''
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self.op_type = "adam"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The second moment is positive
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moment2 = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.004
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beta1 = 0.78
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beta2 = 0.836
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epsilon = 1e-4
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beta1_pow = beta1**10
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beta2_pow = beta2**10
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment1': moment1,
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'Moment2': moment2,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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'Beta2Pow': np.array([beta2_pow]).astype("float32")
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}
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self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
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param_out, moment1_out, moment2_out, beta1_pow_out, \
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beta2_pow_out = adam_step(self.inputs, self.attrs)
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self.outputs = {
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'Moment1Out': moment1_out,
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'Moment2Out': moment2_out,
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'Beta1PowOut': beta1_pow_out,
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'Beta2PowOut': beta2_pow_out,
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'ParamOut': param_out
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}
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def test_check_output(self):
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self.check_output()
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class TestAdamOp2(OpTest):
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def setUp(self):
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'''Test Adam Op with supplied attributes
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'''
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self.op_type = "adam"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The second moment is positive
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moment2 = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.001
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beta1 = 0.9
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beta2 = 0.999
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epsilon = 1e-8
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beta1_pow = beta1**10
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beta2_pow = beta2**10
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment1': moment1,
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'Moment2': moment2,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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'Beta2Pow': np.array([beta2_pow]).astype("float32")
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}
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attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
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param_out, moment1_out, moment2_out, beta1_pow_out, \
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beta2_pow_out = adam_step(self.inputs, attributes)
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self.outputs = {
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'Moment1Out': moment1_out,
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'Moment2Out': moment2_out,
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'Beta1PowOut': beta1_pow_out,
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'Beta2PowOut': beta2_pow_out,
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'ParamOut': param_out
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}
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def test_check_output(self):
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self.check_output()
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class TestAdamOpMultipleSteps(OpTest):
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def setUp(self):
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'''Test Adam Operator with supplied attributes
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'''
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self.op_type = "adam"
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self.num_steps = 10
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The second moment is positive
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moment2 = np.random.random((102, 105)).astype("float32")
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learning_rate = 0.001
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beta1 = 0.9
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beta2 = 0.999
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epsilon = 1e-8
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beta1_pow = beta1**10
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beta2_pow = beta2**10
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'Moment1': moment1,
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'Moment2': moment2,
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'LearningRate': np.array([learning_rate]).astype("float32"),
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'Beta1Pow': np.array([beta1_pow]).astype("float32"),
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'Beta2Pow': np.array([beta2_pow]).astype("float32")
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}
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self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
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def test_check_output(self):
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for _ in range(self.num_steps):
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param_out, moment1_out, moment2_out, beta1_pow_out, \
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beta2_pow_out = adam_step(self.inputs, self.attrs)
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self.outputs = {
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'Moment1Out': moment1_out,
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'Moment2Out': moment2_out,
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'Beta1PowOut': beta1_pow_out,
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'Beta2PowOut': beta2_pow_out,
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'ParamOut': param_out
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}
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# Verify output for this step
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self.check_output()
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# Output of this step becomes input for next step
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self.inputs['Param'] = param_out
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self.inputs['Moment1'] = moment1_out
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self.inputs['Moment2'] = moment2_out
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self.inputs['Beta1Pow'] = beta1_pow_out
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self.inputs['Beta2Pow'] = beta2_pow_out
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# Randomize gradient for next step
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self.inputs['Grad'] = np.random.uniform(
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-1, 1, (102, 105)).astype("float32")
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def adam_step(inputs, attributes):
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'''
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Simulate one step of the adam optimizer
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:param inputs: dict of inputs
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:param attributes: dict of attributes
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:return tuple: tuple of output param, moment1, moment2,
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beta1 power accumulator and beta2 power accumulator
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'''
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param = inputs['Param']
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grad = inputs['Grad']
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moment1 = inputs['Moment1']
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moment2 = inputs['Moment2']
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lr = inputs['LearningRate']
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beta1_pow = inputs['Beta1Pow']
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beta2_pow = inputs['Beta2Pow']
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beta1 = attributes['beta1']
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beta2 = attributes['beta2']
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epsilon = attributes['epsilon']
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moment1_out = beta1 * moment1 + (1 - beta1) * grad
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moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad)
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beta1_pow_out = beta1_pow * beta1
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beta2_pow_out = beta2_pow * beta2
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lr_t = lr * np.sqrt(1 - beta2_pow_out) / (1 - beta1_pow_out)
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param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon))
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return param_out, moment1_out, moment2_out, beta1_pow_out, beta2_pow_out
|
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|
|
||||||
|
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||||||
|
if __name__ == "__main__":
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unittest.main()
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@ -0,0 +1,76 @@
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import unittest
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from paddle.v2.framework.framework import Variable, g_program
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import paddle.v2.framework.core as core
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|
|
||||||
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||||||
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class TestOperator(unittest.TestCase):
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||||||
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def test_error_type(self):
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||||||
|
block = g_program.create_block()
|
||||||
|
try:
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||||||
|
block.append_op()
|
||||||
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self.assertFail()
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||||||
|
except ValueError as v_err:
|
||||||
|
self.assertEqual(
|
||||||
|
v_err.message,
|
||||||
|
"`type` to initilized an Operator can not be None.")
|
||||||
|
try:
|
||||||
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block.append_op(type="no_such_op")
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||||||
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self.assertFail()
|
||||||
|
except AssertionError as a_err:
|
||||||
|
self.assertEqual(a_err.message,
|
||||||
|
"Operator \"no_such_op\" has not been registered.")
|
||||||
|
|
||||||
|
def test_op_desc_creation(self):
|
||||||
|
block = g_program.current_block()
|
||||||
|
mul_x = block.create_var(
|
||||||
|
dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
|
||||||
|
mul_y = block.create_var(
|
||||||
|
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
|
||||||
|
mul_out = block.create_var(
|
||||||
|
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
|
||||||
|
mul_op = block.append_op(
|
||||||
|
type="mul",
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||||||
|
inputs={"X": [mul_x],
|
||||||
|
"Y": mul_y},
|
||||||
|
outputs={"Out": [mul_out]},
|
||||||
|
attrs={"x_num_col_dims": 1})
|
||||||
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self.assertEqual(mul_op.type, "mul")
|
||||||
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self.assertEqual(mul_op.input_names, ["X", "Y"])
|
||||||
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self.assertEqual(mul_op.input("X"), ["mul.x"])
|
||||||
|
self.assertEqual(mul_op.input("Y"), ["mul.y"])
|
||||||
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self.assertEqual(mul_op.output_names, ["Out"])
|
||||||
|
self.assertEqual(mul_op.output("Out"), ["mul.out"])
|
||||||
|
self.assertEqual(
|
||||||
|
set(mul_op.attr_names), set(["x_num_col_dims", "y_num_col_dims"]))
|
||||||
|
self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
|
||||||
|
self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
|
||||||
|
self.assertEqual(mul_op.attr("x_num_col_dims"), 1)
|
||||||
|
self.assertEqual(mul_op.has_attr("y_num_col_dims"), True)
|
||||||
|
self.assertEqual(mul_op.attr_type("y_num_col_dims"), core.AttrType.INT)
|
||||||
|
self.assertEqual(mul_op.attr("y_num_col_dims"), 1)
|
||||||
|
self.assertEqual(mul_out.op, mul_op)
|
||||||
|
|
||||||
|
def test_mult_input(self):
|
||||||
|
block = g_program.current_block()
|
||||||
|
sum_x1 = block.create_var(
|
||||||
|
dtype="int", shape=[3, 4], lod_level=0, name="sum.x1")
|
||||||
|
sum_x2 = block.create_var(
|
||||||
|
dtype="int", shape=[3, 4], lod_level=0, name="sum.x2")
|
||||||
|
sum_x3 = block.create_var(
|
||||||
|
dtype="int", shape=[3, 4], lod_level=0, name="sum.x3")
|
||||||
|
sum_out = block.create_var(
|
||||||
|
dtype="int", shape=[3, 4], lod_level=0, name="sum.out")
|
||||||
|
sum_op = block.append_op(
|
||||||
|
type="sum",
|
||||||
|
inputs={"X": [sum_x1, sum_x2, sum_x3]},
|
||||||
|
outputs={"Out": sum_out})
|
||||||
|
self.assertEqual(sum_op.type, "sum")
|
||||||
|
self.assertEqual(sum_op.input_names, ["X"])
|
||||||
|
self.assertEqual(sum_op.input("X"), ["sum.x1", "sum.x2", "sum.x3"])
|
||||||
|
self.assertEqual(sum_op.output_names, ["Out"])
|
||||||
|
self.assertEqual(sum_op.output("Out"), ["sum.out"])
|
||||||
|
self.assertEqual(sum_out.op, sum_op)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
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Reference in new issue