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97 lines
3.6 KiB
97 lines
3.6 KiB
/* 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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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T>
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class FTRLOpKernel : 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 = ctx.Output<Tensor>("ParamOut");
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auto* sq_accum_out = ctx.Output<Tensor>("SquaredAccumOut");
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auto* lin_accum_out = ctx.Output<Tensor>("LinearAccumOut");
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param_out->mutable_data<T>(ctx.GetPlace());
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sq_accum_out->mutable_data<T>(ctx.GetPlace());
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lin_accum_out->mutable_data<T>(ctx.GetPlace());
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auto grad = ctx.Input<Tensor>("Grad");
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auto l1 = static_cast<T>(ctx.Attr<float>("l1"));
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auto l2 = static_cast<T>(ctx.Attr<float>("l2"));
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auto lr_power = static_cast<T>(ctx.Attr<float>("lr_power"));
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auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
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auto sq_accum =
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EigenVector<T>::Flatten(*ctx.Input<Tensor>("SquaredAccumulator"));
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auto lin_accum =
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EigenVector<T>::Flatten(*ctx.Input<Tensor>("LinearAccumulator"));
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auto g = EigenVector<T>::Flatten(*grad);
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auto lr = EigenVector<T>::Flatten(*ctx.Input<Tensor>("LearningRate"));
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auto p_out = EigenVector<T>::Flatten(*param_out);
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auto s_acc_out = EigenVector<T>::Flatten(*sq_accum_out);
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auto l_acc_out = EigenVector<T>::Flatten(*lin_accum_out);
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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Eigen::DSizes<int, 1> grad_dsize(grad->numel());
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auto new_accum = sq_accum + g * g;
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// Special case for lr_power = -0.5
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if (lr_power == static_cast<T>(-0.5)) {
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l_acc_out.device(place) =
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lin_accum + g -
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((new_accum.sqrt() - sq_accum.sqrt()) / lr.broadcast(grad_dsize)) * p;
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} else {
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l_acc_out.device(place) =
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lin_accum + g -
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((new_accum.pow(-lr_power) - sq_accum.pow(-lr_power)) /
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lr.broadcast(grad_dsize)) *
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p;
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}
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auto x = (l_acc_out.constant(l1) * l_acc_out.sign() - l_acc_out);
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if (lr_power == static_cast<T>(-0.5)) {
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auto y = (new_accum.sqrt() / lr.broadcast(grad_dsize)) +
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l_acc_out.constant(static_cast<T>(2) * l2);
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auto pre_shrink = x / y;
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p_out.device(place) =
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(l_acc_out.abs() > l_acc_out.constant(l1))
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.select(pre_shrink, p.constant(static_cast<T>(0)));
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} else {
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auto y = (new_accum.pow(-lr_power) / lr.broadcast(grad_dsize)) +
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l_acc_out.constant(static_cast<T>(2) * l2);
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auto pre_shrink = x / y;
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p_out.device(place) =
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(l_acc_out.abs() > l_acc_out.constant(l1))
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.select(pre_shrink, p.constant(static_cast<T>(0)));
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}
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s_acc_out.device(place) = sq_accum + g * g;
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}
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};
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} // namespace operators
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} // namespace paddle
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