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73 lines
2.7 KiB
73 lines
2.7 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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class LarsMomentumOpKernel : 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<framework::LoDTensor>("ParamOut");
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auto velocity_out = ctx.Output<framework::LoDTensor>("VelocityOut");
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auto param = ctx.Input<framework::LoDTensor>("Param");
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auto velocity = ctx.Input<framework::LoDTensor>("Velocity");
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auto learning_rate = ctx.Input<framework::LoDTensor>("LearningRate");
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auto* grad_var = ctx.InputVar("Grad");
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// only support dense for now.
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PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>());
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auto grad = ctx.Input<framework::LoDTensor>("Grad");
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param_out->mutable_data<T>(ctx.GetPlace());
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velocity_out->mutable_data<T>(ctx.GetPlace());
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T mu = static_cast<T>(ctx.Attr<float>("mu"));
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T lars_coeff = ctx.Attr<float>("lars_coeff");
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T lars_weight_decay = ctx.Attr<float>("lars_weight_decay");
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auto p_out = framework::EigenVector<T>::Flatten(*param_out);
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auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
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auto p = framework::EigenVector<T>::Flatten(*param);
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auto v = framework::EigenVector<T>::Flatten(*velocity);
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auto g = framework::EigenVector<T>::Flatten(*grad);
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auto* lr = learning_rate->data<T>();
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framework::Tensor p_norm_t, g_norm_t;
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p_norm_t.Resize({1});
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g_norm_t.Resize({1});
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p_norm_t.mutable_data<T>(ctx.GetPlace());
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g_norm_t.mutable_data<T>(ctx.GetPlace());
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auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
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auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
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ep_norm = p.square().sum().sqrt();
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eg_norm = g.square().sum().sqrt();
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T local_lr = lr[0];
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if (ep_norm(0) > 0 && eg_norm(0) > 0) {
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local_lr = lr[0] * lars_coeff * ep_norm(0) /
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(eg_norm(0) + lars_weight_decay * ep_norm(0));
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}
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v_out = v * mu + local_lr * (g + lars_weight_decay * p);
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p_out = p - v_out;
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}
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};
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} // namespace operators
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} // namespace paddle
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