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98 lines
3.4 KiB
98 lines
3.4 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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#include "paddle/operators/proximal_gd_op.h"
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namespace paddle {
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namespace operators {
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class ProximalGDOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Param"),
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"Input(Param) of ProximalGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(Grad) of ProximalGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
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"Input(LearningRate) of ProximalGDOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(ParamOut) of ProximalGDOp should not be null.");
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auto param_dim = ctx->GetInputDim("Param");
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PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"),
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"Two input of ProximalGD Op's dimension must be same.");
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auto lr_dim = ctx->GetInputDim("LearningRate");
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PADDLE_ENFORCE_EQ(framework::product(lr_dim), 1,
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"Learning Rate should be a scalar.");
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ctx->SetOutputDim("ParamOut", param_dim);
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}
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};
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class ProximalGDOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ProximalGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Param",
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"(Tensor, default Tensor<float>) "
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"Input parameter value that has to be updated.");
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AddInput("Grad",
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"(Tensor, default Tensor<float>) "
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"Input gradient of the parameter.");
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AddInput("LearningRate",
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"(Tensor, default Tensor<float>) "
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"The learning rate should be a tensor of size 1.");
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AddOutput("ParamOut", "(Tensor) Output updated parameter value.");
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AddAttr<float>("l1",
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"(float, default 0.0) "
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"L1 regularization strength.")
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.SetDefault(0.0f);
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AddAttr<float>("l2",
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"(float, default 0.0) "
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"L2 regularization strength.")
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.SetDefault(0.0f);
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AddComment(R"DOC(
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ProximalGD Operator.
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Optimizer that implements the proximal gradient descent algorithm:
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$$
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prox\_param = param - learning\_rate * grad \\
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param = sign(prox\_param) / (1 + learning\_rate * l2) *
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\max(|prox\_param| - learning\_rate * l1, 0)
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$$
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The paper that proposed Proximal Gradient Descent:
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(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(proximal_gd, ops::ProximalGDOp,
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ops::ProximalGDOpMaker);
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REGISTER_OP_CPU_KERNEL(
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proximal_gd,
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ops::ProximalGDOpKernel<paddle::platform::CPUDeviceContext, float>);
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