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102 lines
3.9 KiB
102 lines
3.9 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/decayed_adagrad_op.h"
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namespace paddle {
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namespace operators {
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class DecayedAdagradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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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 DecayedAdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(Grad) of DecayedAdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Moment"),
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"Input(Moment) of DecayedAdagradOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasInput("LearningRate"),
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"Input(LearningRate) of DecayedAdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(ParamOut) of DecayedAdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
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"Output(MomentOut) of DecayedAdagradOp should not be null.");
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auto lr_dims = ctx->GetInputDim("LearningRate");
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PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
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"LearningRate should have one element");
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auto param_dims = ctx->GetInputDim("Param");
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PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Grad"),
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"Param and Grad input of DecayedAdagradOp should have "
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"the same dimension.");
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PADDLE_ENFORCE_EQ(param_dims, ctx->GetInputDim("Moment"),
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"Param and Moment input of DecayedAdagradOp should have "
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"the same dimension.");
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ctx->SetOutputDim("ParamOut", param_dims);
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ctx->SetOutputDim("MomentOut", param_dims);
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}
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};
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class DecayedAdagradOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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DecayedAdagradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Param", "(Tensor) Input parameter");
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AddInput("Grad", "(Tensor) Input gradient");
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AddInput("Moment", "(Tensor) Second moment");
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AddInput("LearningRate", "(Tensor) Learning rate");
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AddOutput("ParamOut", "(Tensor) Output parameter");
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AddOutput("MomentOut", "(Tensor) Output second moment");
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AddAttr<float>("decay",
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"(float, default 0.95) "
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"Discounting factor for coming gradient")
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.SetDefault(0.95);
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AddAttr<float>("epsilon",
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"(float, default 1.0e-6) "
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"Constant for numerical stability")
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.SetDefault(1.0e-6f);
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AddComment(R"DOC(
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Decayed Adagrad Optimizer.
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The update is done as follows:
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$$
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moment\_out = decay * moment + (1 - decay) * grad * grad \\
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param\_out = param - \frac{learning\_rate * grad}{\sqrt{moment\_out} + epsilon}
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$$
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The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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does not have an epsilon attribute. It is added here for numerical
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stability to avoid the division by zero error.
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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(decayed_adagrad, ops::DecayedAdagradOp,
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ops::DecayedAdagradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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decayed_adagrad,
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ops::DecayedAdagradOpKernel<paddle::platform::CPUDeviceContext, float>);
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