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115 lines
4.4 KiB
115 lines
4.4 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/adadelta_op.h"
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namespace paddle {
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namespace operators {
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class AdadeltaOp : 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 AdadeltaOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(Grad) of AdadeltaOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("AvgSquaredGrad"),
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"Input(AvgSquaredGrad) of AdadeltaOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"),
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"Input(AvgSquaredUpdate) of AdadeltaOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(ParamOut) of AdadeltaOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("AvgSquaredGradOut"),
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"Output(AvgSquaredGradOut) of AdadeltaOp should not be null.");
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PADDLE_ENFORCE(
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ctx->HasOutput("AvgSquaredUpdateOut"),
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"Output(AvgSquaredUpdateOut) of AdadeltaOp should not be null.");
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auto param_dim = ctx->GetInputDim("Param");
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PADDLE_ENFORCE_EQ(
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param_dim, ctx->GetInputDim("Grad"),
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"param and grad input of AdadeltaOp should have same dimension");
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PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredGrad"),
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"Param and AvgSquaredGrad input of AdadeltaOp "
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"should have same dimension");
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PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredUpdate"),
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"Param and AvgSquaredUpdate input of AdadeltaOp "
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"should have same dimension");
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ctx->SetOutputDim("ParamOut", param_dim);
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ctx->SetOutputDim("AvgSquaredGradOut", param_dim);
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ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim);
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}
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};
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class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AdadeltaOpMaker(framework::OpProto *proto,
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framework::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("AvgSquaredGrad",
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"(Tensor) Input expectation of squared gradient");
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AddInput("AvgSquaredUpdate",
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"(Tensor) Input expectation of squared parameter updates");
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AddOutput("ParamOut", "(Tensor) Output parameter");
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AddOutput("AvgSquaredGradOut",
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"(Tensor) Output expectation of squared gradient");
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AddOutput("AvgSquaredUpdateOut",
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"(Tensor) Output expectation of squared parameter updates");
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AddAttr<float>("rho",
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"(float, default 0.95) Exponential decay rate "
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"for squared gradients.")
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.SetDefault(0.95f);
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AddAttr<float>("epsilon",
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"(float, default 1.0e-6) Constant for "
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"numerical stability")
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.SetDefault(1.0e-6f);
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AddComment(R"DOC(
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Adadelta Updates Operator.
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This implements the Adadelta optimizer[1]. Adadelta is a per-dimension
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adaptive learning rate method for gradient descent.
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Adadelta updates:
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avg_squared_grad_out = rho * avg_squared_grad + (1 - rho) * grad * grad
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param_update = - sqrt((avg_squared_update + epsilon) /
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(avg_squared_grad_out + epsilon)) * grad
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avg_squared_update_out = rho * avg_squared_update + (1 - rho) * param_update**2
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param_out = param + param_update
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References:
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[1] ADADELTA: An Adaptive Learning Rate Method
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https://arxiv.org/abs/1212.5701
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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(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
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REGISTER_OP_CPU_KERNEL(
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adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>);
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