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/* 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/rmsprop_op.h"
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namespace paddle {
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namespace operators {
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class RmspropOp : 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::InferShapeContextBase *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Param"),
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"Input(param) of RmspropOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(grad) of RmspropOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Moment"),
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"Input(moment) of RmspropOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(param_out) of RmspropOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
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"Output(moment_out) of RmspropOp 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 RmspropOp should have the same dimension.");
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PADDLE_ENFORCE_EQ(
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param_dim, ctx->GetInputDim("Moment"),
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"Param and moment input of RmspropOp should have the same dimension.");
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ctx->SetOutputDim("ParamOut", param_dim);
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ctx->SetOutputDim("MomentOut", param_dim);
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}
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};
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class RmspropOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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RmspropOpMaker(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", "Input parameter");
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AddInput("Grad", "Input gradient");
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AddInput("Moment", "Second moment");
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AddOutput("ParamOut", "Output parameter");
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AddOutput("MomentOut", "Output second moment");
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AddAttr<float>("learningRate", "Learning rate");
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AddAttr<float>("epsilon", "Constant for numerical stability");
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AddAttr<float>("decayRate", "Decay rate for moving average of gradients");
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AddComment(R"DOC(
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RMSprop
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MomentOut = decayRate * Moment + (1 - decayRate) * Grad * Grad
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ParamOut = Param - learningRate * Grad / (sqrt(MomentOut) + epsilon)
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The original slide(Slide 29 of
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http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
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does not have the epsilon attribute. It is added here for numerical stability
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to avoid division by zero.
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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(rmsprop, ops::RmspropOp, ops::RmspropOpMaker);
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REGISTER_OP_CPU_KERNEL(rmsprop,
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ops::RmspropOpKernel<paddle::platform::CPUPlace, float>);
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/* 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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#define EIGEN_USE_GPU
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#include "paddle/operators/rmsprop_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(rmsprop,
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ops::RmspropOpKernel<paddle::platform::GPUPlace, float>);
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/* 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 Place, typename T>
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class RmspropOpKernel : 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 moment_out = ctx.Output<Tensor>("MomentOut");
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param_out->mutable_data<T>(ctx.GetPlace());
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moment_out->mutable_data<T>(ctx.GetPlace());
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float lr = ctx.Attr<float>("learningRate");
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float epsilon = ctx.Attr<float>("epsilon");
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float decay = ctx.Attr<float>("decayRate");
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auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
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auto g = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Grad"));
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auto m = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Moment"));
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auto p_out = EigenVector<T>::Flatten(*param_out);
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auto m_out = EigenVector<T>::Flatten(*moment_out);
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auto place = ctx.GetEigenDevice<Place>();
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m_out.device(place) = decay * m + (1 - decay) * g * g;
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p_out.device(place) = p - lr * g / (m_out.sqrt() + epsilon);
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}
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};
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} // namespace operators
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} // namespace paddle
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestRmspropOp(OpTest):
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def setUp(self):
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self.op_type = "rmsprop"
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param = np.random.random((123, 321)).astype("float32")
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grad = np.random.random((123, 321)).astype("float32")
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moment = np.zeros((123, 321)).astype("float32")
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learning_rate = 0.01
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epsilon = 1e-6
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decay_rate = 0.9
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self.inputs = {'Param': param, 'Grad': grad, 'Moment': moment}
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self.attrs = {
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'learningRate': learning_rate,
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'epsilon': epsilon,
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'decayRate': decay_rate
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}
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moment_out = decay_rate * moment + (1 - decay_rate) * grad * grad
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param_out = param - learning_rate * grad / (np.sqrt(moment_out) +
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epsilon)
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self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out}
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def test_check_output(self):
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self.check_output()
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if __name__ == "__main__":
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unittest.main()
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