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146 lines
5.4 KiB
146 lines
5.4 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/adagrad_op.h"
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#include <cmath>
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/math/selected_rows_functor.h"
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namespace paddle {
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namespace operators {
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class AdagradOp : 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 AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Grad"),
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"Input(Grad) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Moment"),
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"Input(Moment) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
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"Input(LearningRate) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
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"Output(ParamOut) of AdagradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("MomentOut"),
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"Output(MomentOut) of AdagradOp 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(
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param_dims, ctx->GetInputDim("Grad"),
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"Param and Grad input of AdagradOp should have the same dimension.");
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PADDLE_ENFORCE_EQ(
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param_dims, ctx->GetInputDim("Moment"),
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"Param and Moment input of AdagradOp should have 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 AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AdagradOpMaker(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>("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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Adaptive Gradient Algorithm (Adagrad).
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The update is done as follows:
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$$moment\_out = moment + grad * grad \\
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param\_out = param - \frac{learning\_rate * grad}{\sqrt{moment\_out} + \epsilon}
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$$
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The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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does not have the epsilon attribute. It is added here in our implementation
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as also proposed here: http://cs231n.github.io/neural-networks-3/#ada
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for numerical 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 {
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size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
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return std::find(rows.begin(), rows.end(), value) - rows.begin();
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}
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} // namespace
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template <typename T>
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struct SparseAdagradFunctor<platform::CPUDeviceContext, T> {
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void operator()(const platform::CPUDeviceContext& context,
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const framework::SelectedRows& grad,
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const framework::Tensor& learning_rate, T epsilon,
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framework::Tensor* moment, framework::Tensor* param) {
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// 1. g_m.rows = set(g.rows)
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auto grad_width = grad.value().dims()[1];
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math::scatter::MergeAdd<platform::CPUDeviceContext, T> merge_func;
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auto grad_merge = merge_func(context, grad);
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auto& merge_rows = grad_merge.rows();
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auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
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// 2. m += g_m * g_m
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math::scatter::Mul<platform::CPUDeviceContext, T> sqare_func;
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auto grad_square = sqare_func(context, grad_merge, grad_merge);
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math::SelectedRowsAddToTensor<platform::CPUDeviceContext, T> functor;
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functor(context, grad_square, moment);
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// 3. update parameter
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auto* lr = learning_rate.data<T>();
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auto* param_data = param->data<T>();
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auto* moment_data = moment->data<T>();
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for (size_t i = 0; i < merge_rows.size(); i++) {
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for (int64_t j = 0; j < grad_width; j++) {
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param_data[merge_rows[i] * grad_width + j] -=
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lr[0] * grad_merge_data[i * grad_width + j] /
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(std::sqrt(moment_data[merge_rows[i] * grad_width + j]) + epsilon);
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}
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}
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}
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};
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template struct SparseAdagradFunctor<platform::CPUDeviceContext, float>;
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template struct SparseAdagradFunctor<platform::CPUDeviceContext, double>;
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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(adagrad, ops::AdagradOp, ops::AdagradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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adagrad, ops::AdagradOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::AdagradOpKernel<paddle::platform::CPUDeviceContext, double>);
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