add optimizer:dpsgd,test=develop (#19915)
jhjiangcs
5 years ago
committed by
zhang wenhui
parent
37f76407b0
commit
766bd529d1
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/operators/optimizers/dpsgd_op.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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class DpsgdOp : 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_EQ(ctx->HasInput("Param"), true,
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"Input(Param) of DpsgdOp should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasInput("Grad"), true,
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"Input(Grad) of DpsgdOp should not be null.");
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PADDLE_ENFORCE_EQ(ctx->HasInput("LearningRate"), true,
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"Input(LearningRate) of DpsgdOp should not be null.");
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PADDLE_ENFORCE_EQ(
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ctx->GetInputsVarType("Param").front(),
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framework::proto::VarType::LOD_TENSOR,
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"The input var's type should be LoDTensor, but the received is %s",
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ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
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PADDLE_ENFORCE_EQ(
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ctx->GetInputsVarType("Grad").front(),
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framework::proto::VarType::LOD_TENSOR,
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"The input var's type should be LoDTensor, but the received is %s",
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ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
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PADDLE_ENFORCE_EQ(ctx->HasOutput("ParamOut"), true,
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"Output(ParamOut) of DpsgdOp 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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"Learning rate should have 1 dimension");
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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 DpsgdOp should have same dimension");
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ctx->SetOutputDim("ParamOut", param_dims);
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(ctx.Input<Tensor>("Param")->type(),
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ctx.GetPlace());
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}
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};
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class DpsgdOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("Param", "(Tensor) Input parameter");
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AddInput("Grad", "(Tensor) Input gradient");
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AddInput("LearningRate", "(Tensor) Learning rate");
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AddOutput("ParamOut", "(Tensor) Output parameter");
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AddAttr<float>("clip",
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"(float, default 0.9) "
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"Exponential decay rate for the "
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"1st moment estimates.")
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.SetDefault(10.0f);
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AddAttr<float>("batch_size",
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"(float, default 0.999) "
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"exponential decay rate for the weighted "
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"infinity norm estimates.")
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.SetDefault(16.0f);
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AddAttr<float>("sigma",
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"(float, default 1.0e-8) "
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"Constant for numerical stability")
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.SetDefault(1.0f);
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AddComment(R"DOC(
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Dpsgd Optimizer.
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We implement the Dpsgd optimizer according to CCS16 paper -
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Deep Learning with Differential Privacy.
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Dpsgd updates:
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CCS16 - Deep Learning with Differential Privacy.
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[https://arxiv.org/abs/1607.00133]
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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(dpsgd, ops::DpsgdOp, ops::DpsgdOpMaker);
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REGISTER_OP_CPU_KERNEL(
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dpsgd, ops::DpsgdOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::DpsgdOpKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,114 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 <math.h>
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#include <stdlib.h>
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#include <iostream>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class DpsgdOpKernel : 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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const auto *param_var = ctx.InputVar("Param");
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PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(), true,
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"The Var(%s)'s type should be LoDTensor, "
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"but the received is %s",
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ctx.Inputs("Param").front(),
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framework::ToTypeName(param_var->Type()));
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const auto *grad_var = ctx.InputVar("Grad");
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PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(), true,
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"The Var(%s)'s type should be LoDTensor, "
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"but the received is %s",
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ctx.Inputs("Grad").front(),
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framework::ToTypeName(grad_var->Type()));
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const auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
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const auto *param = ctx.Input<framework::Tensor>("Param");
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const auto *grad = ctx.Input<framework::Tensor>("Grad");
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auto *param_out = ctx.Output<framework::Tensor>("ParamOut");
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auto sz = param_out->numel();
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PADDLE_ENFORCE_EQ(param->numel(), sz);
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PADDLE_ENFORCE_EQ(grad->numel(), sz);
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const T *lr = learning_rate->data<T>();
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const T *param_data = param->data<T>();
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const T *grad_data = grad->data<T>();
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T *out_data = param_out->mutable_data<T>(ctx.GetPlace());
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T clip = static_cast<T>(ctx.Attr<float>("clip"));
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T batch_size = static_cast<T>(ctx.Attr<float>("batch_size"));
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T sigma = static_cast<T>(ctx.Attr<float>("sigma"));
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// compute clipping
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float l2_norm = 0.0;
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for (int64_t i = 0; i < grad->numel(); ++i) {
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l2_norm = l2_norm + grad_data[i] * grad_data[i];
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}
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l2_norm = std::sqrt(l2_norm);
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float scale = 1.0;
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if (l2_norm > clip) {
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scale = l2_norm / clip;
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}
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// generate gaussian noise.
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// [https://en.wikipedia.org/wiki/Box-Muller_transform]
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float V1, V2, S;
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float X;
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float mu = 0.0;
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float U1, U2;
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unsigned seed = (unsigned int)(time(NULL));
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std::minstd_rand engine;
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engine.seed(seed);
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std::uniform_real_distribution<T> dist(0.0, 1.0);
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do {
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// srand((unsigned int)(time(NULL)));
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// U1 = (rand() * 1.0) / RAND_MAX;
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// U2 = (rand() * 1.0) / RAND_MAX;
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// U1 = rand_rr(&seed) * (1.0 / RAND_MAX);
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// U2 = rand_rr(&seed) * (1.0 / RAND_MAX);
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U1 = dist(engine);
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U2 = dist(engine);
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V1 = 2 * U1 - 1;
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V2 = 2 * U2 - 1;
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S = V1 * V1 + V2 * V2;
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} while (S >= 1 || S == 0);
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X = V1 * sqrt(-2 * log(S) / S);
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float gaussian_noise = mu + X * sigma;
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// update parameters
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for (int64_t i = 0; i < grad->numel(); ++i) {
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out_data[i] =
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param_data[i] -
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lr[0] * (grad_data[i] / scale + gaussian_noise / batch_size);
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}
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// CCS16 - Deep Learning with Differential Privacy.
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// [https://arxiv.org/abs/1607.00133]
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} // Compute
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}; // class
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,73 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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from __future__ import print_function
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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 TestDpsgdOp(OpTest):
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def setUp(self):
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'''Test Dpsgd Operator with supplied attributes
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'''
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self.op_type = "dpsgd"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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learning_rate = 0.001
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clip = 10000.0
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batch_size = 16.0
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sigma = 0.0
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'LearningRate': np.array([learning_rate]).astype("float32")
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}
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self.attrs = {'clip': clip, 'batch_size': batch_size, 'sigma': sigma}
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param_out = dpsgd_step(self.inputs, self.attrs)
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self.outputs = {'ParamOut': param_out}
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def test_check_output(self):
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self.check_output()
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def dpsgd_step(inputs, attributes):
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'''
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Simulate one step of the dpsgd optimizer
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:param inputs: dict of inputs
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:param attributes: dict of attributes
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:return tuple: tuple of output param, moment, inf_norm and
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beta1 power accumulator
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'''
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param = inputs['Param']
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grad = inputs['Grad']
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lr = inputs['LearningRate']
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clip = attributes['clip']
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batch_size = attributes['batch_size']
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sigma = attributes['sigma']
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param_out = param - lr * grad
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return param_out
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if __name__ == "__main__":
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unittest.main()
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