wehub-resource-sync
115 lines
3.5 KiB
C++
115 lines
3.5 KiB
C++
// Copyright (c) 2024 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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#include <math.h>
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#include <stdlib.h>
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#include <iostream>
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void DpsgdOpKernel(const Context &dev_ctx,
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const DenseTensor ¶m_in,
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const DenseTensor &grad_in,
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const DenseTensor &learning_rate_in,
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float clip_in,
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float batch_size_in,
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float sigma_in,
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int seed_in,
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DenseTensor *param_out) {
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const auto *learning_rate = &learning_rate_in;
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const auto *param = ¶m_in;
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const auto *grad = &grad_in;
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auto sz = param_out->numel();
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PADDLE_ENFORCE_EQ(param->numel(),
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sz,
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common::errors::InvalidArgument(
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"Input parameter's number of elements is error, "
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"expected %lld, but received %lld.",
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sz,
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param->numel()));
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PADDLE_ENFORCE_EQ(grad->numel(),
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sz,
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common::errors::InvalidArgument(
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"Input gradient's number of elements is error, "
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"expected %lld, but received %lld.",
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sz,
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grad->numel()));
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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 = dev_ctx.template Alloc<T>(param_out);
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T clip = static_cast<T>(clip_in);
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T batch_size = static_cast<T>(batch_size_in);
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T sigma = static_cast<T>(sigma_in);
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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 = static_cast<unsigned int>(seed_in);
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if (seed == 0) {
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seed = (unsigned)(time(NULL));
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}
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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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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] = 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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} // namespace phi
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PD_REGISTER_KERNEL(dpsgd, CPU, ALL_LAYOUT, phi::DpsgdOpKernel, float, double) {}
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