chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/dpsgd_kernel.cc

@@ -0,0 +1,114 @@
+// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <math.h>
+#include <stdlib.h>
+
+#include <iostream>
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DpsgdOpKernel(const Context &dev_ctx,
+                   const DenseTensor &param_in,
+                   const DenseTensor &grad_in,
+                   const DenseTensor &learning_rate_in,
+                   float clip_in,
+                   float batch_size_in,
+                   float sigma_in,
+                   int seed_in,
+                   DenseTensor *param_out) {
+  const auto *learning_rate = &learning_rate_in;
+
+  const auto *param = &param_in;
+  const auto *grad = &grad_in;
+
+  auto sz = param_out->numel();
+  PADDLE_ENFORCE_EQ(param->numel(),
+                    sz,
+                    common::errors::InvalidArgument(
+                        "Input parameter's number of elements is error, "
+                        "expected %lld, but received %lld.",
+                        sz,
+                        param->numel()));
+  PADDLE_ENFORCE_EQ(grad->numel(),
+                    sz,
+                    common::errors::InvalidArgument(
+                        "Input gradient's number of elements is error, "
+                        "expected %lld, but received %lld.",
+                        sz,
+                        grad->numel()));
+
+  const T *lr = learning_rate->data<T>();
+  const T *param_data = param->data<T>();
+  const T *grad_data = grad->data<T>();
+
+  T *out_data = dev_ctx.template Alloc<T>(param_out);
+
+  T clip = static_cast<T>(clip_in);
+  T batch_size = static_cast<T>(batch_size_in);
+  T sigma = static_cast<T>(sigma_in);
+
+  // compute clipping
+  float l2_norm = 0.0;
+  for (int64_t i = 0; i < grad->numel(); ++i) {
+    l2_norm = l2_norm + grad_data[i] * grad_data[i];
+  }
+  l2_norm = std::sqrt(l2_norm);
+
+  float scale = 1.0;
+  if (l2_norm > clip) {
+    scale = l2_norm / clip;
+  }
+
+  // generate gaussian noise.
+  // [https://en.wikipedia.org/wiki/Box-Muller_transform]
+  float V1, V2, S;
+  float X;
+  float mu = 0.0;
+  float U1, U2;
+  unsigned seed = static_cast<unsigned int>(seed_in);
+  if (seed == 0) {
+    seed = (unsigned)(time(NULL));
+  }
+  std::minstd_rand engine;
+  engine.seed(seed);
+  std::uniform_real_distribution<T> dist(0.0, 1.0);
+  do {
+    U1 = dist(engine);
+    U2 = dist(engine);
+    V1 = 2 * U1 - 1;
+    V2 = 2 * U2 - 1;
+    S = V1 * V1 + V2 * V2;
+  } while (S >= 1 || S == 0);
+
+  X = V1 * sqrt(-2 * log(S) / S);
+
+  float gaussian_noise = mu + X * sigma;
+
+  // update parameters
+  for (int64_t i = 0; i < grad->numel(); ++i) {
+    out_data[i] = param_data[i] -
+                  lr[0] * (grad_data[i] / scale + gaussian_noise / batch_size);
+  }
+  // CCS16 - Deep Learning with Differential Privacy.
+  // [https://arxiv.org/abs/1607.00133]
+}  // Compute
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(dpsgd, CPU, ALL_LAYOUT, phi::DpsgdOpKernel, float, double) {}