chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/asgd_kernel.cc

@@ -0,0 +1,73 @@
+// Copyright (c) 2022 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 "paddle/phi/kernels/asgd_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/jit/kernels.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ASGDKernelCPUImpl(const Context& dev_ctx,
+                       const DenseTensor& param,
+                       const DenseTensor& grad,
+                       const DenseTensor& learning_rate,
+                       const DenseTensor& d,
+                       const DenseTensor& y,
+                       const DenseTensor& n,
+                       DenseTensor* param_out,
+                       DenseTensor* d_out,
+                       DenseTensor* y_out) {
+  auto param_eigen = EigenVector<T>::Flatten(param);
+  auto grad_eigen = EigenVector<T>::Flatten(grad);
+  auto d_eigen = EigenVector<T>::Flatten(d);
+  auto y_eigen = EigenVector<T>::Flatten(y);
+  auto param_out_eigen = EigenVector<T>::Flatten(*param_out);
+  auto d_out_eigen = EigenVector<T>::Flatten(*d_out);
+  auto y_out_eigen = EigenVector<T>::Flatten(*y_out);
+  T learning_rate_T = learning_rate.data<T>()[0];
+  T n_T = n.data<T>()[0];
+
+  d_out_eigen = d_eigen - y_eigen + grad_eigen;
+  y_out_eigen = grad_eigen;
+  param_out_eigen = param_eigen - (learning_rate_T / n_T) * d_out_eigen;
+}
+
+template <typename T, typename Context>
+void ASGDKernel(const Context& dev_ctx,
+                const DenseTensor& param,
+                const DenseTensor& grad,
+                const DenseTensor& learning_rate,
+                const DenseTensor& d,
+                const DenseTensor& y,
+                const DenseTensor& n,
+                const optional<DenseTensor>& master_param UNUSED,
+                bool multi_precision UNUSED,
+                DenseTensor* param_out,
+                DenseTensor* d_out,
+                DenseTensor* y_out,
+                DenseTensor* master_param_out UNUSED) {
+  dev_ctx.template Alloc<T>(param_out);
+  dev_ctx.template Alloc<T>(d_out);
+  dev_ctx.template Alloc<T>(y_out);
+  ASGDKernelCPUImpl<T, Context>(
+      dev_ctx, param, grad, learning_rate, d, y, n, param_out, d_out, y_out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(asgd, CPU, ALL_LAYOUT, phi::ASGDKernel, float, double) {}