chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/adamax_kernel_impl.h

@@ -0,0 +1,72 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/kernels/adamax_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void AdamaxKernel(const Context& dev_ctx,
+                  const DenseTensor& param,
+                  const DenseTensor& grad,
+                  const DenseTensor& learning_rate,
+                  const DenseTensor& moment,
+                  const DenseTensor& inf_norm,
+                  const DenseTensor& beta1_pow,
+                  const optional<DenseTensor>& master_param UNUSED,
+                  float beta1,
+                  float beta2,
+                  float epsilon,
+                  bool multi_precision UNUSED,
+                  DenseTensor* param_out,
+                  DenseTensor* moment_out,
+                  DenseTensor* inf_norm_out,
+                  DenseTensor* master_param_outs UNUSED) {
+  dev_ctx.template Alloc<T>(param_out);
+  dev_ctx.template Alloc<T>(moment_out);
+  dev_ctx.template Alloc<T>(inf_norm_out);
+
+  T beta1_ = static_cast<T>(beta1);
+  T beta2_ = static_cast<T>(beta2);
+  T epsilon_ = static_cast<T>(epsilon);
+
+  auto eigen_param = EigenVector<T>::Flatten(param);
+  auto eigen_grad = EigenVector<T>::Flatten(grad);
+  auto eigen_moment = EigenVector<T>::Flatten(moment);
+  auto eigen_inf_norm = EigenVector<T>::Flatten(inf_norm);
+  auto eigen_lr = EigenVector<T>::Flatten(learning_rate);
+  auto eigen_beta1_pow = EigenVector<T>::Flatten(beta1_pow);
+
+  auto eigen_param_out = EigenVector<T>::Flatten(*param_out);
+  auto eigen_moment_out = EigenVector<T>::Flatten(*moment_out);
+  auto eigen_inf_norm_out = EigenVector<T>::Flatten(*inf_norm_out);
+
+  auto& place = *dev_ctx.eigen_device();
+
+  eigen_moment_out.device(place) =
+      beta1_ * eigen_moment + (static_cast<T>(1) - beta1_) * eigen_grad;
+  eigen_inf_norm_out.device(place) =
+      eigen_grad.abs().cwiseMax((beta2_ * eigen_inf_norm) + epsilon_);
+  auto lr_t = eigen_lr / (static_cast<T>(1) - eigen_beta1_pow);
+  Eigen::DSizes<int, 1> m_dsize(moment_out->numel());
+  eigen_param_out.device(place) =
+      eigen_param -
+      lr_t.broadcast(m_dsize) * (eigen_moment_out / eigen_inf_norm_out);
+}
+
+}  // namespace phi