chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/huber_loss_kernel_impl.h

@@ -0,0 +1,62 @@
+// 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/common/amp_type_traits.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/huber_loss_kernel.h"
+
+namespace phi {
+
+template <typename T>
+struct HuberLossForward {
+  HOSTDEVICE HuberLossForward(const T& delta) : delta(delta) {}
+
+  HOSTDEVICE T operator()(const T& val) const {
+    T abs_val = abs(val);
+    if (abs_val <= delta) {
+      return static_cast<T>(0.5) * val * val;
+    } else {
+      return delta * (abs_val - static_cast<T>(0.5) * delta);
+    }
+  }
+
+  T delta;
+};
+
+template <typename T, typename Context>
+void HuberLossKernel(const Context& dev_ctx,
+                     const DenseTensor& input,
+                     const DenseTensor& label,
+                     float delta,
+                     DenseTensor* out,
+                     DenseTensor* residual) {
+  T delta_ = static_cast<T>(delta);
+  auto& place = *dev_ctx.eigen_device();
+
+  auto x = EigenVector<T>::Flatten(input);
+  auto y = EigenVector<T>::Flatten(label);
+
+  dev_ctx.template Alloc<T>(residual);
+  auto eigen_residual = EigenVector<T>::Flatten(*residual);
+  eigen_residual.device(place) = y - x;
+
+  dev_ctx.template Alloc<T>(out);
+  auto loss = EigenVector<T>::Flatten(*out);
+  loss.device(place) = eigen_residual.unaryExpr(HuberLossForward<T>(delta_));
+}
+
+}  // namespace phi