chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/kldiv_loss_kernel_impl.h

@@ -0,0 +1,82 @@
+// 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 <string>
+
+#include "paddle/common/hostdevice.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+namespace phi {
+using Array1 = Eigen::DSizes<int64_t, 1>;
+template <typename T>
+struct KLDivLossForward {
+  bool log_target = false;
+
+  HOSTDEVICE KLDivLossForward(bool logTarget) : log_target(logTarget) {}
+
+  HOSTDEVICE T operator()(const T& target, const T& input) const {
+    if (log_target) {
+      return std::exp(target) * (target - input);
+    } else {
+      if (target <= 0) {
+        return 0;
+      } else {
+        return target * (std::log(target) - input);
+      }
+    }
+  }
+};
+
+template <typename T, typename Context>
+void KLDivLossKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     const DenseTensor& label,
+                     const std::string& reduction,
+                     bool log_target,
+                     DenseTensor* out) {
+  if (x.numel() == 0) {
+    Full<T, Context>(dev_ctx, out->dims(), NAN, out);
+    return;
+  }
+  auto& place = *(dev_ctx.eigen_device());
+  auto* input = &x;
+  auto* target = &label;
+  auto* loss = out;
+
+  const int64_t n = input->dims()[0];
+  dev_ctx.template Alloc<T>(loss);
+
+  auto input_t = EigenVector<T>::Flatten(*input);
+  auto target_t = EigenVector<T>::Flatten(*target);
+  auto loss_t = EigenVector<T>::Flatten(*loss);
+  auto output = target_t.binaryExpr(input_t, KLDivLossForward<T>(log_target));
+  if ("none" == reduction) {
+    loss_t.device(place) = output;
+  } else if ("batchmean" == reduction) {
+    auto output_sum = output.sum();
+    if (n > 0) {
+      loss_t.device(place) = output_sum / output_sum.constant(n);
+    } else {
+      loss_t.device(place) = output_sum;
+    }
+  } else if ("mean" == reduction) {
+    loss_t.device(place) = output.mean();
+  } else if ("sum" == reduction) {
+    loss_t.device(place) = output.sum();
+  }
+}
+}  // namespace phi