chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/logsumexp_grad_kernel_impl.h

@@ -0,0 +1,117 @@
+// 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 <type_traits>
+#include <vector>
+
+#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/funcs/reduce_grad_functions.h"
+#include "paddle/phi/kernels/logsumexp_grad_kernel.h"
+
+namespace phi {
+
+template <typename T>
+struct LogsumexpGradFunctor {
+  template <typename Context,
+            typename X,
+            typename Y,
+            typename DX,
+            typename DY,
+            typename Dim>
+  void operator()(const Context& place,
+                  X* x,
+                  Y* y,
+                  DX* dx,
+                  DY* dy,
+                  const Dim& dim,
+                  int size UNUSED) {
+    using MT = typename MPTypeTrait<T>::Type;
+    auto x_mt = (*x).template cast<MT>();
+    auto y_mt = (*y).template cast<MT>();
+    auto dy_mt = (*dy).template cast<MT>();
+    dx->device(place) =
+        (dy_mt.broadcast(dim) * (x_mt - y_mt.broadcast(dim)).exp())
+            .template cast<T>();
+  }
+};
+
+template <typename T, typename Context>
+void LogsumexpGradKernel(const Context& dev_ctx,
+                         const DenseTensor& in,
+                         const DenseTensor& out,
+                         const DenseTensor& out_grad,
+                         const std::vector<int>& axis_in,
+                         bool keepdim UNUSED,
+                         bool reduce_all,
+                         DenseTensor* in_grad) {
+  if (in_grad && in_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(in_grad);
+    return;
+  }
+  std::vector<int64_t> axis;
+  axis.reserve(axis_in.size());
+  std::for_each(axis_in.begin(), axis_in.end(), [&axis](const int& t) {
+    axis.push_back(static_cast<int64_t>(t));
+  });
+  dev_ctx.template Alloc<T>(in_grad);
+
+  reduce_all = recompute_reduce_all(in, axis, reduce_all);
+
+  if (reduce_all) {
+    auto x = EigenVector<T>::Flatten(in);
+    auto y = EigenVector<T>::Flatten(out);
+    auto dy = EigenVector<T>::Flatten(out_grad);
+    auto dx = EigenVector<T>::Flatten(*in_grad);
+    auto& place = *dev_ctx.eigen_device();
+    auto broadcast_dim = Eigen::array<int, 1>({{static_cast<int>(in.numel())}});
+    LogsumexpGradFunctor<T>()(
+        place, &x, &y, &dx, &dy, broadcast_dim, broadcast_dim[0]);
+  } else {
+    int rank = in.dims().size();
+    LogsumexpGradFunctor<T> functor;
+    std::vector<int32_t> axis32;
+    axis32.reserve(axis.size());
+    std::for_each(axis.begin(), axis.end(), [&axis32](const int64_t& t) {
+      axis32.push_back(t);
+    });
+    switch (rank) {
+      case 1:
+        funcs::ReduceGradFunctor<Context, T, 1, LogsumexpGradFunctor<T>>(
+            dev_ctx, in, out, out_grad, in_grad, functor, axis32);
+        break;
+      case 2:
+        funcs::ReduceGradFunctor<Context, T, 2, LogsumexpGradFunctor<T>>(
+            dev_ctx, in, out, out_grad, in_grad, functor, axis32);
+        break;
+      case 3:
+        funcs::ReduceGradFunctor<Context, T, 3, LogsumexpGradFunctor<T>>(
+            dev_ctx, in, out, out_grad, in_grad, functor, axis32);
+        break;
+      case 4:
+        funcs::ReduceGradFunctor<Context, T, 4, LogsumexpGradFunctor<T>>(
+            dev_ctx, in, out, out_grad, in_grad, functor, axis32);
+        break;
+      default:
+        PADDLE_THROW(common::errors::Unimplemented(
+            "Unsupported dimensions, please keep maximum dimensions of input "
+            "data less than 4."));
+        break;
+    }
+  }
+}
+
+}  // namespace phi