chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/log_softmax_grad_kernel.cc

@@ -0,0 +1,91 @@
+// 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/log_softmax_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/axis_utils.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, int MajorType = Eigen::RowMajor>
+using EigenMatrixTemplate = EigenMatrix<T, MajorType>;
+
+template <typename Context, typename T>
+struct LogSoftmaxGradFunctor {
+  void operator()(const Context& dev_ctx,
+                  const DenseTensor* Y,
+                  const DenseTensor* dY,
+                  DenseTensor* dX,
+                  const int axis) {
+    constexpr int kBatchDim = 0;
+    constexpr int kClassDim = 1;
+
+    const int n = funcs::SizeToAxis(axis, Y->dims());
+    const int d = funcs::SizeFromAxis(axis, Y->dims());
+    DDim dim_2d{n, d};
+
+    auto y = EigenMatrixTemplate<T>::From(*Y, dim_2d);
+    auto dy = EigenMatrixTemplate<T>::From(*dY, dim_2d);
+    auto dx = EigenMatrixTemplate<T>::From(*dX, dim_2d);
+
+    const int axis_dim = static_cast<int>(Y->dims()[axis]);
+    const int batch_size = y.dimension(kBatchDim);
+    const int num_classes = y.dimension(kClassDim);
+    const int num_remain = num_classes / axis_dim;
+
+    Eigen::DSizes<int, 1> along_class(kClassDim);
+    Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
+    Eigen::DSizes<int, 2> one_axis(1, axis_dim);
+
+    dx.device(*dev_ctx.eigen_device()) =
+        dy - (y.exp()) * (dy.reshape(batch_axis_remain)
+                              .sum(along_class)
+                              .broadcast(one_axis));
+  }
+};
+
+template <typename T, typename Context>
+void LogSoftmaxGradKernel(const Context& dev_ctx,
+                          const DenseTensor& out,
+                          const DenseTensor& out_grad,
+                          int axis,
+                          DenseTensor* x_grad) {
+  const int rank = out.dims().size();
+  const int canonical_axis = funcs::CanonicalAxis(axis, rank);
+
+  dev_ctx.template Alloc<T>(x_grad);
+  // For 0D Tensor
+  if (rank == 0) {
+    funcs::set_constant(dev_ctx, x_grad, static_cast<T>(0.0));
+    return;
+  }
+  if (out.numel() != 0) {
+    LogSoftmaxGradFunctor<Context, T>()(
+        dev_ctx, &out, &out_grad, x_grad, canonical_axis);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(log_softmax_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::LogSoftmaxGradKernel,
+                   float,
+                   double) {}