chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/dropout_grad_kernel.cc

@@ -0,0 +1,97 @@
+// 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/dropout_grad_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void DropoutNdGradKernel(const Context& dev_ctx,
+                         const DenseTensor& mask,
+                         const DenseTensor& out_grad,
+                         const Scalar& p,
+                         bool is_test,
+                         const std::string& mode,
+                         const std::vector<int>& axis,
+                         DenseTensor* x_grad) {
+  auto* grad_x = x_grad;
+  auto* grad_y = &out_grad;
+  dev_ctx.template Alloc<T>(grad_x);
+
+  auto dX = EigenVector<T>::Flatten(*grad_x);
+  auto dY = EigenVector<T>::Flatten(*grad_y);
+  float prob = p.to<float>();
+
+  auto& place = *dev_ctx.eigen_device();
+  auto& dropout_implementation = mode;
+  if (is_test == true) {
+    if (dropout_implementation == "upscale_in_train") {
+      dX.device(place) = static_cast<T>(1) * dY;
+    } else {
+      dX.device(place) = dY * static_cast<T>(1.0f - prob);
+    }
+  } else {
+    std::vector<int64_t> out_dims = vectorize(out_grad.dims());
+    auto M = EigenVector<uint8_t>::Flatten(mask);
+    if (dropout_implementation == "upscale_in_train") {
+      if (prob == 1.0f) {
+        dX.device(place) = static_cast<T>(0) * dY;
+      } else {
+        if (axis.empty()) {
+          dX.device(place) = dY * M.cast<T>() / static_cast<T>(1.0f - prob);
+        } else {
+          dX.device(place) = dY * M.broadcast(out_dims).cast<T>() /
+                             static_cast<T>(1.0f - prob);
+        }
+      }
+    } else {
+      if (axis.empty()) {
+        dX.device(place) = dY * M.cast<T>();
+      } else {
+        dX.device(place) = dY * M.broadcast(out_dims).cast<T>();
+      }
+    }
+  }
+}
+
+template <typename T, typename Context>
+void DropoutGradRawKernel(const Context& dev_ctx,
+                          const DenseTensor& mask,
+                          const DenseTensor& out_grad,
+                          const Scalar& p,
+                          bool is_test,
+                          const std::string& mode,
+                          DenseTensor* x_grad) {
+  DropoutNdGradKernel<T, Context>(
+      dev_ctx, mask, out_grad, p.to<float>(), is_test, mode, {}, x_grad);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(dropout_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::DropoutGradRawKernel,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16) {}
+
+PD_REGISTER_KERNEL(
+    dropout_nd_grad, CPU, ALL_LAYOUT, phi::DropoutNdGradKernel, float, double) {
+}