instance_norm_grad_kernel.cc

// 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/instance_norm_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T, typename Context>
void InstanceNormGradKernel(const Context& dev_ctx,
                            const DenseTensor& x,
                            const paddle::optional<DenseTensor>& scale,
                            const DenseTensor& saved_mean,
                            const DenseTensor& saved_variance,
                            const DenseTensor& y_grad,
                            float epsilon,
                            DenseTensor* x_grad,
                            DenseTensor* scale_grad,
                            DenseTensor* bias_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;

  const auto& x_dims = x.dims();
  int n = x_dims[0];
  int c = x_dims[1];
  int h = x_dims[2];
  int w = x_dims[3];

  dev_ctx.template Alloc<T>(x_grad);
  if (bias_grad != nullptr) {
    dev_ctx.template Alloc<float>(bias_grad);
  }
  if (scale_grad != nullptr) {
    dev_ctx.template Alloc<float>(scale_grad);
  }

  const auto scale_ptr = scale.get_ptr();

  int r = xpu::instance_norm_grad(
      dev_ctx.x_context(),
      reinterpret_cast<const XPUType*>(x.data<T>()),
      reinterpret_cast<const XPUType*>(y_grad.data<T>()),
      reinterpret_cast<XPUType*>(x_grad->data<T>()),
      scale_ptr->data<float>(),
      saved_mean.data<float>(),
      saved_variance.data<float>(),
      scale_grad->data<float>(),
      bias_grad->data<float>(),
      n,
      c,
      h,
      w,
      epsilon,
      true);

  PADDLE_ENFORCE_XDNN_SUCCESS(r, "instance_norm_grad");
}

}  // namespace phi

PD_REGISTER_KERNEL(
    instance_norm_grad, XPU, ALL_LAYOUT, phi::InstanceNormGradKernel, float) {}