atan2_grad_kernel_impl.h

// 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 "paddle/fluid/platform/for_range.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/atan2_grad_kernel.h"

namespace phi {

// dx1 = dout * x2 / ((x1)^2 + (x2)^2)
// dx2 = - dout * x1 / ((x1)^2 + (x2)^2)
template <typename T>
struct Atan2GradFunctor {
  Atan2GradFunctor(
      const T* x1, const T* x2, const T* dout, T* dx1, T* dx2, int64_t numel)
      : x1_(x1), x2_(x2), dout_(dout), dx1_(dx1), dx2_(dx2), numel_(numel) {}

  HOSTDEVICE void operator()(int64_t idx) const {
    float x1 = static_cast<float>(x1_[idx]);
    float x2 = static_cast<float>(x2_[idx]);
    float x = x1 * x1 + x2 * x2;
    dx1_[idx] = static_cast<T>(static_cast<float>(dout_[idx]) * x2 / x);
    dx2_[idx] = static_cast<T>(-static_cast<float>(dout_[idx]) * x1 / x);
  }

  const T* x1_;
  const T* x2_;
  const T* dout_;
  T* dx1_;
  T* dx2_;
  int64_t numel_;
};

template <>
struct Atan2GradFunctor<double> {
  Atan2GradFunctor(const double* x1,
                   const double* x2,
                   const double* dout,
                   double* dx1,
                   double* dx2,
                   int64_t numel)
      : x1_(x1), x2_(x2), dout_(dout), dx1_(dx1), dx2_(dx2), numel_(numel) {}

  HOSTDEVICE void operator()(int64_t idx) const {
    auto x = x1_[idx] * x1_[idx] + x2_[idx] * x2_[idx];
    dx1_[idx] = dout_[idx] * x2_[idx] / x;
    dx2_[idx] = -dout_[idx] * x1_[idx] / x;
  }

  const double* x1_;
  const double* x2_;
  const double* dout_;
  double* dx1_;
  double* dx2_;
  int64_t numel_;
};

template <typename T, typename Context>
void Atan2GradKernel(const Context& ctx,
                     const DenseTensor& x,
                     const DenseTensor& y,
                     const DenseTensor& out_grad,
                     DenseTensor* x_grad,
                     DenseTensor* y_grad) {
  auto numel = x.numel();
  auto x_data = x.data<T>();
  auto y_data = y.data<T>();
  auto out_grad_data = out_grad.data<T>();

  auto* x_grad_data =
      ctx.template Alloc<T>(x_grad, size_t(x.numel() * sizeof(T)));
  auto* y_grad_data =
      ctx.template Alloc<T>(y_grad, size_t(y.numel() * sizeof(T)));

  paddle::platform::ForRange<Context> for_range(ctx, numel);
  phi::Atan2GradFunctor<T> functor(
      x_data, y_data, out_grad_data, x_grad_data, y_grad_data, numel);
  for_range(functor);
}

}  // namespace phi