// Copyright (c) 2025 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/top_k_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/xpu/xpu_mem_util.h"

namespace {
inline void GetDims(
    const phi::DDim& dim, int axis, int64_t* pre, int64_t* n, int64_t* post) {
  *pre = 1;
  *post = 1;
  *n = dim[axis];
  for (int i = 0; i < axis; ++i) {
    (*pre) *= dim[i];
  }
  for (int i = axis + 1; i < dim.size(); ++i) {
    (*post) *= dim[i];
  }
}
}  // namespace

namespace phi {

template <typename T, typename Context>
void TopkGradKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    const DenseTensor& indices,
                    const DenseTensor& out_grad,
                    const Scalar& k_scalar,
                    int axis,
                    bool largest,
                    bool sorted,
                    DenseTensor* x_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;

  if (x_grad && x_grad->numel() == 0) {
    dev_ctx.template Alloc<T>(x_grad);
    return;
  }
  const auto& in_dims = x.dims();

  // get the real the axis and the k
  if (axis < 0) {
    axis += in_dims.size();
  }

  // allocate the xpu memory for the x_grad
  T* x_grad_data = dev_ctx.template Alloc<T>(x_grad);
  const T* out_grad_data = out_grad.data<T>();
  const int64_t* indices_data = indices.data<int64_t>();

  if (in_dims.size() == 0) {
    Copy<Context>(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
    return;
  }

  int64_t pre, n, post;
  GetDims(in_dims, axis, &pre, &n, &post);

  Full<T, Context>(dev_ctx, x_grad->dims(), 0.0f, x_grad);

  // launch the xpu kernel to assign the grad
  int ret = xpu::scatter_element<XPUType, int64_t>(
      dev_ctx.x_context(),
      reinterpret_cast<const XPUType*>(x_grad_data),
      reinterpret_cast<const XPUType*>(out_grad_data),
      indices_data,
      reinterpret_cast<XPUType*>(x_grad_data),
      vectorize(x_grad->dims()),
      vectorize(out_grad.dims()),
      vectorize(indices.dims()),
      axis,
      /*reduction=override*/ 0);
  PADDLE_ENFORCE_XDNN_SUCCESS(ret, "scatter");
}

template <typename T, typename Context>
void TopkV1GradKernel(const Context& dev_ctx,
                      const DenseTensor& x,
                      const DenseTensor& indices,
                      const DenseTensor& out_grad,
                      const Scalar& k_scalar,
                      DenseTensor* x_grad) {
  TopkGradKernel<T, Context>(
      dev_ctx, x, indices, out_grad, k_scalar, -1, true, true, x_grad);
}

}  // namespace phi

PD_REGISTER_KERNEL(topk_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::TopkGradKernel,
                   float,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16) {}

PD_REGISTER_KERNEL(topk_v1_grad,
                   XPU,
                   ALL_LAYOUT,
                   phi::TopkV1GradKernel,
                   float,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16) {}