// 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/argsort_kernel.h"

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

namespace phi {

const int XPU_SORT_MAX_SIZE = 16384;

template <typename T, typename TID>
static inline void xpu_argsort(xpu::Context* ctx,
                               const T* input_data,
                               T* output_data,
                               TID* indices_data,
                               int m,
                               int n,
                               bool descending) {
  int ret =
      xpu::sort(ctx, input_data, output_data, indices_data, m, n, descending);
  PADDLE_ENFORCE_EQ(
      ret,
      XPU_SUCCESS,
      errors::External("XPU sort kernel return wrong value[%d %s].",
                       ret,
                       XPUAPIErrorMsg[ret]));
}

template <typename T>
static inline void xpu_transpose(xpu::Context* ctx,
                                 const T* x,
                                 T* y,
                                 const std::vector<int>& xshape,
                                 const std::vector<int>& permute) {
  int ret = xpu::transpose(ctx, x, y, xshape, permute);
  PADDLE_ENFORCE_EQ(
      ret,
      XPU_SUCCESS,
      errors::External("XPU transpose kernel return wrong value[%d %s]",
                       ret,
                       XPUAPIErrorMsg[ret]));
}

template <typename TX, typename TY>
static inline void xpu_cast(xpu::Context* ctx, const TX* x, TY* y, int len) {
  int ret = xpu::cast_v2(ctx, x, y, len);
  PADDLE_ENFORCE_EQ(
      ret,
      XPU_SUCCESS,
      errors::External("XPU cast kernel return wrong value[%d %s]",
                       ret,
                       XPUAPIErrorMsg[ret]));
}

template <typename T,
          bool VALUE_NEED_CAST = false,
          bool INDEX_NEED_CAST = false>
struct XPUArgsort {
  void operator()(xpu::Context* ctx,
                  const T* input_data,
                  T* output_data,
                  int64_t* indices_data,
                  const std::vector<int>& data_shape,
                  const std::vector<int>& permute,
                  bool descending) {
    xpu::ctx_guard RAII_GUARD(ctx);
    int m = data_shape[0] * data_shape[2];
    int n = data_shape[1];
    int len = data_shape[0] * data_shape[1] * data_shape[2];
    std::vector<int> trans_data_shape{
        data_shape[0], data_shape[2], data_shape[1]};

    T* input_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
    T* output_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
    int64_t* indices_data_trans = RAII_GUARD.alloc_l3_or_gm<int64_t>(len);

    xpu_transpose(ctx, input_data, input_data_trans, data_shape, permute);
    xpu_argsort(ctx,
                input_data_trans,
                output_data_trans,
                indices_data_trans,
                m,
                n,
                descending);
    xpu_transpose(
        ctx, output_data_trans, output_data, trans_data_shape, permute);
    xpu_transpose(
        ctx, indices_data_trans, indices_data, trans_data_shape, permute);
  }
};

template <typename T>
struct XPUArgsort<T, false, true> {
  void operator()(xpu::Context* ctx,
                  const T* input_data,
                  T* output_data,
                  int64_t* indices_data,
                  const std::vector<int>& data_shape,
                  const std::vector<int>& permute,
                  bool descending) {
    xpu::ctx_guard RAII_GUARD(ctx);
    int m = data_shape[0] * data_shape[2];
    int n = data_shape[1];
    int len = data_shape[0] * data_shape[1] * data_shape[2];
    std::vector<int> trans_data_shape{
        data_shape[0], data_shape[2], data_shape[1]};

    T* input_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
    T* output_data_trans = RAII_GUARD.alloc_l3_or_gm<T>(len);
    int* indices_data_trans = RAII_GUARD.alloc_l3_or_gm<int>(len);
    int64_t* cast_data_int64 = RAII_GUARD.alloc_l3_or_gm<int64_t>(len);

    xpu_transpose(ctx, input_data, input_data_trans, data_shape, permute);
    xpu_argsort(ctx,
                input_data_trans,
                output_data_trans,
                indices_data_trans,
                m,
                n,
                descending);
    xpu_transpose(
        ctx, output_data_trans, output_data, trans_data_shape, permute);
    xpu_cast(ctx, indices_data_trans, cast_data_int64, len);
    xpu_transpose(
        ctx, cast_data_int64, indices_data, trans_data_shape, permute);
  }
};

template <>
struct XPUArgsort<int64_t, true, true> {
  void operator()(xpu::Context* ctx,
                  const int64_t* input_data,
                  int64_t* output_data,
                  int64_t* indices_data,
                  const std::vector<int>& data_shape,
                  const std::vector<int>& permute,
                  bool descending) {
    xpu::ctx_guard RAII_GUARD(ctx);
    int m = data_shape[0] * data_shape[2];
    int n = data_shape[1];
    int len = data_shape[0] * data_shape[1] * data_shape[2];
    std::vector<int> trans_data_shape{
        data_shape[0], data_shape[2], data_shape[1]};

    int* input_data_trans = RAII_GUARD.alloc_l3_or_gm<int>(len);
    int* output_data_trans = RAII_GUARD.alloc_l3_or_gm<int>(len);
    int* indices_data_trans = RAII_GUARD.alloc_l3_or_gm<int>(len);
    int* cast_data_int = RAII_GUARD.alloc_l3_or_gm<int>(len);
    int64_t* cast_data_int64 = RAII_GUARD.alloc_l3_or_gm<int64_t>(len);

    xpu_cast(ctx, input_data, cast_data_int, len);
    xpu_transpose(ctx, cast_data_int, input_data_trans, data_shape, permute);
    xpu_argsort(ctx,
                input_data_trans,
                output_data_trans,
                indices_data_trans,
                m,
                n,
                descending);

    xpu_cast(ctx, output_data_trans, cast_data_int64, len);
    xpu_transpose(ctx, cast_data_int64, output_data, trans_data_shape, permute);
    xpu_cast(ctx, indices_data_trans, cast_data_int64, len);
    xpu_transpose(
        ctx, cast_data_int64, indices_data, trans_data_shape, permute);
  }
};

template <typename T, typename Context>
void ArgsortKernel(const Context& dev_ctx,
                   const DenseTensor& input,
                   int axis,
                   bool descending,
                   DenseTensor* output,
                   DenseTensor* indices) {
  auto in_dims = input.dims();
  axis = (axis < 0) ? (in_dims.size() + axis) : axis;
  int n = in_dims[axis];

  PADDLE_ENFORCE_LT(
      n,
      XPU_SORT_MAX_SIZE,
      errors::InvalidArgument(
          "The axis dimension of Input should less than %d, but got %d.",
          XPU_SORT_MAX_SIZE,
          in_dims[axis]));

  auto input_data = input.data<T>();
  auto output_data = dev_ctx.template Alloc<T>(output);
  auto indices_data = dev_ctx.template Alloc<int64_t>(indices);

  int len_before = phi::product(phi::slice_ddim(in_dims, 0, axis));
  int len_after =
      phi::product(phi::slice_ddim(in_dims, axis + 1, in_dims.size()));
  bool int64_need_cast =
      (std::is_same<T, int64_t>::value && n > (XPU_SORT_MAX_SIZE / 2)) ? true
                                                                       : false;
  bool index_need_cast = (n > (XPU_SORT_MAX_SIZE / 2)) ? true : false;
  std::vector<int> permute_vec{0, 2, 1};
  std::vector<int> data_shape{len_before, n, len_after};

  if (int64_need_cast) {
    XPUArgsort<T, true, true>()(dev_ctx.x_context(),
                                input_data,
                                output_data,
                                indices_data,
                                data_shape,
                                permute_vec,
                                descending);
  } else if (index_need_cast) {
    XPUArgsort<T, false, true>()(dev_ctx.x_context(),
                                 input_data,
                                 output_data,
                                 indices_data,
                                 data_shape,
                                 permute_vec,
                                 descending);
  } else {
    XPUArgsort<T, false, false>()(dev_ctx.x_context(),
                                  input_data,
                                  output_data,
                                  indices_data,
                                  data_shape,
                                  permute_vec,
                                  descending);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(
    argsort, XPU, ALL_LAYOUT, phi::ArgsortKernel, float, int, int64_t) {}
