// Copyright (c) 2023 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/logsumexp_kernel.h"
#include "paddle/phi/kernels/gpu/logsumexp_function.cu.h"

#include "paddle/phi/common/bfloat16.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/activation_kernel.h"
#include "paddle/phi/kernels/elementwise_add_kernel.h"
#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
#include "paddle/phi/kernels/funcs/activation_functor.h"
#include "paddle/phi/kernels/funcs/elementwise_base.h"
#include "paddle/phi/kernels/funcs/transpose_function.cu.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_max_kernel.h"
#include "paddle/phi/kernels/transpose_kernel.h"

namespace phi {

template <typename T>
struct ComputeType {
  using type = T;
};

template <>
struct ComputeType<phi::dtype::float16> {
  using type = float;
};

template <>
struct ComputeType<phi::dtype::bfloat16> {
  using type = float;
};

template <typename T, typename Context>
void LogsumexpFallbackKernel(const Context& dev_ctx,
                             const DenseTensor& x,
                             const std::vector<int>& axis_vec,
                             const std::vector<int64_t>& outdim_vec,
                             const std::vector<int64_t>& keeped_outdim_vec,
                             bool keepdim,
                             bool reduce_all,
                             DenseTensor* out) {
  auto* in_x = &x;
  auto* out_y = out;

  auto outdim = phi::make_ddim(outdim_vec);
  auto keeped_outdim = phi::make_ddim(keeped_outdim_vec);
  out->Resize(outdim);
  dev_ctx.template Alloc<T>(out_y);

  DenseTensor max_x;
  max_x.Resize(outdim);
  dev_ctx.template Alloc<T>(&max_x);

  phi::MaxKernel<T, Context>(dev_ctx, *in_x, axis_vec, false, &max_x);

  max_x.Resize(keeped_outdim);
  DenseTensor temp_x = Subtract<T, Context>(dev_ctx, *in_x, max_x);
  phi::funcs::ReduceKernel<T, T, kps::AddFunctor, kps::ExpFunctor<T>>(
      dev_ctx, temp_x, out_y, kps::ExpFunctor<T>(), axis_vec);

  phi::LogKernel<T, Context>(dev_ctx, *out_y, &temp_x);
  temp_x.Resize(outdim);
  out->Resize(outdim);
  phi::AddKernel<T, Context>(dev_ctx, temp_x, max_x, out);
}

template <typename T, typename Context>
void LogsumexpKernel(const Context& dev_ctx,
                     const DenseTensor& x,
                     const std::vector<int64_t>& axis,
                     bool keepdim,
                     bool reduce_all,
                     DenseTensor* out) {
  auto xdim = x.dims();
  for (size_t i = 0; i < xdim.size(); i++)
    PADDLE_ENFORCE_LT(0,
                      xdim[i],
                      errors::InvalidArgument(
                          "The dims of Input(X) should be greater than 0."));

  reduce_all = recompute_reduce_all(x, axis, reduce_all);
  std::vector<int64_t> outdim_vec, keeped_outdim_vec, transpose_shape;
  std::vector<int> axis_vec, perm;
  int64_t compute_size = 1, other_size = 1;
  for (auto i : axis) {
    auto v = i >= 0 ? i : i + xdim.size();
    axis_vec.push_back(v);
  }
  if (axis.size() == 0 || reduce_all) {
    axis_vec.clear();
    for (size_t i = 0; i < xdim.size(); i++) {
      axis_vec.push_back(i);
    }
  }
  for (size_t i = 0; i < xdim.size(); i++) {
    bool flag = false;
    for (auto v : axis_vec) {
      if (v == i) {
        flag = true;
        break;
      }
    }
    if (flag) {
      compute_size *= xdim[i];
      keeped_outdim_vec.push_back(1);
      if (keepdim) outdim_vec.push_back(1);
    } else {
      other_size *= xdim[i];
      transpose_shape.push_back(xdim[i]);
      perm.push_back(i);
      outdim_vec.push_back(xdim[i]);
      keeped_outdim_vec.push_back(xdim[i]);
    }
  }

  auto outdim = phi::make_ddim(outdim_vec);
  if (compute_size <= 1024) {
    if (perm.size() != xdim.size())
      perm.insert(perm.end(), axis_vec.begin(), axis_vec.end());
    for (auto i : axis_vec) transpose_shape.push_back(xdim[i]);
    DenseTensor transpose_x;
    if (xdim.size() == 0 ||
        (axis_vec.size() == 1 && axis_vec[0] == xdim.size())) {
      transpose_x = x;
    } else {
      transpose_x.Resize(make_ddim(transpose_shape));
      dev_ctx.template Alloc<T>(&transpose_x);
      phi::funcs::TransposeGPUKernelDriver<T>(dev_ctx, x, perm, &transpose_x);
    }
    dev_ctx.template Alloc<T>(out);
    using compute_type = typename ComputeType<T>::type;
    const int64_t num_col = compute_size, num_row = other_size;
    funcs::DispatchLogsumexpWarp<compute_type, T, Context>(
        dev_ctx, num_row, num_col, transpose_x.data<T>(), out->data<T>());
    out->Resize(outdim);
  } else {
    LogsumexpFallbackKernel<T, Context>(dev_ctx,
                                        x,
                                        axis_vec,
                                        outdim_vec,
                                        keeped_outdim_vec,
                                        keepdim,
                                        reduce_all,
                                        out);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(logsumexp,
                   GPU,
                   ALL_LAYOUT,
                   phi::LogsumexpKernel,
                   float,
                   double,
                   phi::dtype::float16,
                   phi::dtype::bfloat16) {}
