// 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/graph_send_recv_kernel.h"
#include "paddle/phi/kernels/cpu/graph_send_recv_funcs.h"

#include <algorithm>
#include <set>
#include <vector>

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/hostdevice.h"
#include "paddle/phi/core/kernel_registry.h"

namespace phi {

template <typename T, typename IndexT, typename Functor>
void GraphSendRecvCpuLoop(const int& input_size,
                          const int& index_size,
                          const IndexT* s_index,
                          const IndexT* d_index,
                          const DenseTensor& src,
                          DenseTensor* dst,
                          const std::string& pool_type,
                          int* dst_count = nullptr) {
  Functor functor;
  if (pool_type == "SUM") {
    for (int i = 0; i < index_size; ++i) {
      const IndexT& src_idx = s_index[i];
      const IndexT& dst_idx = d_index[i];
      ElementwiseInnerOperation<T, IndexT, Functor>(
          src, dst, src_idx, dst_idx, false, functor);
    }
  } else if (pool_type == "MEAN") {
    for (int i = 0; i < index_size; ++i) {
      const IndexT& src_idx = s_index[i];
      const IndexT& dst_idx = d_index[i];
      ElementwiseInnerOperation<T, IndexT, Functor>(
          src, dst, src_idx, dst_idx, false, functor);
    }
    for (int i = 0; i < index_size; ++i) {
      IndexT dst_idx = d_index[i];
      *(dst_count + dst_idx) += 1;
    }
    for (int i = 0; i < input_size; ++i) {
      if (*(dst_count + i) == 0) continue;
      auto dst_slice = dst->Slice(i, i + 1);
      auto eigen_dst = phi::EigenVector<T>::Flatten(dst_slice);
      eigen_dst = eigen_dst / static_cast<T>(*(dst_count + i));
    }
  } else if (pool_type == "MIN" || pool_type == "MAX") {
    std::set<IndexT> existed_dst;
    for (int i = 0; i < index_size; ++i) {
      const IndexT& src_idx = s_index[i];
      const IndexT& dst_idx = d_index[i];
      bool in_set = existed_dst.find(dst_idx) != existed_dst.end();
      if (!in_set) {
        ElementwiseInnerOperation<T, IndexT, Functor>(
            src, dst, src_idx, dst_idx, true, functor);
        existed_dst.emplace(dst_idx);
      } else {
        ElementwiseInnerOperation<T, IndexT, Functor>(
            src, dst, src_idx, dst_idx, false, functor);
      }
    }
  }
}

template <typename Context, typename T, typename IndexT>
void GraphSendRecvOpKernelLaunchHelper(const Context& ctx,
                                       const DenseTensor& x,
                                       const DenseTensor& src_index,
                                       const DenseTensor& dst_index,
                                       const std::string& pool_type,
                                       DenseTensor* out,
                                       DenseTensor* dst_count = nullptr) {
  const int& index_size = src_index.dims()[0];

  ctx.template Alloc<T>(out);
  T* p_output = out->data<T>();
  const auto& src_dims = x.dims();
  int64_t memset_size = 1;
  for (int i = 0; i < src_dims.size(); ++i) memset_size *= src_dims[i];
  const size_t& memset_bytes = memset_size * sizeof(T);
  memset(p_output, 0, memset_bytes);

  if (index_size == 0) return;

  const IndexT* s_index = src_index.data<IndexT>();
  const IndexT* d_index = dst_index.data<IndexT>();
  if (pool_type == "SUM") {
    GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(
        src_dims[0], index_size, s_index, d_index, x, out, pool_type);
  } else if (pool_type == "MIN") {
    GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMinFunctor<T>>(
        src_dims[0], index_size, s_index, d_index, x, out, pool_type);
  } else if (pool_type == "MAX") {
    GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvMaxFunctor<T>>(
        src_dims[0], index_size, s_index, d_index, x, out, pool_type);
  } else if (pool_type == "MEAN") {
    ctx.template Alloc<int>(dst_count);
    int* p_dst_count = dst_count->data<int>();
    memset(p_dst_count, 0, src_dims[0] * sizeof(int));
    GraphSendRecvCpuLoop<T, IndexT, GraphSendRecvSumFunctor<T>>(src_dims[0],
                                                                index_size,
                                                                s_index,
                                                                d_index,
                                                                x,
                                                                out,
                                                                pool_type,
                                                                p_dst_count);
  }
}

template <typename T, typename Context>
void GraphSendRecvKernel(const Context& ctx,
                         const DenseTensor& x,
                         const DenseTensor& src_index,
                         const DenseTensor& dst_index,
                         const std::string& pool_type,
                         DenseTensor* out,
                         DenseTensor* dst_count) {
  auto index_type = src_index.dtype();
  if (index_type == phi::DataType::INT32) {
    GraphSendRecvOpKernelLaunchHelper<Context, T, int32_t>(
        ctx, x, src_index, dst_index, pool_type, out, dst_count);
  } else if (index_type == phi::DataType::INT64) {
    GraphSendRecvOpKernelLaunchHelper<Context, T, int64_t>(
        ctx, x, src_index, dst_index, pool_type, out, dst_count);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(graph_send_recv,
                   CPU,
                   ALL_LAYOUT,
                   phi::GraphSendRecvKernel,
                   float,
                   double,
                   int,
                   int64_t) {}