//   Copyright (c) 2018 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

#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) || defined(PADDLE_WITH_ASCEND_CL)

#include <stdio.h>
#include <memory>
#include <string>
#include <thread>  // NOLINT
#include <typeindex>
#include <unordered_map>
#include <vector>

#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/collective_helper.h"

#ifdef PADDLE_WITH_NCCL
#include "paddle/fluid/platform/dynload/nccl.h"
#endif

#ifdef PADDLE_WITH_RCCL
#include "paddle/fluid/platform/dynload/rccl.h"
#endif

#ifdef PADDLE_WITH_ASCEND_CL
#include "paddle/fluid/platform/dynload/hccl.h"
#endif

#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"

#define NCCL_ID_VARNAME "NCCLID"

namespace paddle {
namespace platform {

inline hcclDataType_t ToHCCLDataType(framework::proto::VarType::Type type) {
  if (type == framework::proto::VarType::FP32) {
    return HCCL_DATA_TYPE_FP32;
  } else if (type == framework::proto::VarType::FP16) {
    return HCCL_DATA_TYPE_FP16;
  }else if (type == framework::proto::VarType::INT32) {
    return HCCL_DATA_TYPE_INT32;
  } else if (type == framework::proto::VarType::INT8) {
    return HCCL_DATA_TYPE_INT8;
  } 
  // else if (type == framework::proto::VarType::FP64) {
  //   return HCCL_DATA_TYPE_FP32;
  // }
  else {
    PADDLE_THROW(platform::errors::Unimplemented(
        "This datatype in hccl is not supported."));
  }
}

// // NOTE(minqiyang): according to the ncclGroupEnd documentations:
// // https://docs.nvidia.com/deeplearning/sdk/nccl-api/ncclapidoc.html,
// // ncclGroupEnd will wait for all communicators to be initialized, which will
// // cause blocking problem when a runtime_error was thrown, so try only guard
// // NCCL actions when use it.
// class NCCLGroupGuard {
//  public:
//   static std::mutex &NCCLMutex() {
//     static std::mutex mtx;
//     return mtx;
//   }

//   inline NCCLGroupGuard() {
//     NCCLMutex().lock();
//     PADDLE_ENFORCE_CUDA_SUCCESS(dynload::ncclGroupStart());
//   }

//   inline ~NCCLGroupGuard() PADDLE_MAY_THROW {
//     PADDLE_ENFORCE_CUDA_SUCCESS(dynload::ncclGroupEnd());
//     NCCLMutex().unlock();
//   }
// };

// struct NCCLContext {
//   std::unique_ptr<CUDADeviceContext> ctx_;
//   ncclComm_t comm_;

//   explicit NCCLContext(int dev_id)
//       : ctx_(new CUDADeviceContext(CUDAPlace(dev_id))), comm_{nullptr} {}

//   gpuStream_t stream() const { return ctx_->stream(); }
//   ncclComm_t comm() const { return comm_; }

//   int device_id() const {
//     return BOOST_GET_CONST(platform::CUDAPlace, ctx_->GetPlace()).device;
//   }
// };

// struct NCCLContextMap {
//   std::unordered_map<int, NCCLContext> contexts_;
//   std::vector<int> order_;

//   explicit NCCLContextMap(const std::vector<platform::Place> &places,
//                           ncclUniqueId *nccl_id = nullptr,
//                           size_t num_trainers = 1, size_t trainer_id = 0) {
//     PADDLE_ENFORCE_EQ(!places.empty(), true,
//                       platform::errors::InvalidArgument(
//                           "The NCCL place should not be empty."));
//     order_.reserve(places.size());
//     for (auto &p : places) {
//       int dev_id = BOOST_GET_CONST(CUDAPlace, p).device;
//       order_.emplace_back(dev_id);
//       contexts_.emplace(dev_id, NCCLContext(dev_id));
//     }
//     PADDLE_ENFORCE_EQ(
//         order_.size(), contexts_.size(),
//         platform::errors::Unavailable("NCCL Context Map does not support "
//                                       "contain two or more same device."));

//     std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
//     // if num_trainers == 1, should create a new nccl id for local comms.
//     if (num_trainers == 1 && nccl_id == nullptr) {
//       std::lock_guard<std::mutex> guard(NCCLGroupGuard::NCCLMutex());
//       PADDLE_RETRY_CUDA_SUCCESS(platform::dynload::ncclCommInitAll(
//           comms.get(), static_cast<int>(order_.size()), order_.data()));
//     } else {
//       PADDLE_ENFORCE_NOT_NULL(nccl_id, platform::errors::InvalidArgument(
//                                            "The NCCL id should not be null."));
//       {
//         int nranks = num_trainers * order_.size();
//         NCCLGroupGuard gurad;
//         for (size_t i = 0; i < order_.size(); ++i) {
//           int gpu_id = order_[i];
//           int rank;
//           if (order_.size() > 1) {
//             rank = trainer_id * order_.size() + i;
//           } else {
//             rank = trainer_id;
//           }
//           VLOG(1) << "init nccl rank:" << rank << ", nranks:" << nranks
//                   << ", gpu_id:" << gpu_id << ", dev_id:" << order_[i];
//           SetDeviceId(gpu_id);
//           PADDLE_RETRY_CUDA_SUCCESS(platform::dynload::ncclCommInitRank(
//               comms.get() + i, nranks, *nccl_id, rank));
//         }
//       }
//     }
//     int i = 0;
//     for (auto &dev_id : order_) {
//       contexts_.at(dev_id).comm_ = comms[i++];
//     }
//   }

//   NCCLContextMap(const NCCLContextMap &other) = delete;
//   NCCLContextMap &operator=(const NCCLContextMap &other) = delete;

//   CUDADeviceContext *DevCtx(int dev_id) const { return at(dev_id).ctx_.get(); }

//   CUDADeviceContext *DevCtx(platform::Place p) const {
//     return DevCtx(BOOST_GET_CONST(CUDAPlace, p).device);
//   }

//   const NCCLContext &at(platform::Place p) const {
//     return this->at(BOOST_GET_CONST(CUDAPlace, p).device);
//   }

//   const NCCLContext &at(int dev_id) const { return contexts_.at(dev_id); }

//   void WaitAll() {
//     for (auto &p : contexts_) {
//       p.second.ctx_->Wait();
//     }
//   }
// };

// inline std::string GetFlatNCCLVarName(size_t pos) {
//   if (pos == 0) {
//     return NCCL_ID_VARNAME;
//   }
//   return string::Sprintf("%s_%d", NCCL_ID_VARNAME, static_cast<int>(pos));
// }

// inline std::string GetHierarchicalExterNCCLVarName(size_t pos) {
//   return string::Sprintf("Hierarchical_exter_%s_%d", NCCL_ID_VARNAME,
//                          static_cast<int>(pos));
// }
// inline std::string GetHierarchicalInterNCCLVarName(size_t pos) {
//   return string::Sprintf("Hierarchical_inter_%s_%d", NCCL_ID_VARNAME,
//                          static_cast<int>(pos));
// }

// class NCCLCommunicator {
//  public:
//   NCCLCommunicator() {}
//   virtual ~NCCLCommunicator() PADDLE_MAY_THROW {}

//   NCCLContextMap *DefaultFlatCtx() const {
//     if (flat_ctxs_.size() == 0) {
//       return nullptr;
//     }

//     return flat_ctxs_[0].get();
//   }

//   std::vector<std::unique_ptr<NCCLContextMap>> *GetFlatCtxs() {
//     return &flat_ctxs_;
//   }

//   NCCLContextMap *GetFlatCtx(size_t run_order) const {
//     return flat_ctxs_[run_order % flat_ctxs_.size()].get();
//   }

//   NCCLContextMap *GetRunEnvNCCLCtx(size_t run_order,
//                                    bool use_hierarchical_allreduce) const {
//     if (!use_hierarchical_allreduce) {
//       return GetFlatCtx(run_order);
//     }

//     return GetHierarchicalInterCtx(run_order);
//   }

  
//    *When nccl inits nccl comm using ncclCommInitAll, it meets error when
//    *allreduce ophandle and sync_batch_norm_op use ncclallreduce parallelly. So
//    *create a new nccl comm for sync_batch_norm_op. And these codes should be
//    *polished with a unified nccl management.
  
//   NCCLContextMap *GetSyncBatchNormCtx(
//       framework::Scope *scope, const std::vector<platform::Place> &places) {
//     auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
//     if (nccl_id_var != nullptr) {
//       return DefaultFlatCtx();
//     }

//     if (sync_batch_norm_ctx_.get() == nullptr) {
//       sync_batch_norm_ctx_.reset(new NCCLContextMap(places));
//     }
//     return sync_batch_norm_ctx_.get();
//   }

//   void InitFlatCtxs(const std::vector<platform::Place> &places,
//                     const std::vector<ncclUniqueId *> &nccl_ids,
//                     size_t trainers_num, size_t trainer_id) {
//     if (nccl_ids.size() == 0) {
//       auto ptr = new platform::NCCLContextMap(places);
//       VLOG(1) << "init local trainer";
//       flat_ctxs_.emplace_back(ptr);
//     } else {
//       for (size_t i = 0; i < nccl_ids.size(); i++) {
//         auto ptr = new platform::NCCLContextMap(places, nccl_ids[i],
//                                                 trainers_num, trainer_id);
//         VLOG(1) << "init trainer_id:" << trainer_id << ", comm no:" << i;
//         flat_ctxs_.emplace_back(ptr);
//       }
//     }

//     // as Executor have no way to use ncclComm created by ParallelExecutor,
//     // we assign all flatten contexts to NCCLCommContext to fix.
//     int nranks = static_cast<int>(trainers_num * places.size());
//     int nrings = static_cast<int>(flat_ctxs_.size());
//     for (int ring_id = 0; ring_id < nrings; ++ring_id) {
//       for (size_t p = 0; p < places.size(); ++p) {
//         int rank = trainer_id * places.size() + p;
//         int dev_id = BOOST_GET_CONST(CUDAPlace, places[p]).device;
//         auto &ctx = flat_ctxs_[ring_id]->contexts_.at(dev_id);
//         NCCLCommContext::Instance().AssignNCCLComm(ctx.comm_, nranks, rank,
//                                                    dev_id, ring_id);
//       }
//     }
//   }

//   void InitHierarchicalCtxs(const std::vector<platform::Place> &places,
//                             const std::vector<ncclUniqueId *> &inter_nccl_ids,
//                             const std::vector<ncclUniqueId *> &exter_nccl_ids,
//                             size_t trainers_num, size_t trainer_id,
//                             size_t inter_trainers_num,
//                             size_t exter_trainers_num) {
//     PADDLE_ENFORCE_EQ(
//         trainers_num, inter_trainers_num * exter_trainers_num,
//         platform::errors::InvalidArgument(
//             "trainers_num:%llu != inter_trainers_num:%llu * "
//             "exter_trainers_num:%llu",
//             trainers_num, inter_trainers_num, exter_trainers_num));

//     PADDLE_ENFORCE_GT(
//         inter_trainers_num, 1,
//         platform::errors::InvalidArgument(
//             "The inter_trainers_num:%llu should be larger than 1.",
//             inter_trainers_num));

//     int inter_trainer_id = trainer_id % inter_trainers_num;
//     for (size_t i = 0; i < inter_nccl_ids.size(); i++) {
//       VLOG(1) << "init inter_trainer_id:" << inter_trainer_id
//               << ", comm no:" << i;
//       auto local = new NCCLContextMap(places, inter_nccl_ids[i],
//                                       inter_trainers_num, inter_trainer_id);

//       h_inter_ctxs_.emplace_back(local);
//     }

//     int exter_trainer_id = -1;
//     if (trainer_id % inter_trainers_num == 0) {
//       exter_trainer_id = trainer_id / inter_trainers_num;
//     }

//     if (exter_trainer_id >= 0) {
//       for (size_t i = 0; i < exter_nccl_ids.size(); i++) {
//         auto ex = new NCCLContextMap(places, exter_nccl_ids[i],
//                                      exter_trainers_num, exter_trainer_id);
//         VLOG(1) << "init exter_trainer_id:" << exter_trainer_id
//                 << ", comm no:" << i;
//         h_exter_ctxs_.emplace_back(ex);
//       }
//     }
//   }

//   bool NeedExterAllReduce() const { return h_exter_ctxs_.size() > 0; }

//   NCCLContextMap *GetHierarchicalInterCtx(size_t run_order) const {
//     PADDLE_ENFORCE_GT(h_inter_ctxs_.size(), 0,
//                       platform::errors::InvalidArgument(
//                           "Hierarchical ctxs should be initialized firstly!"));
//     return h_inter_ctxs_[run_order % h_inter_ctxs_.size()].get();
//   }

//   NCCLContextMap *GetHierarchicalExterCtx(size_t run_order) const {
//     PADDLE_ENFORCE_GT(h_exter_ctxs_.size(), 0,
//                       platform::errors::InvalidArgument(
//                           "Hierarchical ctxs should be initialized firstly!"));
//     return h_exter_ctxs_[run_order % h_exter_ctxs_.size()].get();
//   }

//   std::vector<std::unique_ptr<NCCLContextMap>> *GetHierarchicalInterCtxs() {
//     return &h_inter_ctxs_;
//   }

//   std::vector<std::unique_ptr<NCCLContextMap>> *GetHierarchicalExterCtxs() {
//     return &h_exter_ctxs_;
//   }

//  protected:
//   // Support multi nccl comm on default nccl ring while NCCLContextMap can't.
//   std::vector<std::unique_ptr<NCCLContextMap>> flat_ctxs_;

//   // h_inter_ctxs_ and h_exter_ctxs_ are for 2d allreduce.
//   // And h_exter_ctxs_ can support multi comm too.
//   std::vector<std::unique_ptr<NCCLContextMap>> h_inter_ctxs_;
//   std::vector<std::unique_ptr<NCCLContextMap>> h_exter_ctxs_;

//   // just used for sync_batch_norm op.
//   std::unique_ptr<NCCLContextMap> sync_batch_norm_ctx_;
// };

}  // namespace platform
}  // namespace paddle
#endif