// 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/fluid/distributed/collective/ProcessGroupBKCL.h"

#include "paddle/fluid/distributed/collective/BKCLTools.h"
#include "paddle/fluid/distributed/collective/Common.h"
#include "paddle/fluid/platform/device/xpu/bkcl_helper.h"
#include "paddle/fluid/platform/device/xpu/xpu_info.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/phi/core/errors.h"

namespace paddle {
namespace distributed {
using XPUDeviceContext = paddle::platform::XPUDeviceContext;

ProcessGroupBKCL::BKCLTask::BKCLTask(const Place& place,
                                     int rank,
                                     CommType comm_type,
                                     bool sync_op,
                                     bool use_calc_stream)
    : TaskStream(rank, comm_type, sync_op, use_calc_stream), place_(place) {
  comm_event_ = std::make_shared<XPUEventManager>();
}

ProcessGroupBKCL::BKCLTask::~BKCLTask() {}

bool ProcessGroupBKCL::BKCLTask::IsCompleted() {
  LOG_FIRST_N(WARNING, 1) << "XPU do not support event query now.";
  return true;
}

// TODO(sheniang03): Add timeout for wait, now timeout unused
bool ProcessGroupBKCL::BKCLTask::Wait(std::chrono::milliseconds timeout) {
  // Warning here when use calc stream but also invoke waiting explicitly.
  if (UseCalcStream()) {
    VLOG(3) << "Warning: The communication is on calc stream, wait here is "
               "useless.";
    return true;
  }

  const auto* calc_ctx = static_cast<XPUContext*>(
      platform::DeviceContextPool::Instance().Get(place_));
  comm_event_->Block(*calc_ctx);

  if (barrier_) {
    // If we use the work to do barrier, we should block cpu
    platform::XPUDeviceGuard guard(place_.GetDeviceId());
    xpu_wait();
  }
  return true;
}

// Same as Wait
void ProcessGroupBKCL::BKCLTask::Synchronize() { Wait(kWaitTimeout); }

ProcessGroupBKCL::ProcessGroupBKCL(const std::shared_ptr<Store>& store,
                                   int rank,
                                   int size,
                                   int gid)
    : ProcessGroupStream(rank, size, gid), store_(store) {}

void ProcessGroupBKCL::GroupStart() {
  PADDLE_ENFORCE_XPU_SUCCESS(bkcl_group_start());
}

void ProcessGroupBKCL::GroupEnd() {
  PADDLE_ENFORCE_XPU_SUCCESS(bkcl_group_end());
}

std::shared_ptr<ProcessGroupBKCL::BKCLTask> ProcessGroupBKCL::CreateTask(
    const Place& place,
    int rank,
    CommType comm_type,
    bool is_sync,
    bool use_calc_stream) {
  return std::make_shared<ProcessGroupBKCL::BKCLTask>(
      place, rank, comm_type, is_sync, use_calc_stream);
}

void ProcessGroupBKCL::BroadcastUniqueBKCLID(BKCLUniqueId* bkcl_id) {
  auto key = "ProcessGroupBKCL/bkcl_ids/" + std::to_string(gid_) + "/0";
  if (rank_ == 0) {
    auto id = std::vector<uint8_t>(
        reinterpret_cast<uint8_t*>(bkcl_id),
        reinterpret_cast<uint8_t*>(bkcl_id) + BKCL_UNIQUE_ID_BYTES);
    store_->set(key, id);
  } else {
    const auto& ret = store_->get(key);
    std::memcpy(bkcl_id, ret.data(), ret.size());
  }
}

void ProcessGroupBKCL::CreateBKCLEnvCache(const Place& place,
                                          const std::string& place_key) {
  BKCLUniqueId bkcl_id;
  if (rank_ == 0) {
    PADDLE_ENFORCE_XPU_SUCCESS(bkcl_get_unique_id(&bkcl_id));
  }
  BroadcastUniqueBKCLID(&bkcl_id);

  VLOG(3) << "init bkcl rank: " << rank_ << ", nranks: " << size_
          << ", place: " << place_key
          << ", bkcl uniqueid: " << SerializeBKCLUniqueId(bkcl_id);

  calc_event_ = std::make_shared<XPUEventManager>();
  auto* calc_ctx = static_cast<phi::XPUContext*>(
      platform::DeviceContextPool::Instance().Get(place));
  // must use XPUDeviceContext here to make sure XPUContext::Init() is called
  auto comm_ctx = std::make_unique<XPUDeviceContext>(place);
  BKCLContext_t bkcl_comm;
  BKCLCHECK(bkcl_init_rank(&bkcl_comm, GetRank(), GetSize(), &bkcl_id));
  comm_ctx->SetBkclContext(bkcl_comm);

  place_to_calc_ctx_[place_key] = calc_ctx;
  place_to_comm_ctx_[place_key] = std::move(comm_ctx);
}

void ProcessGroupBKCL::SyncCalcStream(const Place& place) {
  const std::string& key = GetKeyFromPlace(place);
  const auto* calc_ctx = place_to_calc_ctx_[key];
  const auto* comm_ctx = place_to_comm_ctx_[key].get();
  calc_event_->Record(*calc_ctx);
  calc_event_->Block(*comm_ctx);
}

template <typename Fn>
std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::Collective(
    phi::DenseTensor* out_tensor,
    const phi::DenseTensor& in_tensor,
    Fn fn,
    CommType op_type,
    bool sync_op,
    bool use_calc_stream) {
  const auto& place = in_tensor.place();
  const auto& key = GetKeyFromPlace(place);

  if (!calc_event_) {
    CreateBKCLEnvCache(place, key);
  }

  if (!use_calc_stream) {
    SyncCalcStream(place);
  }

  auto task = CreateTask(place, rank_, op_type, sync_op, use_calc_stream);

  const auto* calc_ctx = place_to_calc_ctx_[key];
  const auto& comm_ctx = place_to_comm_ctx_[key];
  auto bkcl_stream = use_calc_stream ? calc_ctx->stream() : comm_ctx->stream();
  fn(out_tensor, in_tensor, comm_ctx->bkcl_context(), bkcl_stream);

  if (!use_calc_stream) {
    task->comm_event_->Record(*comm_ctx.get());
  }

  return task;
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllReduce(
    phi::DenseTensor* out_tensor,
    const phi::DenseTensor& in_tensor,
    const AllreduceOptions& opts,
    bool sync_op,
    bool use_calc_stream) {
  return Collective(
      out_tensor,
      in_tensor,
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_reduce(
            comm,
            input.data(),
            output->data(),
            input.numel(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            ToBKCLRedType(opts.reduce_op),
            stream);
      },
      CommType::ALLREDUCE,
      sync_op,
      use_calc_stream);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::Broadcast(
    phi::DenseTensor* out_tensor,
    const phi::DenseTensor& in_tensor,
    const BroadcastOptions& opts,
    bool sync_op,
    bool use_calc_stream) {
  return Collective(
      out_tensor,
      in_tensor,
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        int root = opts.source_rank + opts.source_root;
        return bkcl_broadcast(comm,
                              input.data(),
                              output->data(),
                              input.numel(),
                              platform::ToBKCLDataType(
                                  framework::TransToProtoVarType(input.type())),
                              root,
                              stream);
      },
      CommType::BROADCAST,
      sync_op,
      use_calc_stream);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllGather(
    phi::DenseTensor* out_tensor,
    const phi::DenseTensor& in_tensor,
    int64_t offset,  // for compatibility, no use now
    int64_t numel,   // for compatibility, no use now
    bool sync_op,
    bool use_calc_stream) {
  return Collective(
      out_tensor,
      in_tensor,
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_gather(
            comm,
            input.data(),
            input.numel(),
            output->data(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            stream);
      },
      CommType::ALLGATHER,
      sync_op,
      use_calc_stream);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::Barrier(
    const BarrierOptions& opts) {
  PADDLE_ENFORCE_GE(opts.device_id,
                    0,
                    platform::errors::PreconditionNotMet(
                        "The barrier device id must greater or equal than 0."));
  platform::XPUPlace place(opts.device_id);
  auto allocator = std::unique_ptr<phi::Allocator>(
      new paddle::experimental::DefaultAllocator(place));
  phi::DenseTensorMeta meta(phi::DataType::FLOAT32, phi::DDim{1});
  phi::DenseTensor barrier_tensor{allocator.get(), meta};

  auto task = AllReduce(&barrier_tensor,
                        barrier_tensor,
                        {},
                        /*sync_op*/ true,
                        /*use_calc_stream*/ false);
  auto bkcl_task = dynamic_cast<BKCLTask*>(task.get());
  bkcl_task->barrier_ = true;
  return task;
}

const phi::DeviceContext& ProcessGroupBKCL::GetDeviceContext(
    const Place& place) const {
  return GetDeviceContext(place, /*use_calc_stream*/ false);
}

const phi::DeviceContext& ProcessGroupBKCL::GetDeviceContext(
    const Place& place, bool use_calc_stream) const {
  const std::string& key = GetKeyFromPlace(place);
  if (use_calc_stream) {
    const auto& iter = place_to_calc_ctx_.find(key);
    return *iter->second;
  } else {
    const auto& iter = place_to_comm_ctx_.find(key);
    PADDLE_ENFORCE_NE(iter,
                      place_to_comm_ctx_.end(),
                      platform::errors::InvalidArgument(
                          "Cannot find device context in process group."));
    return *iter->second;
  }
}

// below are old apis
std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllReduce(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors,
    const AllreduceOptions& opts) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_reduce(
            comm,
            input.data(),
            output->data(),
            input.numel(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            ToBKCLRedType(opts.reduce_op),
            stream);
      },
      CommType::ALLREDUCE,
      /*sync_op*/ true,
      /*use_calc_stream*/ false);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllReduce(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors,
    const AllreduceOptions& opts,
    bool sync_op) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_reduce(
            comm,
            input.data(),
            output->data(),
            input.numel(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            ToBKCLRedType(opts.reduce_op),
            stream);
      },
      CommType::ALLREDUCE,
      sync_op,
      /*use_calc_stream*/ false);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::Broadcast(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors,
    const BroadcastOptions& opts) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));

  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        const auto root =
            opts.source_rank * in_tensors.size() + opts.source_root;
        return bkcl_broadcast(comm,
                              input.data(),
                              output->data(),
                              input.numel(),
                              platform::ToBKCLDataType(
                                  framework::TransToProtoVarType(input.type())),
                              root,
                              stream);
      },
      CommType::BROADCAST,
      /*sync_op*/ true,
      /*use_calc_stream*/ false);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::Broadcast(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors,
    const BroadcastOptions& opts,
    bool sync_op) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));

  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        const auto root =
            opts.source_rank * in_tensors.size() + opts.source_root;
        return bkcl_broadcast(comm,
                              input.data(),
                              output->data(),
                              input.numel(),
                              platform::ToBKCLDataType(
                                  framework::TransToProtoVarType(input.type())),
                              root,
                              stream);
      },
      CommType::BROADCAST,
      sync_op,
      /*use_calc_stream*/ false);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllGather(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      CheckTensorsInXPUPlace(out_tensors),
      true,
      platform::errors::InvalidArgument("All outputs should be in XPUPlace."));
  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_gather(
            comm,
            input.data(),
            input.numel(),
            output->data(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            stream);
      },
      CommType::ALLGATHER,
      /*sync_op*/ true,
      /*use_calc_stream*/ false);
}

std::shared_ptr<ProcessGroup::Task> ProcessGroupBKCL::AllGather(
    std::vector<phi::DenseTensor>& in_tensors,
    std::vector<phi::DenseTensor>& out_tensors,
    bool sync_op) {
  PADDLE_ENFORCE_EQ(
      in_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      out_tensors.size(),
      1,
      platform::errors::InvalidArgument(
          "BKCL only support single tensor collective communication."));
  PADDLE_ENFORCE_EQ(
      CheckTensorsInXPUPlace(out_tensors),
      true,
      platform::errors::InvalidArgument("All outputs should be in XPUPlace."));
  return Collective(
      &out_tensors[0],
      in_tensors[0],
      [&](phi::DenseTensor* output,
          const phi::DenseTensor& input,
          BKCLContext_t comm,
          const XPUStream& stream) {
        return bkcl_all_gather(
            comm,
            input.data(),
            input.numel(),
            output->data(),
            platform::ToBKCLDataType(
                framework::TransToProtoVarType(input.type())),
            stream);
      },
      CommType::ALLGATHER,
      sync_op,
      /*use_calc_stream*/ false);
}

}  //  namespace distributed
}  //  namespace paddle