/* Copyright (c) 2019 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

#include <ThreadPool.h>
#include <stdint.h>

#include <atomic>
#include <deque>
#include <map>
#include <memory>
#include <numeric>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>

#include "gflags/gflags.h"
#include "paddle/fluid/distributed/ps/service/communicator/communicator_common.h"
#include "paddle/fluid/distributed/ps/service/coordinator_client.h"
#include "paddle/fluid/distributed/ps/service/ps_client.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"
#include "paddle/phi/core/flags.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/selected_rows_functor.h"

namespace paddle {
namespace distributed {
class PSClient;
struct CommContext;
}  // namespace distributed
}  // namespace paddle

PHI_DECLARE_bool(communicator_is_sgd_optimizer);

namespace paddle {
namespace distributed {

using Scope = framework::Scope;
using Variable = framework::Variable;

template <typename T>
class BlockingQueue {
 public:
  explicit BlockingQueue(size_t capacity) : capacity_(capacity) {
    PADDLE_ENFORCE_GT(capacity_,
                      0,
                      platform::errors::InvalidArgument(
                          "The capacity must be greater than 0."));
  }

  bool Push(const T &elem) {
    std::unique_lock<std::mutex> lock(mutex_);
    WaitForWrite(lock);

    queue_.push_back(elem);

    Notify();
    return true;
  }
  bool WaitForWrite(std::unique_lock<std::mutex> &lock) {  // NOLINT
    while (FullUnlocked()) {
      if (empty_waiters_ != 0) {
        empty_cond_.notify_one();
      }
      full_waiters_++;
      full_cond_.wait(lock);
      full_waiters_--;
    }
    return true;
  }
  bool WaitForRead(std::unique_lock<std::mutex> &lock) {  // NOLINT
    while (EmptyUnlocked()) {
      if (full_waiters_ != 0) {
        full_cond_.notify_one();
      }
      empty_waiters_++;
      empty_cond_.wait(lock);
      empty_waiters_--;
    }
    return true;
  }
  bool EmptyUnlocked() { return queue_.empty(); }

  bool FullUnlocked() { return queue_.size() >= capacity_; }
  void Notify() {
    if (empty_waiters_ != 0 && (!EmptyUnlocked())) {
      empty_cond_.notify_one();
    }
    if (full_waiters_ != 0 && (!FullUnlocked())) {
      full_cond_.notify_one();
    }
  }

  bool Push(T &&elem) {
    std::unique_lock<std::mutex> lock(mutex_);
    WaitForWrite(lock);
    queue_.emplace_back(std::move(elem));

    Notify();
    return true;
  }
  T Pop() {
    std::unique_lock<std::mutex> lock(mutex_);
    WaitForRead(lock);
    T rc(std::move(queue_.front()));
    queue_.pop_front();
    Notify();
    return rc;
  }

  size_t Cap() const {
    std::lock_guard<std::mutex> lock(mutex_);
    return capacity_;
  }

  size_t Size() const {
    std::lock_guard<std::mutex> lock(mutex_);
    return queue_.size();
  }

 private:
  int empty_waiters_ = 0;
  int full_waiters_ = 0;
  std::condition_variable empty_cond_;
  std::condition_variable full_cond_;
  const size_t capacity_;
  std::deque<T> queue_;

  mutable std::mutex mutex_;
};

template <typename T,
          int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenVector = phi::EigenVector<T, MajorType, IndexType>;

template <typename T>
inline void MergeVars(const std::string &var_name,
                      const std::vector<std::shared_ptr<Variable>> &vars,
                      Scope *scope,
                      bool merge_add = true) {
  PADDLE_ENFORCE_NE(
      vars.empty(),
      true,
      platform::errors::InvalidArgument("vector vars are empty."));
  auto cpu_place = platform::CPUPlace();
  auto &var0 = vars[0];
  auto *out_var = scope->Var(var_name);

  if (var0->IsType<phi::DenseTensor>()) {
    auto dims = var0->Get<phi::DenseTensor>().dims();
    VLOG(3) << "merge " << var_name << " LoDTensor dims " << dims
            << "; merge add: " << merge_add;
    // init output tensor
    auto *out_t = out_var->GetMutable<phi::DenseTensor>();
    out_t->mutable_data<T>(dims, cpu_place);
    // check the input dims
    for (auto &var : vars) {
      auto &var_t = var->Get<phi::DenseTensor>();
      PADDLE_ENFORCE_EQ(
          var_t.dims(),
          dims,
          platform::errors::InvalidArgument("vars should have the same dims."));
    }

    // set output tensor to 0.
    phi::CPUContext cpu_ctx;
    phi::funcs::SetConstant<phi::CPUContext, T> constant_functor;
    constant_functor(cpu_ctx, out_t, static_cast<T>(0));
    // sum all vars to out
    auto result = EigenVector<T>::Flatten(*out_t);
    for (auto &var : vars) {
      auto &in_t = var->Get<phi::DenseTensor>();
      auto in = EigenVector<T>::Flatten(in_t);
      result.device(*cpu_ctx.eigen_device()) = result + in;
    }
    if (!merge_add) {
      result.device(*cpu_ctx.eigen_device()) =
          result / static_cast<T>(vars.size());
    }
  } else if (var0->IsType<phi::SelectedRows>()) {
    auto &slr0 = var0->Get<phi::SelectedRows>();
    auto *out_slr = out_var->GetMutable<phi::SelectedRows>();
    out_slr->mutable_rows()->clear();
    out_slr->mutable_value()->mutable_data<T>({{}}, cpu_place);
    std::vector<const phi::SelectedRows *> inputs;
    inputs.reserve(vars.size());
    for (auto &var : vars) {
      inputs.push_back(&var->Get<phi::SelectedRows>());
    }
    phi::CPUContext dev_ctx;
    if (merge_add) {
      phi::funcs::scatter::MergeAdd<phi::CPUContext, T> merge_add;
      merge_add(dev_ctx, inputs, out_slr);
    } else {
      phi::funcs::scatter::MergeAverage<phi::CPUContext, T> merge_average;
      merge_average(dev_ctx, inputs, out_slr);
    }

    VLOG(3) << "merge " << var_name << " SelectedRows height: " << slr0.height()
            << " dims: " << slr0.value().dims() << "; merge add: " << merge_add;
  } else {
    PADDLE_THROW(platform::errors::InvalidArgument("unsupported var type: %s!",
                                                   var0->Type()));
  }
}

using RpcCtxMap = std::unordered_map<std::string, CommContext>;
using RecvCtxMap = std::unordered_map<uint64_t, std::vector<std::string>>;
using SparseValue = std::unordered_map<int64_t, std::vector<float>>;

class Communicator {
 public:
  Communicator();

  explicit Communicator(const std::map<std::string, std::string> &envs_) {
    VLOG(3) << "Communicator Init Envs";
    for (auto &iter : envs_) {
      envs[iter.first] = iter.second;
      VLOG(3) << iter.first << ": " << iter.second;
    }
    if (!envs.empty()) {
      barrier_table_id_ = std::stoi(envs.at("barrier_table_id"));
      trainer_id_ = std::stoi(envs.at("trainer_id"));
      trainers_ = std::stoi(envs.at("trainers"));
    }
  }

  virtual void InitBrpcClient(const std::string &dist_desc,
                              const std::vector<std::string> &host_sign_list);

  virtual std::vector<uint64_t> GetClientInfo();

  virtual int SetClients(std::vector<uint64_t> &host_sign_list);  // NOLINT

  // 1. recv dense param
  virtual void RpcRecvDense(const std::vector<std::string> &varnames,
                            int table_id,
                            Scope *scope);
  // 2. send dense param
  virtual void RpcSendDenseParam(const std::vector<std::string> &varnames,
                                 int table_id,
                                 const Scope &scope);
  // 3. send dense grad
  virtual void RpcSendDense(const CommContext &ctx, const Scope &scope);
  // 4. send sparse grad
  virtual void RpcSendSparse(const std::string &var_name,
                             int table_id,
                             const Scope &scope);
  // 5. send sparse param
  virtual void RpcSendSparseParam(const std::string &varname,
                                  int table_id,
                                  const Scope &scope);
  // 6. recv sparse param
  virtual void RpcRecvSparse(const std::string &varname,
                             int table_id,
                             Scope *scope);
  // 7. send global step
  virtual void SendGlobalStep(const CommContext &ctx,
                              int batches,
                              Scope *send_scope);

  virtual std::unordered_map<uint32_t, std::string> QueryFLClientsInfo() {
    return {};
  }
  virtual void SaveFLStrategy(
      const std::unordered_map<uint32_t, std::string> &fl_strategy UNUSED) {}
  virtual void StartCoordinator(
      const std::string &self_endpoint UNUSED,
      const std::vector<std::string> &trainer_endpoints UNUSED) {}

  virtual ~Communicator() {}
  virtual void RpcProfilerControl();

  virtual void InitParams(const RecvCtxMap &recv_varname_to_ctx);

  // note: only for pull dense param first before training
  virtual void PullDense(const RecvCtxMap &recv_varname_to_ctx);

  virtual void Start() = 0;

  virtual void Stop() = 0;

  virtual bool IsRunning() { return running_; }

  virtual void Clean() {}

  virtual bool Check(const int table_id) = 0;
  virtual bool Check(const std::vector<std::string> &var_tables) = 0;

  virtual void Send(const std::vector<std::string> &var_names,
                    const framework::Scope &scope) = 0;

  virtual void RecvNoBarrier() {}

  virtual void Barrier() {}

  virtual void BarrierWithTable(uint32_t barrier_type) {
    auto rets = _worker_ptr->Barrier(barrier_table_id_, barrier_type);
    rets.wait();
    int status = rets.get();
    PADDLE_ENFORCE_EQ(status,
                      0,
                      platform::errors::InvalidArgument(
                          "The ret status must be 0 when barrier with table"));
  }

  virtual void CreateC2CConnection(int pserver_timeout_ms,
                                   int pserver_connect_timeout_ms,
                                   int max_retry) {
    _worker_ptr->CreateClient2ClientConnection(
        pserver_timeout_ms, pserver_connect_timeout_ms, max_retry);
  }

  virtual void BarrierTriggerDecrement() {}

  virtual void BarrierTriggerReset(int init_counter UNUSED) {}

  virtual void InitEnvs() = 0;

  virtual void InitImpl(const RpcCtxMap &send_varname_to_ctx UNUSED,
                        const RecvCtxMap &recv_varname_to_ctx UNUSED,
                        Scope *recv_scope UNUSED) {}

  static Communicator *GetInstance() { return communicator_.get(); }

  static std::shared_ptr<Communicator> GetInstantcePtr() {
    return communicator_;
  }

  template <typename T>
  static Communicator *InitInstance(
      const RpcCtxMap &send_ctx,
      const RecvCtxMap &recv_ctx,
      const std::string &dist_desc,
      const std::vector<std::string> &host_sign_list,
      Scope *recv_scope,
      const std::map<std::string, std::string> &envs) {
    std::call_once(init_flag_,
                   &Communicator::InitWithRpcCtx<T>,
                   send_ctx,
                   recv_ctx,
                   dist_desc,
                   host_sign_list,
                   recv_scope,
                   std::ref(envs));
    return communicator_.get();
  }

  // called by InitInstance.
  template <typename T>
  static void InitWithRpcCtx(const RpcCtxMap &send_ctx,
                             const RecvCtxMap &recv_ctx,
                             const std::string &dist_desc,
                             const std::vector<std::string> &host_sign_list,
                             Scope *recv_scope,
                             const std::map<std::string, std::string> &envs) {
    VLOG(0) << "Communicator type is: " << typeid(T).name();
    if (communicator_.get() == nullptr) {
      communicator_.reset(new T(std::ref(envs)));
      communicator_->InitEnvs();
      communicator_->InitBrpcClient(dist_desc, host_sign_list);
      communicator_->InitImpl(send_ctx, recv_ctx, recv_scope);
    }
  }

  PSClient *GetPsClient() { return _worker_ptr.get(); }

  RecvCtxMap &GetRecvCtxMap() { return recv_varname_to_ctx_; }

  std::shared_ptr<PSClient> _worker_ptr;  // pointer to worker

 protected:
  bool running_ = false;
  bool waiting_ = true;
  bool flushing_ = false;
  bool do_server_profiler_ = false;
  static std::shared_ptr<Communicator> communicator_;
  static std::once_flag init_flag_;

  std::unordered_map<std::string, std::string> envs;

  // 计算每个shard 对 dense的存储量
  inline uint32_t DenseDimPerShard(uint32_t dense_dim_total,
                                   uint32_t shard_num) {
    return dense_dim_total / shard_num + 1;
  }

  void InitGFlag(const std::string &gflags);
  paddle::distributed::PSParameter _ps_param;
  paddle::distributed::PaddlePSEnvironment _ps_env;
  int servers_ = 0;
  int trainers_;
  int trainer_id_ = 0;
  int barrier_table_id_ = 0;
  RpcCtxMap send_varname_to_ctx_;
  RecvCtxMap recv_varname_to_ctx_;

  Scope *recv_scope_;  // should be global scope
  std::unique_ptr<Scope> xpu_temp_scope_;
  std::atomic<uint32_t> _async_call_num{0};
};

class AsyncCommunicator : public Communicator {
 public:
  AsyncCommunicator() : Communicator() {}

  explicit AsyncCommunicator(const std::map<std::string, std::string> &envs)
      : Communicator(envs) {}

  ~AsyncCommunicator();

  void InitEnvs() {
    independent_recv_ = static_cast<bool>(
        std::stoi(envs.at("communicator_independent_recv_thread")));
    min_send_grad_num_before_recv_ =
        std::stoi(envs.at("communicator_min_send_grad_num_before_recv"));
    thread_pool_size_ = std::stoi(envs.at("communicator_thread_pool_size"));
    max_merge_var_num_ = std::stoi(envs.at("communicator_max_merge_var_num"));
    send_wait_times_ = std::stoi(envs.at("communicator_send_wait_times"));
    send_queue_size_ = std::stoi(envs.at("communicator_send_queue_size"));
    need_global_step_ =
        static_cast<bool>(std::stoi(envs.at("need_global_step")));
  }

  void Start() override;

  void Stop() override;

  void InitImpl(const RpcCtxMap &send_varname_to_ctx,
                const RecvCtxMap &recv_varname_to_ctx,
                Scope *recv_scope) override;

  virtual void MainThread();
  virtual void RecvThread();

  virtual bool Check(const int table_id);
  virtual bool Check(const std::vector<std::string> &var_tables);

  void Send(const std::vector<std::string> &var_names,
            const framework::Scope &scope) override;

  virtual void SendByCommunicator();

  virtual void RecvByCommunicator();

  virtual void RecvNoBarrier();

  virtual int BatchesCounter() { return 1; }

  virtual void BarrierSend() {}

  virtual void BarrierRecv() {}

  virtual void BarrierWeakUp() {}

  void PushDensePostProcessing();

  void PullSparseToTensorSync(
      const uint64_t table_id,
      int fea_dim,
      uint64_t padding_id,
      platform::Place place,
      bool is_training,
      std::vector<const phi::DenseTensor *> *inputs,  // NOLINT
      std::vector<phi::DenseTensor *> *outputs);      // NOLINT

  void PushSparseFromTensorAsync(const uint64_t table_id,
                                 int fea_dim,
                                 uint64_t padding_id,
                                 platform::Place place,
                                 std::vector<const phi::DenseTensor *> *inputs,
                                 const phi::DenseTensor *shows,
                                 const phi::DenseTensor *clicks,
                                 std::vector<phi::DenseTensor *> *outputs);

 protected:
  std::unordered_map<std::string,
                     std::shared_ptr<BlockingQueue<std::shared_ptr<Variable>>>>
      send_varname_to_queue_;
  std::unique_ptr<::ThreadPool> send_threadpool_{nullptr};

  int min_send_grad_num_before_recv_;
  int thread_pool_size_;
  int max_merge_var_num_;
  int send_wait_times_;
  int send_queue_size_;
  bool need_global_step_ = false;
  bool independent_recv_ = true;
  int parallel_task_nums_ = 0;
  int32_t sleep_seconds_before_fail_exit_;

  std::unique_ptr<std::thread> main_thread_{nullptr};
  std::unique_ptr<std::thread> recv_thread_{nullptr};

  std::unique_ptr<Scope> send_scope_;  // an independent scope
  std::atomic_uint grad_num_{0};  // the num of gradient sent since last recv
};

class HalfAsyncCommunicator : public AsyncCommunicator {
 public:
  HalfAsyncCommunicator() {}

  explicit HalfAsyncCommunicator(const std::map<std::string, std::string> &envs)
      : AsyncCommunicator(envs) {}

  void InitEnvs() {
    // enfore to recv after send
    independent_recv_ = false;
    min_send_grad_num_before_recv_ = 0;
    thread_pool_size_ = std::stoi(envs.at("communicator_thread_pool_size"));
    max_merge_var_num_ = std::stoi(envs.at("communicator_max_merge_var_num"));
    send_wait_times_ = std::stoi(envs.at("communicator_send_wait_times"));
    send_queue_size_ = std::stoi(envs.at("communicator_send_queue_size"));
    need_global_step_ =
        static_cast<bool>(std::stoi(envs.at("need_global_step")));

    VLOG(1) << "HalfAsyncCommunicator Initialized";
  }

  void MainThread() override;

  void SendByCommunicator() override;

  void Clean() override;

  void Barrier() override;

  void BarrierTriggerDecrement() override;

  void BarrierTriggerReset(int initial_val) override;

  int BatchesCounter();

  void BarrierWeakUp();

 protected:
  // mutex for Wait for barrier
  std::mutex barrier_mutex_;
  std::condition_variable barrier_cond_;
  std::atomic<int64_t> barrier_trigger_{0};
  std::atomic<int64_t> barrier_counter_{0};
};

class SyncCommunicator : public HalfAsyncCommunicator {
 public:
  SyncCommunicator() : HalfAsyncCommunicator() {}

  explicit SyncCommunicator(const std::map<std::string, std::string> &envs)
      : HalfAsyncCommunicator(envs) {}

  void InitEnvs() {
    // enforce to recv after send
    independent_recv_ = false;
    min_send_grad_num_before_recv_ = 0;
    max_merge_var_num_ = std::stoi(envs.at("communicator_max_merge_var_num"));
    send_wait_times_ = std::stoi(envs.at("communicator_send_wait_times"));
    thread_pool_size_ = std::stoi(envs.at("communicator_thread_pool_size"));
    send_queue_size_ = std::stoi(envs.at("communicator_send_queue_size"));
    need_global_step_ =
        static_cast<bool>(std::stoi(envs.at("need_global_step")));

    VLOG(1) << "SyncCommunicator Initialized";
  }

  void BarrierSend();

  void BarrierRecv();

 private:
  std::vector<std::string> pserver_endpoints_{};
};

class GeoCommunicator : public AsyncCommunicator {
 public:
  GeoCommunicator() : AsyncCommunicator() {}

  explicit GeoCommunicator(const std::map<std::string, std::string> &envs)
      : AsyncCommunicator(envs) {}

  void InitParams(const RecvCtxMap &recv_varname_to_ctx) override;
  void InitDense(std::vector<std::string> &varnames, int table_id);  // NOLINT
  void InitSparse(const std::string &var_name, int table_id);

  void SendDense(const CommContext &send_ctx);
  void RecvDense(const CommContext &send_ctx);

  std::vector<int64_t> MergeSparseIds(const std::string &varname);
  void SendSparse(const std::string &varname,
                  std::vector<int64_t> &sparse_ids,  // NOLINT
                  int table_id,
                  int ep_idx);
  void RecvSparse(const std::string &varname, int table_id, int ep_idx);

  void MainThread() override;

  virtual void InitEnvs() {
    independent_recv_ = false;
    min_send_grad_num_before_recv_ = 0;
    send_wait_times_ = std::stoi(envs.at("communicator_send_wait_times"));
    thread_pool_size_ = std::stoi(envs.at("communicator_thread_pool_size"));
    // id_queue's size
    max_merge_var_num_ = std::stoi(envs.at("communicator_max_merge_var_num"));
    send_queue_size_ = max_merge_var_num_;
    VLOG(1) << "GeoCommunicator Initialized";
  }

  void InitImpl(const RpcCtxMap &send_varname_to_ctx,
                const RecvCtxMap &recv_varname_to_ctx,
                Scope *recv_scope) override;

  void Send(const std::vector<std::string> &var_names,
            const framework::Scope &scope) override;

  void SendByCommunicator() { return; }

  void RecvByCommunicator() override { return; }

  inline std::string GradToParam(const std::string var_name) {
    std::string param_name = var_name.substr(0, var_name.size() - 5);
    return param_name;
  }

  inline std::string SplitedGradToParam(const std::string delta_name) {
    // delta_name: emb.delta0
    auto pos = delta_name.find(".block");
    std::string param_name = delta_name.substr(0, pos);
    return param_name;
  }

 public:
  // parameter for delta calc and send
  std::shared_ptr<Scope> delta_scope_;
  // parameter for storage the pserver param after last recv
  std::shared_ptr<Scope> old_scope_;
  // parameter on pserver
  std::shared_ptr<Scope> pserver_scope_;

  std::unordered_map<
      std::string,
      paddle::framework::Channel<std::shared_ptr<std::vector<int64_t>>>>
      sparse_id_queues_;
};

class FLCommunicator : public GeoCommunicator {
 public:
  FLCommunicator() : GeoCommunicator() {}

  ~FLCommunicator() {
    is_running_ = false;
    async_send_thread_->join();
  }

  explicit FLCommunicator(const std::map<std::string, std::string> &envs)
      : GeoCommunicator(envs) {}

  void InitEnvs() override {}

  virtual void InitBrpcClient(const std::string &dist_desc,
                              const std::vector<std::string> &host_sign_list);

  void InitImpl(const RpcCtxMap &send_varname_to_ctx UNUSED,
                const RecvCtxMap &recv_varname_to_ctx UNUSED,
                Scope *recv_scope UNUSED) {}

  void StartCoordinatorClient(
      const std::vector<std::string> &trainer_endpoints);

  void StartCoordinatorServer();

  void StartCoordinator(
      const std::string &self_endpoint,
      const std::vector<std::string> &trainer_endpoints) override;

  std::unordered_map<uint32_t, std::string> QueryFLClientsInfo();
  void SaveFLStrategy(
      const std::unordered_map<uint32_t, std::string> &fl_strategy);

  void SendThreadAsync();
  void RpcSendFLStrategy();

 private:
  int thread_pool_size_ = 1;
  bool is_running_ = true;
  PaddlePSEnvironment ps_env_;
  std::shared_ptr<CoordinatorClient> coordinator_client_ptr_{nullptr};
  std::unique_ptr<std::thread> async_send_thread_{nullptr};
};

}  // namespace distributed
}  // namespace paddle
