/* Copyright (c) 2020 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 <thread>
#include <vector>

#include "cub/cub.cuh"
#include "cub/util_allocator.cuh"
#if defined(PADDLE_WITH_CUDA)
#include "paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h"
#include "paddle/fluid/platform/cuda_device_guard.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/timer.h"
#include "thrust/pair.h"
#elif defined(PADDLE_WITH_XPU_KP)
#include <xpu/runtime.h>

#include "paddle/fluid/platform/device/xpu/enforce_xpu.h"
#endif

#include "paddle/fluid/framework/fleet/heter_ps/hashtable.h"
#include "paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h"
#include "paddle/fluid/framework/fleet/heter_ps/heter_resource.h"
#include "paddle/fluid/memory/allocation/allocator.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/platform/place.h"

#ifdef PADDLE_WITH_HETERPS

namespace paddle {
namespace framework {

#define TYPEALIGN(ALIGNVAL, LEN) \
  (((uint64_t)(LEN) + ((ALIGNVAL)-1)) & ~((uint64_t)((ALIGNVAL)-1)))

template <typename KeyType,
          typename ValType,
          typename GradType,
          typename GPUAccessor>
class HeterComm {
 public:
  HeterComm(size_t capacity, std::shared_ptr<HeterPsResource> resource);
  HeterComm(size_t capacity,
            std::shared_ptr<HeterPsResource> resource,
            GPUAccessor& gpu_accessor);
  virtual ~HeterComm();
  HeterComm(const HeterComm&) = delete;
  HeterComm& operator=(const HeterComm&) = delete;

  void merge_keys(int gpu_num,
                  const KeyType* d_keys,
                  size_t len,
                  KeyType* d_sorted_keys,
                  KeyType* d_merged_keys,
                  uint32_t* d_restore_idx,
                  size_t& uniq_len);
  void dynamic_merge_grad(int gpu_num,
                          KeyType* d_keys,
                          float* d_grads,
                          size_t len,
                          int& uniq_len,
                          size_t& segment_len,
                          bool enable_segment_merge_grad);
  void segment_merge_grad(int gpu_num,
                          KeyType* d_keys,
                          float* d_grads,
                          const uint32_t* d_index,
                          size_t len,
                          const uint32_t* d_fea_num_info,
                          size_t uniq_len,
                          size_t& segment_len);
  void build_ps(int num,
                KeyType* h_keys,
                ValType* h_vals,
                size_t len,
                size_t chunk_size,
                int stream_num,
                int offset = -1);
  void split_input_to_shard(KeyType* d_keys,
                            int* d_idx_ptr,
                            size_t len,
                            int* left,
                            int* right,
                            int gpu_num);
  void merge_grad(int gpu_num,
                  KeyType* d_keys,
                  GradType* d_grads,
                  size_t len,
                  int& uniq_len);  // NOLINT
  void dynamic_merge_grad(
      int gpu_num, KeyType* d_keys, float* d_grads, size_t len, int& uniq_len);
  void pull_sparse(int num, KeyType* d_keys, float* d_vals, size_t len);
  void build_ps(int num,
                KeyType* h_keys,
                char* pool,
                size_t len,
                size_t feature_value_size,
                size_t chunk_size,
                int stream_num);
  void dump();
  void show_one_table(int gpu_num);
  void show_table_collisions();
  int get_index_by_devid(int devid);

#if defined(PADDLE_WITH_CUDA)
  template <typename Sgd>
  void push_sparse(int num,
                   KeyType* d_keys,
                   float* d_grads,
                   size_t len,
                   Sgd& sgd);  // NOLINT
#elif defined(PADDLE_WITH_XPU_KP)
  void push_sparse(int num, KeyType* d_keys, GradType* d_grads, size_t len);
#endif

  void set_sparse_sgd(const OptimizerConfig& optimizer_config);
  void set_embedx_sgd(const OptimizerConfig& optimizer_config);

  int log2i(int x);

  template <typename DstPlace, typename SrcPlace, typename StreamType>
  void memory_copy(DstPlace dst_place,
                   void* dst,
                   SrcPlace src_place,
                   const void* src,
                   size_t count,
                   StreamType stream = 0);

#if defined(PADDLE_WITH_CUDA)
  template <typename Sgd>
  void push_sparse_multi_node(int num,
                              KeyType* d_keys,
                              GradType* d_grads,
                              size_t len,
                              Sgd& sgd);  // NOLINT

  template <typename Sgd>
  void update_one_table(int num,
                        KeyType* d_keys,
                        GradType* d_grads,
                        size_t len,
                        Sgd& sgd);  // NOLINT

  int gather_one_node_grad(int num,
                           KeyType* d_keys,
                           GradType* d_grads,
                           int len);

  int gather_multi_node_grad(int num,
                             KeyType* d_keys,
                             GradType* d_grads,
                             int len);

  void set_nccl_comm_and_size(const std::vector<ncclComm_t>& inner_comms,
                              const std::vector<ncclComm_t>& inter_comms,
                              int comm_size) {
    nccl_inner_comms_ = inner_comms;
    nccl_inter_comms_ = inter_comms;
    node_size_ = comm_size;
  }

  void set_multi_mf_dim(int multi_mf_dim, int max_mf_dim) {
    multi_mf_dim_ = multi_mf_dim;
    max_mf_dim_ = max_mf_dim;
  }

#endif

  bool need_transfer(int send_id, int receive_id) {
    return ((send_id / 4 != receive_id / 4) && (send_id + 4) % 8 != receive_id);
  }

  // void dump_to_cpu(int index);

  int get_transfer_devid(int send_id) { return (send_id + 4) % 8; }

  void end_pass();
#if defined(PADDLE_WITH_CUDA)
  // dedup
  int dedup_keys_and_fillidx(const int gpu_id,
                             const int total_fea_num,
                             const KeyType* d_keys,   // input
                             KeyType* d_merged_keys,  // output
                             KeyType* d_sorted_keys,
                             uint32_t* d_restore_idx,
                             uint32_t* d_sorted_idx,
                             uint32_t* d_offset,
                             uint32_t* d_merged_cnts,
                             bool filter_zero);
#endif

  struct Node {
    ppStream in_stream;
    ppStream out_stream;
    char* key_storage;
    char* val_storage;
    int sync;
    size_t key_bytes_len;
    size_t val_bytes_len;
    int dev_num;
  };

  struct Path {
    std::vector<Node> nodes_;
  };

  struct CopyTask {
    Path* path;
    int step;
    CopyTask(Path* path_, int step_) : path(path_), step(step_) {}
  };

  struct LocalStorage {
    LocalStorage() {}
    void init(int size, int dev_id) {
      place_ = platform::CUDAPlace(dev_id);
      alloc(size, true);
    }

    void alloc(size_t size, bool force = false) {
      if (force || size > all_keys_mem->size()) {
        all_keys_mem.reset();
        all_grads_mem.reset();
        all_keys_mem = memory::Alloc(place_, size * sizeof(KeyType));
        all_grads_mem = memory::Alloc(place_, size * sizeof(GradType));
        all_keys = reinterpret_cast<KeyType*>(all_keys_mem->ptr());
        all_grads = reinterpret_cast<GradType*>(all_grads_mem->ptr());
      }
      if (force || size > local_keys_mem->size()) {
        local_keys_mem.reset();
        local_grads_mem.reset();
        local_keys_mem = memory::Alloc(place_, size * sizeof(KeyType));
        local_grads_mem = memory::Alloc(place_, size * sizeof(GradType));
        local_keys = reinterpret_cast<KeyType*>(local_keys_mem->ptr());
        local_grads = reinterpret_cast<GradType*>(local_grads_mem->ptr());
      }
    }

#if defined(PADDLE_WITH_CUDA)
    platform::CUDAPlace place_;

#elif defined(PADDLE_WITH_XPU_KP)
    platform::XPUPlace place_;
#endif
    std::shared_ptr<memory::Allocation> all_keys_mem;
    std::shared_ptr<memory::Allocation> all_grads_mem;

    KeyType* all_keys;
    GradType* all_grads;

    std::shared_ptr<memory::Allocation> local_keys_mem;
    std::shared_ptr<memory::Allocation> local_grads_mem;
    KeyType* local_keys;
    GradType* local_grads;
  };

  void init_path();

  template <typename StreamType>
  void sync_stream(const StreamType& stream) {
#if defined(PADDLE_WITH_CUDA)
    PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamSynchronize(stream));
#elif defined(PADDLE_WITH_XPU_KP)
    PADDLE_ENFORCE_XPU_SUCCESS(xpu_wait(stream));
#endif
  }

  template <typename StreamType>
  void create_stream(StreamType* stream) {
#if defined(PADDLE_WITH_CUDA)
    PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamCreate(stream));
#elif defined(PADDLE_WITH_XPU_KP)
    PADDLE_ENFORCE_XPU_SUCCESS(xpu_stream_create(stream));
#endif
  }

  template <typename StreamType>
  void destroy_stream(StreamType stream) {
#if defined(PADDLE_WITH_CUDA)
    PADDLE_ENFORCE_GPU_SUCCESS(cudaStreamDestroy(stream));
#elif defined(PADDLE_WITH_XPU_KP)
    PADDLE_ENFORCE_XPU_SUCCESS(xpu_stream_destroy(stream));
#endif
  }

  void create_storage(int start_index,
                      int end_index,
                      size_t keylen,
                      size_t vallen);
  void destroy_storage(int start_index, int end_index);
  void walk_to_dest(int start_index,
                    int gpu_num,
                    int* h_left,
                    int* h_right,
                    KeyType* src_key,
                    GradType* src_val);
  void walk_to_dest(int start_index,
                    int gpu_num,
                    int* h_left,
                    int* h_right,
                    KeyType* src_key,
                    char* src_val,
                    size_t val_size);
  void walk_to_src(int start_index,
                   int gpu_num,
                   int* h_left,
                   int* h_right,
                   ValType* src_val);
  void walk_to_src(int start_index,
                   int gpu_num,
                   int* h_left,
                   int* h_right,
                   char* src_val,
                   size_t val_size);

 protected:
  void pull_merge_sparse(int num, KeyType* d_keys, float* d_vals, size_t len);
  void pull_normal_sparse(int num, KeyType* d_keys, float* d_vals, size_t len);

  using Table = HashTable<KeyType, ValType>;
  using PtrTable = HashTable<KeyType, float*>;
  std::vector<Table*> tables_;
  std::vector<PtrTable*> ptr_tables_;
  std::shared_ptr<HeterPsResource> resource_;
  std::vector<std::vector<Path>> path_;
  float load_factor_{0.75};
  int block_size_{256};
  std::unique_ptr<HeterCommKernel> heter_comm_kernel_;

  GPUAccessor gpu_accessor_;

 private:
  int topo_aware_{0};
  std::vector<LocalStorage> storage_;
  DynamicGradMerger merger_;
  int feanum_{1800 * 2048};
  int multi_node_{0};
  int node_size_;

#if defined(PADDLE_WITH_CUDA)
  std::vector<ncclComm_t> nccl_inner_comms_;
  std::vector<ncclComm_t> nccl_inter_comms_;
  int multi_mf_dim_{8};
  int max_mf_dim_ = 8;
  std::vector<std::shared_ptr<cub::CachingDeviceAllocator>> allocators_;
#endif
};

}  // end namespace framework
}  // end namespace paddle

#include "paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h"

#endif