/* Copyright (c) 2016 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 <future>  // NOLINT
#include <memory>
#include <mutex>  // NOLINT
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/memory/malloc.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cuda_helper.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#if !defined(__APPLE__) && defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/platform/dynload/nccl.h"
#endif
#include "paddle/fluid/platform/gpu_info.h"
#endif

#ifdef PADDLE_WITH_MKLDNN
#include "mkldnn.hpp"
#include "paddle/fluid/framework/data_layout.h"
#endif

#include <map>
#include "glog/logging.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/stream_callback_manager.h"
#endif
#include "unsupported/Eigen/CXX11/Tensor"

namespace paddle {
namespace platform {

class DeviceContext {
 public:
  virtual ~DeviceContext() PADDLE_MAY_THROW {}
  virtual Place GetPlace() const = 0;

  virtual void Wait() const {}
};

class CPUDeviceContext : public DeviceContext {
 public:
  CPUDeviceContext();
  explicit CPUDeviceContext(CPUPlace place);

  Eigen::DefaultDevice* eigen_device() const;

  Place GetPlace() const override;

 private:
  CPUPlace place_;
  std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};

template <typename Place>
struct DefaultDeviceContextType;

template <>
struct DefaultDeviceContextType<platform::CPUPlace> {
  using TYPE = CPUDeviceContext;
};

#ifdef PADDLE_WITH_CUDA

class EigenCudaStreamDevice;
class CudnnWorkspaceHandle;

class CUDADeviceContext : public DeviceContext {
 public:
  explicit CUDADeviceContext(CUDAPlace place);
  virtual ~CUDADeviceContext();

  /*! \brief  Wait for all operations completion in the stream. */
  void Wait() const override;

  /*! \brief  Return place in the device context. */
  Place GetPlace() const override;

  /*! \brief  Return compute capability in the device context. */
  int GetComputeCapability() const;

  /*! \brief  Return the max physical thread count in the device context */
  int GetMaxPhysicalThreadCount() const;

  /*! \brief  Return the SM count in the device context */
  int GetSMCount() const;

  /*! \brief  Return the Max thread num of block in the device context */
  int GetMaxThreadsPerBlock() const;

  /*! \brief  Return the max grid dim size in the device context */
  dim3 GetCUDAMaxGridDimSize() const;

  /*! \brief  Return eigen device in the device context. */
  Eigen::GpuDevice* eigen_device() const;

  /*! \brief  Call cublas function safely. */
  template <typename Callback>
  inline void CublasCall(Callback&& callback) const {
    cublas_handle_->Call(std::forward<Callback>(callback));
  }

  /*! \brief  Check whether tensor core is supported */
  bool tensor_core_available() const;

  /*! \brief  Call cublas function with Tensor Core safely. If
      Tensor Core is not available, use DEFAULT_MATH instead. */
  template <typename Callback>
  inline void TensorCoreCublasCallIfAvailable(Callback&& callback) const {
    if (cublas_tensor_core_handle_) {
      cublas_tensor_core_handle_->Call(std::forward<Callback>(callback));
    } else {
      cublas_handle_->Call(std::forward<Callback>(callback));
    }
  }

  /*! \brief  Return cudnn  handle in the device context. */
  cudnnHandle_t cudnn_handle() const;

  /*! \brief  Return a cudnn workspace handle to call multiple cudnn
   *  functions without interrupting by other threads.
   *  Once the first cudnn function is called by the handle, a lock
   *  would be acquired to prevent other threads from accessing the
   *  workspace. Once the handle is destructed, the lock would be released.
   *  CudnnWorkspaceHandle is an RAII object to implement thread-safe
   *  sequential cudnn function calls. */
  CudnnWorkspaceHandle cudnn_workspace_handle() const;

  /*! \brief  Return cuda stream in the device context. */
  cudaStream_t stream() const;

#if defined(PADDLE_WITH_NCCL)
  /*! \brief  Return nccl communicators. */
  ncclComm_t nccl_comm() const { return nccl_comm_; }

  /*! \brief  Set nccl communicators. */
  void set_nccl_comm(ncclComm_t comm) { nccl_comm_ = comm; }
#endif

  template <typename Callback>
  void RecordEvent(cudaEvent_t ev, Callback callback) {
    callback();
    PADDLE_ENFORCE_CUDA_SUCCESS(cudaEventRecord(ev, stream_));
  }

  template <typename Callback>
  void AddStreamCallback(Callback&& callback) const {
    callback_manager_->AddCallback(callback);
  }

  void WaitStreamCallback() const { callback_manager_->Wait(); }

 private:
  CUDAPlace place_;

  mutable std::once_flag init_cudnn_;

  std::unique_ptr<Eigen::GpuDevice> eigen_device_;
  std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
  cudaStream_t stream_;

  cudnnHandle_t cudnn_handle_;
  mutable std::mutex cudnn_handle_mtx_;

  std::unique_ptr<CublasHandleHolder> cublas_handle_;
  std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;

#if defined(PADDLE_WITH_NCCL)
  // NCCL communicator (single process version) for NCCL collective operations.
  // NCCL collective operations provides fast collectives over multiple GPUs
  // both within and across nodes.
  // But, this collectives is used for collectives over multiple GPUs within
  // nodes.
  ncclComm_t nccl_comm_{nullptr};
#endif

  int compute_capability_;
  int runtime_version_;
  int driver_version_;
  int multi_process_;
  int max_threads_per_mp_;
  int max_threads_per_block_;
  dim3 max_grid_dim_size_;

  // StreamCallbackManager is thread-safe
  std::unique_ptr<StreamCallbackManager> callback_manager_;

  DISABLE_COPY_AND_ASSIGN(CUDADeviceContext);
};

class CudnnWorkspaceHandle {
 public:
  inline CudnnWorkspaceHandle(const CUDADeviceContext& dev_ctx, std::mutex* mtx)
      : device_context_(dev_ctx), mtx_(mtx) {}

  template <typename Callback>
  inline void RunFunc(Callback&& cudnn_func, size_t required_workspace_bytes) {
    if (required_workspace_bytes > WorkspaceSize()) {
      ReallocWorkspace(required_workspace_bytes);
    }
    VLOG(2) << "Cudnn workspace size at RunFunc: "
            << static_cast<double>(WorkspaceSize()) / (1 << 20) << " MB";
    {
      std::lock_guard<std::mutex> guard(*mtx_);
      cudnn_func(allocation_ ? allocation_->ptr() : nullptr);
    }
  }

  /*! \brief Thread which call RunFuncSync() would release gpu memory after
   *  running the function. Currently this function is only used when cudnn
   *  exhaustive searching and callers have to guarantee that the input function
   *  is host blocking */
  template <typename Callback>
  inline void RunFuncSync(Callback&& cudnn_func,
                          size_t required_workspace_bytes) {
    RunFunc(cudnn_func, required_workspace_bytes);
    ResetWorkspace();
  }

  void ReallocWorkspace(size_t required_workspace_bytes);

  inline void ResetWorkspace() { allocation_ = nullptr; }

  inline size_t WorkspaceSize() {
    if (allocation_ == nullptr) {
      return 0;
    }
    return allocation_->size();
  }

  CudnnWorkspaceHandle(CudnnWorkspaceHandle&&) = default;
  CudnnWorkspaceHandle& operator=(CudnnWorkspaceHandle&&) = delete;

 private:
  memory::allocation::AllocationPtr allocation_;
  const CUDADeviceContext& device_context_;
  std::mutex* mtx_;
};

template <>
struct DefaultDeviceContextType<platform::CUDAPlace> {
  using TYPE = CUDADeviceContext;
};

// Currently, CUDAPinnedDeviceContext is only used to data copying.
class CUDAPinnedDeviceContext : public DeviceContext {
 public:
  CUDAPinnedDeviceContext();
  explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place);

  Place GetPlace() const override;

  Eigen::DefaultDevice* eigen_device() const;

 private:
  CUDAPinnedPlace place_;
  std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};

template <>
struct DefaultDeviceContextType<platform::CUDAPinnedPlace> {
  using TYPE = CUDAPinnedDeviceContext;
};
#endif

#ifdef PADDLE_WITH_MKLDNN
// Following three maps are used to cache MKLDNN primitives.
// There relations are:
// - BlobMap = Map<cur_thread_id, ShapeBlob>
// - ShapeBlob = Map<cur_input_shape_str, KeyBlob>
// - KeyBlob  = Map<blob_name, blob>
// Where:
using KeyBlob = std::unordered_map<std::string, std::shared_ptr<void>>;
using ShapeBlob = std::unordered_map<std::string, std::shared_ptr<KeyBlob>>;
using BlobMap = std::unordered_map<int, std::shared_ptr<ShapeBlob>>;

// default mkldnn session id
constexpr size_t kMKLDNNSessionID_Default = 0;
// mkldnn session id for cache clearing mode
constexpr size_t kMKLDNNSessionID_CacheClearing = -1;

void set_cur_mkldnn_session_id(size_t);
size_t get_cur_mkldnn_session_id(void);
void set_cur_input_shape_str(std::string input_shape_str);
void set_cur_input_shape_cache_capacity(int input_shape_cache_capacity);
void set_cur_paddle_data_layout(framework::DataLayout);
framework::DataLayout get_cur_paddle_data_layout(void);

class MKLDNNDeviceContext : public CPUDeviceContext {
 public:
  explicit MKLDNNDeviceContext(CPUPlace place);

  /* \brief  Get the active engine */
  const mkldnn::engine& GetEngine() const { return engine_; }

  // Remove all entries from the blob map
  void ResetBlobMap() const;

  // Get the ShapeBlob size in cur_mkldnn_session_id.
  size_t GetShapeBlobSize() const;

  // Set data to blob (i.e. name/data pair). Create blob if not existing
  void SetBlob(const std::string& name, std::shared_ptr<void> data) const;

  // Find a saved blob. Return nullptr if not found
  std::shared_ptr<void> GetBlob(const std::string& name) const;

 private:
  mkldnn::engine engine_;
  std::shared_ptr<BlobMap> p_blobmap_;
  std::shared_ptr<std::mutex> p_mutex_;
};
#endif

/*! \brief device context pool singleton */
class DeviceContextPool {
 public:
  explicit DeviceContextPool(const std::vector<platform::Place>& places);

  static DeviceContextPool& Instance() {
    PADDLE_ENFORCE_NOT_NULL(pool, "Need to Create DeviceContextPool first!");
    return *pool;
  }

  /*! \brief  Create should only called by Init function */
  static DeviceContextPool& Init(const std::vector<platform::Place>& places) {
    if (pool == nullptr) {
      pool = new DeviceContextPool(places);
    }
    return *pool;
  }

  static void SetPool(DeviceContextPool* dev_pool) { pool = dev_pool; }

  /*! \brief  Return handle of single device context. */
  platform::DeviceContext* Get(const platform::Place& place);

  template <typename Place>
  const typename DefaultDeviceContextType<Place>::TYPE* GetByPlace(
      const Place& place) {
    return reinterpret_cast<
        const typename DefaultDeviceContextType<Place>::TYPE*>(Get(place));
  }

  size_t size() const { return device_contexts_.size(); }

 private:
  static DeviceContextPool* pool;
  std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>
      device_contexts_;
  DISABLE_COPY_AND_ASSIGN(DeviceContextPool);
};

}  // namespace platform
}  // namespace paddle
