device_context.h

/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
Copyright (c) 2022 NVIDIA Corporation. 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 <functional>
#include <future>  // NOLINT
#include <memory>
#include <mutex>  // NOLINT
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>

#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/platform/device/gpu/gpu_types.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/backends/custom/custom_context.h"
#include "paddle/phi/backends/gpu/gpu_decls.h"
#include "paddle/phi/core/device_context.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/device/gpu/gpu_helper.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cublasLt.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/dynload/cusolver.h"
#include "paddle/fluid/platform/dynload/cusparse.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#if !defined(__APPLE__) && defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/platform/dynload/nccl.h"
#endif
#include "paddle/fluid/platform/device/gpu/gpu_info.h"
#endif

#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/platform/device/gpu/gpu_helper.h"  // NOLINT
#include "paddle/fluid/platform/dynload/miopen.h"
#include "paddle/fluid/platform/dynload/rocblas.h"
#include "paddle/phi/backends/gpu/gpu_context.h"  // NOLINT
#if !defined(__APPLE__) && defined(PADDLE_WITH_RCCL)
#include "paddle/fluid/platform/dynload/rccl.h"
#endif
#include "paddle/fluid/platform/device/gpu/gpu_info.h"  // NOLINT
#endif

#if defined(PADDLE_WITH_XPU_BKCL)
#include "xpu/bkcl.h"
#endif

#ifdef PADDLE_WITH_MKLDNN
#include "dnnl.hpp"  // NOLINT
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/phi/backends/onednn/onednn_context.h"
#endif

#include <map>

#include "glog/logging.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
#include "paddle/fluid/platform/stream/cuda_stream.h"
#endif
#ifdef PADDLE_WITH_ASCEND_CL
#include "paddle/fluid/platform/device/npu/enforce_npu.h"
#include "paddle/fluid/platform/device/npu/npu_stream.h"
#endif

#include "paddle/phi/backends/device_ext.h"
#include "paddle/phi/backends/stream.h"

#if !defined(PADDLE_WITH_XPU_KP) || defined(__xpu_on_host__)
#include "unsupported/Eigen/CXX11/Tensor"
#endif

namespace Eigen {
struct DefaultDevice;
struct GpuDevice;
}  // namespace Eigen

#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/platform/device/xpu/xpu_header.h"
#include "paddle/fluid/platform/device/xpu/xpu_info.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#endif

#ifdef PADDLE_WITH_ASCEND_CL
#include "acl/acl.h"
#include "paddle/fluid/platform/device/npu/npu_info.h"
#endif

namespace paddle {
namespace platform {

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
/*Set the value of the global variable allow_tf32_cublas*/
void SetAllowTF32Cublas(bool active);
/*Get the global variable allow_tf32_cublas value*/
bool AllowTF32Cublas();
extern bool allow_tf32_cudnn;
/*Set the value of the global variable allow_tf32_cudnn*/
void SetAllowTF32Cudnn(bool active);
/*Get the global variable allow_tf32_cudnn value*/
bool AllowTF32Cudnn();
#endif  // PADDLE_WITH_CUDA

enum DeviceType {
  CPU = 0,
  CUDA = 1,
  XPU = 2,
  NPU = 3,
  IPU = 4,
  MLU = 5,

  MAX_DEVICE_TYPES = 6,
};

DeviceType Place2DeviceType(const platform::Place& place);

constexpr DeviceType kCPU = DeviceType::CPU;
constexpr DeviceType kCUDA = DeviceType::CUDA;
constexpr DeviceType kXPU = DeviceType::XPU;
constexpr DeviceType kNPU = DeviceType::NPU;
constexpr DeviceType kIPU = DeviceType::IPU;
constexpr DeviceType kMLU = DeviceType::MLU;

using DeviceContext = phi::DeviceContext;

template <typename Place>
struct DefaultDeviceContextType;

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

// Graphcore IPU
#ifdef PADDLE_WITH_IPU
class IPUDeviceContext : public DeviceContext {
 public:
  IPUDeviceContext() = delete;
  explicit IPUDeviceContext(IPUPlace place);
  virtual ~IPUDeviceContext();
  Eigen::DefaultDevice* eigen_device() const { return nullptr; }
  const Place& GetPlace() const override;
  /*! \brief  Wait for all operations completion in the stream. */
  void Wait() const override;

 private:
  IPUPlace place_;
};
template <>
struct DefaultDeviceContextType<platform::IPUPlace> {
  using TYPE = IPUDeviceContext;
};
#endif

#ifdef PADDLE_WITH_MLU
class MLUDeviceContext;

template <>
struct DefaultDeviceContextType<platform::MLUPlace>;
#endif

#ifdef PADDLE_WITH_XPU
namespace xpu = baidu::xpu::api;
class XPUDeviceContext : public phi::XPUContext {
 public:
  XPUDeviceContext();
  explicit XPUDeviceContext(XPUPlace place);
  virtual ~XPUDeviceContext();
  Eigen::DefaultDevice* eigen_device() const { return nullptr; }
  xpuStream stream() const { return XPUContext::x_context()->xpu_stream; }
};

template <>
struct DefaultDeviceContextType<platform::XPUPlace> {
  using TYPE = XPUDeviceContext;
};
#endif

#ifdef PADDLE_WITH_ASCEND_CL
class NPUDeviceContext : public DeviceContext {
 public:
  explicit NPUDeviceContext(NPUPlace place);
  virtual ~NPUDeviceContext();
  Eigen::DefaultDevice* eigen_device() const { return nullptr; }
  const Place& GetPlace() const override;
  aclrtContext context() const;

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

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

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

  void WaitStreamCallback() const { return stream_->WaitCallback(); }

#if defined(PADDLE_WITH_ASCEND_CL)
  /*! \brief  Return hccl communicators. */
  HcclComm hccl_comm() const { return hccl_comm_; }

  /*! \brief  Set hccl communicators. */
  void set_hccl_comm(HcclComm comm) { hccl_comm_ = comm; }
#endif

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

  // void WaitStreamCallback() const { return stream_->WaitCallback(); }

 private:
  NPUPlace place_;
  aclrtContext context_;

#ifdef PADDLE_WITH_ASCEND_CL
  // HCCLContext_t hccl_context_;
  HcclComm hccl_comm_{nullptr};
#endif

  // Need to be the same with other DeviceContext,
  // Eventhough eigen_device_ is not used in NPU
  // NOTE(zhiqiu): why need?
  std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
  std::shared_ptr<stream::NPUStream> stream_;

  DISABLE_COPY_AND_ASSIGN(NPUDeviceContext);
};

template <>
struct DefaultDeviceContextType<platform::NPUPlace> {
  using TYPE = NPUDeviceContext;
};

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

  const Place& GetPlace() const override;

  Eigen::DefaultDevice* eigen_device() const;

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

template <>
struct DefaultDeviceContextType<platform::NPUPinnedPlace> {
  using TYPE = NPUPinnedDeviceContext;
};

#endif

#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
class CudnnWorkspaceHandle;
class EigenCudaStreamDevice;

class CUDAContext {
 public:
  CUDAContext() = default;
  explicit CUDAContext(
      const CUDAPlace& place,
      const stream::Priority& priority = stream::Priority::kNormal,
      const stream::StreamFlag& flag = stream::StreamFlag::kDefaultFlag);

  ~CUDAContext();

  const CUDAPlace& Place() const { return place_; }

  const std::unique_ptr<Eigen::GpuDevice>& EigenDevice() const {
    return eigen_device_;
  }

  const std::unique_ptr<EigenCudaStreamDevice>& EigenStream() const {
    return eigen_stream_;
  }

  const std::unique_ptr<stream::CUDAStream>& Stream() const { return stream_; }

  stream::CUDAStream* SetStream(stream::CUDAStream* new_stream_ptr) {
    auto* old_stream_ptr = stream_.release();
    stream_.reset(new_stream_ptr);
    return old_stream_ptr;
  }

  void SetStream(gpuStream_t stream);

  const gpuStream_t& RawStream() { return stream_->raw_stream(); }

#ifdef PADDLE_WITH_HIP
  const miopenHandle_t& CudnnHandle() const { return cudnn_handle_; }
#else
  const cudnnHandle_t& CudnnHandle() const { return cudnn_handle_; }
#endif

#ifndef PADDLE_WITH_HIP
  const cusolverDnHandle_t& CusolverDnHandle() const {
    return cusolver_dn_handle_;
  }
#endif

  const std::unique_ptr<CublasHandleHolder>& CublasHandle() const {
    return cublas_handle_;
  }

  const std::unique_ptr<CublasHandleHolder>& CublasTensorCoreHandle() const {
    return cublas_tensor_core_handle_;
  }

#ifndef PADDLE_WITH_HIP
#if CUDA_VERSION >= 11060
  const std::unique_ptr<CublasLtHandleHolder>& CublasLtHandle() const {
    return cublaslt_handle_;
  }
#endif

  const std::unique_ptr<CusparseHandleHolder>& CusparseHandle() const {
    return cusparse_handle_;
  }
#endif

  /*! \brief  Call cublas function safely. */
  inline void CublasCall(
      const std::function<void(blasHandle_t)>& callback) const {
    if (cublas_tf32_tensor_core_handle_) {
      cublas_tf32_tensor_core_handle_->Call(callback);
    } else {
      cublas_handle_->Call(callback);
    }
  }

#ifndef PADDLE_WITH_HIP
#if CUDA_VERSION >= 11060
  /*! \brief  Call cublasLt function safely. */
  inline void CublasLtCall(
      const std::function<void(blasLtHandle_t)>& callback) const {
    cublaslt_handle_->Call(callback);
  }
#endif

  /*! \brief  Call cusparse function safely. */
  inline void CusparseCall(
      const std::function<void(phi::sparseHandle_t)>& callback) const {
    cusparse_handle_->Call(callback);
  }
#endif

  /*! \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. */
  inline void TensorCoreCublasCallIfAvailable(
      const std::function<void(blasHandle_t)>& callback) const {
    if (cublas_tensor_core_handle_) {
      cublas_tensor_core_handle_->Call(callback);
    } else {
      cublas_handle_->Call(callback);
    }
  }

 private:
  void InitEigenContext();

#ifdef PADDLE_WITH_HIP
  void InitCuBlasContext() {
    cublas_handle_.reset(new CublasHandleHolder(RawStream()));
  }
#else
  void InitCuBlasContext() {
    cublas_handle_.reset(
        new CublasHandleHolder(RawStream(), CUBLAS_DEFAULT_MATH));
    if (TensorCoreAvailable()) {
#if CUDA_VERSION >= 9000
      cublas_tensor_core_handle_.reset(
          new CublasHandleHolder(RawStream(), CUBLAS_TENSOR_OP_MATH));
#if CUDA_VERSION >= 11000
      cublas_tf32_tensor_core_handle_.reset(
          new CublasHandleHolder(RawStream(), CUBLAS_TF32_TENSOR_OP_MATH));
#endif  // CUDA_VERSION >= 11000
#endif  // CUDA_VERSION >= 9000
    }
  }
#endif

#ifndef PADDLE_WITH_HIP
#if CUDA_VERSION >= 11060
  void InitCuBlasLtContext() {
    cublaslt_handle_.reset(new CublasLtHandleHolder());
  }
#endif

  void InitCuSparseContext() {
    cusparse_handle_.reset(new CusparseHandleHolder(RawStream()));
  }
#endif

  void InitCuDNNContext() {
    if (dynload::HasCUDNN()) {
#ifdef PADDLE_WITH_HIP
      size_t miopen_major, miopen_minor, miopen_patch;
      PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenGetVersion(
          &miopen_major, &miopen_minor, &miopen_patch));
      auto local_miopen_version =
          (miopen_major * 1000 + miopen_minor * 10 + miopen_patch) / 10;
      auto compile_miopen_version = MIOPEN_VERSION / 10;
      if (local_miopen_version < static_cast<size_t>(compile_miopen_version)) {
        LOG_FIRST_N(WARNING, 1)
            << "WARNING: device: " << place_.device
            << ". The installed Paddle is compiled with MIOPEN "
            << compile_miopen_version / 100 << "."
            << compile_miopen_version % 100
            << ", but MIOPEN version in your machine is "
            << local_miopen_version / 100 << "." << local_miopen_version % 100
            << ", which may cause serious incompatible bug. "
            << "Please recompile or reinstall Paddle with compatible MIOPEN "
               "version.";
      }
      PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenCreate(&cudnn_handle_));
      PADDLE_ENFORCE_GPU_SUCCESS(
          dynload::miopenSetStream(cudnn_handle_, RawStream()));
#else
      auto local_cudnn_version = dynload::cudnnGetVersion() / 100;
      auto compile_cudnn_version = CUDNN_VERSION / 100;
      if (local_cudnn_version < static_cast<size_t>(compile_cudnn_version)) {
        LOG_FIRST_N(WARNING, 1)
            << "WARNING: device: " << place_.device
            << ". The installed Paddle is compiled with CUDNN "
            << compile_cudnn_version / 10 << "." << compile_cudnn_version % 10
            << ", but CUDNN version in your machine is "
            << local_cudnn_version / 10 << "." << local_cudnn_version % 10
            << ", which may cause serious incompatible bug. "
            << "Please recompile or reinstall Paddle with compatible CUDNN "
               "version.";
      }
      PADDLE_RETRY_CUDA_SUCCESS(dynload::cudnnCreate(&cudnn_handle_));
      PADDLE_RETRY_CUDA_SUCCESS(
          dynload::cudnnSetStream(cudnn_handle_, RawStream()));
#endif
    } else {
      cudnn_handle_ = nullptr;
    }
  }

#ifndef PADDLE_WITH_HIP
  void InitCuSolverContext() {
    PADDLE_RETRY_CUDA_SUCCESS(dynload::cusolverDnCreate(&cusolver_dn_handle_));
    PADDLE_RETRY_CUDA_SUCCESS(
        dynload::cusolverDnSetStream(cusolver_dn_handle_, RawStream()));
  }
#endif

  void DestoryCuDNNContext() {
    if (cudnn_handle_) {
#ifdef PADDLE_WITH_HIP
      PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenDestroy(cudnn_handle_));
#else
      PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnDestroy(cudnn_handle_));
#endif
    }
    cudnn_handle_ = nullptr;
  }

  void DestoryCuBlasContext() {
    cublas_handle_.reset();
    cublas_tensor_core_handle_.reset();
    cublas_tf32_tensor_core_handle_.reset();
  }

#ifndef PADDLE_WITH_HIP
#if CUDA_VERSION >= 11060
  void DestoryCuBlasLtContext() { cublaslt_handle_.reset(); }
#endif

  void DestoryCuSparseContext() { cusparse_handle_.reset(); }
#endif

#ifndef PADDLE_WITH_HIP
  void DestoryCuSolverContext() {
    if (cusolver_dn_handle_) {
      PADDLE_ENFORCE_GPU_SUCCESS(
          dynload::cusolverDnDestroy(cusolver_dn_handle_));
    }
  }
#endif

  CUDAPlace place_;
  std::unique_ptr<Eigen::GpuDevice> eigen_device_;
  std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
  std::unique_ptr<stream::CUDAStream> stream_;
#ifdef PADDLE_WITH_HIP
  miopenHandle_t cudnn_handle_;
#else
  cudnnHandle_t cudnn_handle_;
#endif
  std::unique_ptr<CublasHandleHolder> cublas_handle_;
  std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
  std::unique_ptr<CublasHandleHolder> cublas_tf32_tensor_core_handle_;
#ifndef PADDLE_WITH_HIP
#if CUDA_VERSION >= 11060
  std::unique_ptr<CublasLtHandleHolder> cublaslt_handle_;
#endif
  cusolverDnHandle_t cusolver_dn_handle_;
  std::unique_ptr<CusparseHandleHolder> cusparse_handle_;
#endif
  DISABLE_COPY_AND_ASSIGN(CUDAContext);
};

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

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

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

  /*! \brief  Call cublas function safely. */
  inline void CublasCall(
      const std::function<void(blasHandle_t)>& callback) const {
    if (!thread_ctx_.count(this)) {
      phi::GPUContext::CublasCall(callback);
      return;
    }
    return context()->CublasCall(callback);
  }

#ifndef PADDLE_WITH_HIP
  /*! \brief  Call cusparse function safely. */
  inline void CusparseCall(
      const std::function<void(phi::sparseHandle_t)>& callback) const {
    if (!thread_ctx_.count(this)) {
      phi::GPUContext::CusparseCall(callback);
      return;
    }
    context()->CusparseCall(callback);
  }
#endif

  /*! \brief  Call cublas function with Tensor Core safely. If
      Tensor Core is not available, use DEFAULT_MATH instead. */
  inline void TensorCoreCublasCallIfAvailable(
      const std::function<void(blasHandle_t)>& callback) const {
    if (!thread_ctx_.count(this)) {
      phi::GPUContext::TensorCoreCublasCallIfAvailable(callback);
      return;
    }
    context()->TensorCoreCublasCallIfAvailable(callback);
  }

/*! \brief  Return cudnn  handle in the device context. */
#ifdef PADDLE_WITH_HIP
  miopenHandle_t cudnn_handle() const;
#else
  cudnnHandle_t cudnn_handle() const;
#endif

/*! \brief  Return cublas handle in the device context. */
#ifdef PADDLE_WITH_HIP
  rocblas_handle cublas_handle() const;
#else
  cublasHandle_t cublas_handle() const;
  cublasLtHandle_t cublaslt_handle() const;
  cusparseHandle_t cusparse_handle() const;
#endif

#ifndef PADDLE_WITH_HIP
  cusolverDnHandle_t cusolver_dn_handle() const;
#endif

  /*! \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. */
  phi::DnnWorkspaceHandle cudnn_workspace_handle() const;

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

  void RecordEvent(gpuEvent_t ev, const std::function<void()>& callback) const;

  void AddStreamCallback(const std::function<void()>& callback) const;

  void WaitStreamCallback() const;

  void ResetThreadContext(const stream::Priority& priority) {
    std::lock_guard<std::mutex> guard(ctx_mtx_);
    thread_ctx_[this].reset(new CUDAContext(this->GetPlace(), priority));
  }

  std::shared_ptr<CUDAContext> context() const;

  // Note: Can only be used under thread_local semantics.
  void SetThreadLocalStream(const gpuStream_t stream) {
    thread_ctx_.at(this)->SetStream(stream);
  }

  // NOTE: Just for compatibility with the past, please delete if there is an
  // elegant way.
  stream::CUDAStream* GetCudaStream() const;
  stream::CUDAStream* SetCudaStream(stream::CUDAStream*);

 private:
  // The thread_local static variable will be released before the
  // global static variable, so avoid using it in dtor.
  static thread_local std::unordered_map<const CUDADeviceContext*,
                                         std::shared_ptr<CUDAContext>>
      thread_ctx_;
  static thread_local std::mutex ctx_mtx_;

  mutable std::mutex cudnn_handle_mtx_;

  // NOTE: Just for compatibility with the past, please delete if there is an
  // elegant way.
  std::unique_ptr<stream::CUDAStream> cuda_stream_;

  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);

  const 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
using MKLDNNDeviceContextThreadLocals = phi::OneDNNContextThreadLocals;
using MKLDNNDeviceContext = phi::OneDNNContext;
#endif

#ifdef PADDLE_WITH_CUSTOM_DEVICE
class CustomDeviceContext : public phi::CustomContext {
 public:
  explicit CustomDeviceContext(CustomPlace place);
  virtual ~CustomDeviceContext();

  Eigen::DefaultDevice* eigen_device() const { return nullptr; }

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

  void WaitStreamCallback() const { return stream_->WaitCallback(); }

 private:
  std::shared_ptr<phi::stream::Stream> stream_;
};
template <>
struct DefaultDeviceContextType<platform::CustomPlace> {
  using TYPE = CustomDeviceContext;
};
#else
template <>
struct DefaultDeviceContextType<platform::CustomPlace> {
  using TYPE = DeviceContext;
};
#endif

void EmplaceDeviceContexts(
    std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>*
        place_to_device_context,
    const std::vector<platform::Place>& places,
    bool disable_setting_default_stream_for_allocator);

/*! \brief device context pool singleton */
class DeviceContextPool {
 public:
  static DeviceContextPool& Instance() {
    PADDLE_ENFORCE_NOT_NULL(pool,
                            platform::errors::PreconditionNotMet(
                                "Need to Create DeviceContextPool firstly!"));
    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 bool IsInitialized() { return pool != nullptr; }

  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;

  const std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>&
  device_contexts() const;

  static void SetDeviceContexts(
      const std::map<Place,
                     std::shared_future<std::unique_ptr<DeviceContext>>>*);

 private:
  explicit DeviceContextPool(const std::vector<platform::Place>& places);

  static DeviceContextPool* pool;
  std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>
      device_contexts_;
  static thread_local const std::
      map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>*
          external_device_contexts_;  // not owned
  DISABLE_COPY_AND_ASSIGN(DeviceContextPool);
};

}  // namespace platform
}  // namespace paddle