/* 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. */

#include "paddle/fluid/platform/gpu_info.h"
#include <cstdlib>
#include <mutex>
#include <vector>

#include "gflags/gflags.h"
#include "paddle/fluid/platform/cuda_device_guard.h"
#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/platform/dynload/miopen.h"
#else
#include "paddle/fluid/platform/cuda_graph.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#endif
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/lock_guard_ptr.h"
#include "paddle/fluid/platform/macros.h"
#include "paddle/fluid/platform/monitor.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"

DECLARE_double(fraction_of_gpu_memory_to_use);
DECLARE_uint64(initial_gpu_memory_in_mb);
DECLARE_uint64(reallocate_gpu_memory_in_mb);
DECLARE_bool(enable_cublas_tensor_op_math);
DECLARE_string(selected_gpus);
DECLARE_uint64(gpu_memory_limit_mb);

constexpr static float fraction_reserve_gpu_memory = 0.05f;

static std::once_flag g_device_props_size_init_flag;
static std::vector<std::unique_ptr<std::once_flag>> g_device_props_init_flags;
static std::vector<paddle::gpuDeviceProp> g_device_props;

USE_GPU_MEM_STAT;
namespace paddle {
namespace platform {

int CudnnVersion() {
  if (!dynload::HasCUDNN()) return -1;

#ifdef PADDLE_WITH_HIP
  size_t version_major, version_minor, version_patch;
  PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenGetVersion(
      &version_major, &version_minor, &version_patch));
  return version_major * 100 + version_minor * 10 + version_patch;
#else
  return dynload::cudnnGetVersion();
#endif
}
static int GetCUDADeviceCountImpl() {
  int driverVersion = 0;
#ifdef PADDLE_WITH_HIP
  hipError_t status = hipDriverGetVersion(&driverVersion);
#else
  cudaError_t status = cudaDriverGetVersion(&driverVersion);
#endif

  if (!(status == gpuSuccess && driverVersion != 0)) {
    // No GPU driver
    VLOG(2) << "GPU Driver Version can't be detected. No GPU driver!";
    return 0;
  }

#ifdef PADDLE_WITH_HIP
  const auto *cuda_visible_devices = std::getenv("HIP_VISIBLE_DEVICES");
#else
  const auto *cuda_visible_devices = std::getenv("CUDA_VISIBLE_DEVICES");
#endif
  if (cuda_visible_devices != nullptr) {
    std::string cuda_visible_devices_str(cuda_visible_devices);
    if (!cuda_visible_devices_str.empty()) {
      cuda_visible_devices_str.erase(
          0, cuda_visible_devices_str.find_first_not_of('\''));
      cuda_visible_devices_str.erase(
          cuda_visible_devices_str.find_last_not_of('\'') + 1);
      cuda_visible_devices_str.erase(
          0, cuda_visible_devices_str.find_first_not_of('\"'));
      cuda_visible_devices_str.erase(
          cuda_visible_devices_str.find_last_not_of('\"') + 1);
    }
    if (std::all_of(cuda_visible_devices_str.begin(),
                    cuda_visible_devices_str.end(),
                    [](char ch) { return ch == ' '; })) {
      VLOG(2) << "CUDA_VISIBLE_DEVICES or HIP_VISIBLE_DEVICES is set to be "
                 "empty. No GPU detected.";
      return 0;
    }
  }
  int count;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipGetDeviceCount(&count));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaGetDeviceCount(&count));
#endif
  return count;
}

int GetCUDADeviceCount() {
  // cache the count
  static auto dev_cnt = GetCUDADeviceCountImpl();
  return dev_cnt;
}

/* Here is a very simple CUDA “pro tip”: cudaDeviceGetAttribute() is a much
faster way to query device properties. You can see details in
https://devblogs.nvidia.com/cuda-pro-tip-the-fast-way-to-query-device-properties/
*/
int GetCUDAComputeCapability(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int major, minor;

#ifdef PADDLE_WITH_HIP
  auto major_error_code = hipDeviceGetAttribute(
      &major, hipDeviceAttributeComputeCapabilityMajor, id);
  auto minor_error_code = hipDeviceGetAttribute(
      &minor, hipDeviceAttributeComputeCapabilityMinor, id);
#else
  auto major_error_code =
      cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, id);
  auto minor_error_code =
      cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, id);
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(major_error_code);
  PADDLE_ENFORCE_CUDA_SUCCESS(minor_error_code);
#ifdef PADDLE_WITH_HIP
  return major * 100 + minor;
#else
  return major * 10 + minor;
#endif
}

dim3 GetGpuMaxGridDimSize(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  dim3 ret;
  int size;
#ifdef PADDLE_WITH_HIP
  auto error_code_x =
      hipDeviceGetAttribute(&size, hipDeviceAttributeMaxGridDimX, id);
#else
  auto error_code_x = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimX, id);
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(error_code_x);
  ret.x = size;

#ifdef PADDLE_WITH_HIP
  auto error_code_y =
      hipDeviceGetAttribute(&size, hipDeviceAttributeMaxGridDimY, id);
#else
  auto error_code_y = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimY, id);
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(error_code_y);
  ret.y = size;

#ifdef PADDLE_WITH_HIP
  auto error_code_z =
      hipDeviceGetAttribute(&size, hipDeviceAttributeMaxGridDimZ, id);
#else
  auto error_code_z = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimZ, id);
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(error_code_z);
  ret.z = size;
  return ret;
}

int GetCUDARuntimeVersion(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int runtime_version = 0;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipRuntimeGetVersion(&runtime_version));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaRuntimeGetVersion(&runtime_version));
#endif
  return runtime_version;
}

int GetCUDADriverVersion(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int driver_version = 0;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipDriverGetVersion(&driver_version));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaDriverGetVersion(&driver_version));
#endif
  return driver_version;
}

bool TensorCoreAvailable() {
#if !defined(PADDLE_WITH_HIP) && CUDA_VERSION >= 9000
  int device = GetCurrentDeviceId();
  int driver_version = GetCUDAComputeCapability(device);
  return driver_version >= 70;
#else
  return false;
#endif
}

int GetCUDAMultiProcessors(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int count;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(
      hipDeviceGetAttribute(&count, hipDeviceAttributeMultiprocessorCount, id));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaDeviceGetAttribute(&count, cudaDevAttrMultiProcessorCount, id));
#endif
  return count;
}

int GetCUDAMaxThreadsPerMultiProcessor(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int count;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipDeviceGetAttribute(
      &count, hipDeviceAttributeMaxThreadsPerMultiProcessor, id));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaDeviceGetAttribute(
      &count, cudaDevAttrMaxThreadsPerMultiProcessor, id));
#endif
  return count;
}

int GetCUDAMaxThreadsPerBlock(int id) {
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
  int count;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(
      hipDeviceGetAttribute(&count, hipDeviceAttributeMaxThreadsPerBlock, id));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaDeviceGetAttribute(&count, cudaDevAttrMaxThreadsPerBlock, id));
#endif
  return count;
}

int GetCurrentDeviceId() {
  int device_id;
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipGetDevice(&device_id));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaGetDevice(&device_id));
#endif
  return device_id;
}

//! Get a list of device ids from environment variable or use all.
std::vector<int> GetSelectedDevices() {
  // use user specified GPUs in single-node multi-process mode.
  std::vector<int> devices;
  if (!FLAGS_selected_gpus.empty()) {
    auto devices_str = paddle::string::Split(FLAGS_selected_gpus, ',');
    for (auto id : devices_str) {
      devices.push_back(atoi(id.c_str()));
    }
  } else {
    int count = GetCUDADeviceCount();
    for (int i = 0; i < count; ++i) {
      devices.push_back(i);
    }
  }
  return devices;
}

const gpuDeviceProp &GetDeviceProperties(int id) {
  std::call_once(g_device_props_size_init_flag, [&] {
    int gpu_num = 0;
    gpu_num = platform::GetCUDADeviceCount();
    g_device_props_init_flags.resize(gpu_num);
    g_device_props.resize(gpu_num);
    for (int i = 0; i < gpu_num; ++i) {
      g_device_props_init_flags[i] = std::make_unique<std::once_flag>();
    }
  });

  if (id == -1) {
    id = platform::GetCurrentDeviceId();
  }

  if (id < 0 || id >= static_cast<int>(g_device_props.size())) {
    PADDLE_THROW(platform::errors::OutOfRange(
        "The device id %d is out of range [0, %d), where %d is the number of "
        "devices on this machine. Because the device id should be greater than "
        "or equal to zero and smaller than the number of gpus. Please input "
        "appropriate device again!",
        id, static_cast<int>(g_device_props.size()),
        static_cast<int>(g_device_props.size())));
  }

  std::call_once(*(g_device_props_init_flags[id]), [&] {
#ifdef PADDLE_WITH_CUDA
    PADDLE_ENFORCE_CUDA_SUCCESS(
        cudaGetDeviceProperties(&g_device_props[id], id));
#else
    PADDLE_ENFORCE_CUDA_SUCCESS(
      hipGetDeviceProperties(&g_device_props[id], id));
#endif
  });

  return g_device_props[id];
}

void SetDeviceId(int id) {
  // TODO(qijun): find a better way to cache the cuda device count
  PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(),
                    platform::errors::InvalidArgument(
                        "Device id must be less than GPU count, "
                        "but received id is: %d. GPU count is: %d.",
                        id, GetCUDADeviceCount()));
#ifdef PADDLE_WITH_HIP
  PADDLE_RETRY_CUDA_SUCCESS(hipSetDevice(id));
#else
  PADDLE_RETRY_CUDA_SUCCESS(cudaSetDevice(id));
#endif
}

void GpuMemoryUsage(size_t *available, size_t *total) {
  size_t actual_available, actual_total;
  RecordedCudaMemGetInfo(available, total, &actual_available, &actual_total,
                         platform::GetCurrentDeviceId());
}

size_t GpuAvailableMemToAlloc() {
  size_t total = 0;
  size_t available = 0;
  GpuMemoryUsage(&available, &total);
  size_t reserving =
      static_cast<size_t>(fraction_reserve_gpu_memory * available);
  // If available size is less than minimum chunk size, no usable memory exists
  size_t available_to_alloc = available - reserving;
  size_t min_chunk_size = GpuMinChunkSize();
  if (available_to_alloc < min_chunk_size) {
    available_to_alloc = 0;
  }
  VLOG(10) << "GPU usage " << (available >> 20) << "M/" << (total >> 20)
           << "M, " << (available_to_alloc >> 20) << "M available to allocate";
  return available_to_alloc;
}

size_t GpuMaxAllocSize() {
  return std::max(GpuInitAllocSize(), GpuReallocSize());
}

static size_t GpuAllocSize(bool realloc) {
  size_t available_to_alloc = GpuAvailableMemToAlloc();
  PADDLE_ENFORCE_GT(
      available_to_alloc, 0,
      platform::errors::ResourceExhausted("Not enough available GPU memory."));
  // If FLAGS_initial_gpu_memory_in_mb is 0, then initial memory will be
  // allocated by fraction
  size_t flag_mb = realloc ? FLAGS_reallocate_gpu_memory_in_mb
                           : FLAGS_initial_gpu_memory_in_mb;
  size_t alloc_bytes =
      (flag_mb > 0ul ? flag_mb << 20 : available_to_alloc *
                                           FLAGS_fraction_of_gpu_memory_to_use);
  PADDLE_ENFORCE_GE(
      available_to_alloc, alloc_bytes,
      platform::errors::ResourceExhausted("Not enough available GPU memory."));
  VLOG(10) << "Alloc size is " << (alloc_bytes >> 20)
           << " MiB, is it Re-alloc: " << realloc;
  return alloc_bytes;
}

size_t GpuInitAllocSize() { return GpuAllocSize(/* realloc = */ false); }

size_t GpuReallocSize() { return GpuAllocSize(/* realloc = */ true); }

size_t GpuMinChunkSize() {
  // Allow to allocate the minimum chunk size is 256 bytes.
  return 1 << 8;
}

size_t GpuMaxChunkSize() {
  size_t max_chunk_size = GpuMaxAllocSize();
  VLOG(10) << "Max chunk size " << (max_chunk_size >> 20) << "M";
  return max_chunk_size;
}

#ifdef PADDLE_WITH_HIP
void GpuMemcpyAsync(void *dst, const void *src, size_t count,
                    enum hipMemcpyKind kind, hipStream_t stream) {
  PADDLE_ENFORCE_CUDA_SUCCESS(hipMemcpyAsync(dst, src, count, kind, stream));
}
#else
void GpuMemcpyAsync(void *dst, const void *src, size_t count,
                    enum cudaMemcpyKind kind, cudaStream_t stream) {
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemcpyAsync(dst, src, count, kind, stream));
}
#endif

#ifdef PADDLE_WITH_HIP
void GpuMemcpySync(void *dst, const void *src, size_t count,
                   enum hipMemcpyKind kind) {
  PADDLE_ENFORCE_CUDA_SUCCESS(hipMemcpy(dst, src, count, kind));
}
#else
void GpuMemcpySync(void *dst, const void *src, size_t count,
                   enum cudaMemcpyKind kind) {
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemcpy(dst, src, count, kind));
}
#endif

void GpuMemcpyPeerAsync(void *dst, int dst_device, const void *src,
                        int src_device, size_t count, gpuStream_t stream) {
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(
      hipMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream));
#endif
}

void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
                       int src_device, size_t count) {
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(
      hipMemcpyPeer(dst, dst_device, src, src_device, count));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaMemcpyPeer(dst, dst_device, src, src_device, count));
#endif
}

void GpuMemsetAsync(void *dst, int value, size_t count, gpuStream_t stream) {
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipMemsetAsync(dst, value, count, stream));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemsetAsync(dst, value, count, stream));
#endif
}

void GpuStreamSync(gpuStream_t stream) {
#ifdef PADDLE_WITH_HIP
  PADDLE_ENFORCE_CUDA_SUCCESS(hipStreamSynchronize(stream));
#else
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamSynchronize(stream));
#endif
}

static void RaiseNonOutOfMemoryError(gpuError_t *status) {
#ifdef PADDLE_WITH_HIP
  if (*status == hipErrorOutOfMemory) {
    *status = hipSuccess;
  }
#else
  if (*status == cudaErrorMemoryAllocation) {
    *status = cudaSuccess;
  }
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(*status);

#ifdef PADDLE_WITH_HIP
  *status = hipGetLastError();
  if (*status == hipErrorOutOfMemory) {
    *status = hipSuccess;
  }
#else
  *status = cudaGetLastError();
  if (*status == cudaErrorMemoryAllocation) {
    *status = cudaSuccess;
  }
#endif
  PADDLE_ENFORCE_CUDA_SUCCESS(*status);
}

class RecordedCudaMallocHelper {
 private:
  explicit RecordedCudaMallocHelper(int dev_id, uint64_t limit_size = 0)
      : dev_id_(dev_id), limit_size_(limit_size) {
    if (NeedRecord()) {
      mtx_.reset(new std::mutex());
    }
  }

  DISABLE_COPY_AND_ASSIGN(RecordedCudaMallocHelper);

 public:
  static RecordedCudaMallocHelper *Instance(int dev_id) {
    std::call_once(once_flag_, [] {
      int dev_cnt = GetCUDADeviceCount();
      instances_.reserve(dev_cnt);
      for (int i = 0; i < dev_cnt; ++i) {
        instances_.emplace_back(
            new RecordedCudaMallocHelper(i, FLAGS_gpu_memory_limit_mb << 20));
      }
    });

    PADDLE_ENFORCE_GE(
        dev_id, 0,
        platform::errors::OutOfRange(
            "Device id must be not less than 0, but got %d.", dev_id));
    PADDLE_ENFORCE_LT(
        dev_id, instances_.size(),
        platform::errors::OutOfRange("Device id %d exceeds gpu card number %d.",
                                     dev_id, instances_.size()));
    return instances_[dev_id].get();
  }

  /**
   * Try to allocate `size` gpu memory. Only cudaErrorMemoryAllocation
   * or cudaSuccess would be returned, and the cudaGetLastError() flag
   * would be clear.
   */
  gpuError_t Malloc(void **ptr, size_t size) {
    LockGuardPtr<std::mutex> lock(mtx_);
    if (UNLIKELY(NeedRecord() && cur_size_.load() + size > limit_size_)) {
#ifdef PADDLE_WITH_HIP
      return hipErrorOutOfMemory;
#else
      return cudaErrorMemoryAllocation;
#endif
    }

    CUDADeviceGuard guard(dev_id_);
#ifdef PADDLE_WITH_HIP
    auto result = hipMalloc(ptr, size);
#else
    CUDAGraphCaptureModeGuard capture_mode_guard;
    auto result = cudaMalloc(ptr, size);
#endif
    if (result == gpuSuccess) {
      cur_size_.fetch_add(size);
      STAT_INT_ADD("STAT_gpu" + std::to_string(dev_id_) + "_mem_size", size);
      return gpuSuccess;
    } else {
      RaiseNonOutOfMemoryError(&result);
// Non out of memory error would be raised inside
// RaiseNonOutOfMemoryError. Therefore, we can
// return cudaErrorMemoryAllocation directly here.
#ifdef PADDLE_WITH_HIP
      return hipErrorOutOfMemory;
#else
      return cudaErrorMemoryAllocation;
#endif
    }
  }

  /**
   * Free gpu memory. Usually, free is not allowed to raise error.
   * If it does raise error, the process should be crashed.
   */
  void Free(void *ptr, size_t size) {
    // Purposefully allow cudaErrorCudartUnloading, because
    // that is returned if you ever call cudaFree after the
    // driver has already shutdown. This happens only if the
    // process is terminating, in which case we don't care if
    // cudaFree succeeds.
    CUDADeviceGuard guard(dev_id_);
#ifdef PADDLE_WITH_HIP
    auto err = hipFree(ptr);
    if (err != hipErrorDeinitialized) {
#else
    auto err = cudaFree(ptr);
    if (err != cudaErrorCudartUnloading) {
#endif
      PADDLE_ENFORCE_CUDA_SUCCESS(err);
      cur_size_.fetch_sub(size);
      STAT_INT_SUB("STAT_gpu" + std::to_string(dev_id_) + "_mem_size", size);
    } else {
#ifdef PADDLE_WITH_HIP
      hipGetLastError();  // clear the error flag when hipErrorDeinitialized
#else
      cudaGetLastError();  // clear the error flag when cudaErrorCudartUnloading
#endif
    }
  }

  bool GetMemInfo(size_t *avail, size_t *total, size_t *actual_avail,
                  size_t *actual_total) {
    {
      CUDADeviceGuard guard(dev_id_);
#ifdef PADDLE_WITH_HIP
      auto result = hipMemGetInfo(actual_avail, actual_total);
#else
      auto result = cudaMemGetInfo(actual_avail, actual_total);
#endif
      if (result != gpuSuccess) {
        *actual_avail = 0;
      }
      RaiseNonOutOfMemoryError(&result);
    }

    if (NeedRecord()) {
      std::lock_guard<std::mutex> guard(*mtx_);
      *avail = std::min(*actual_avail, limit_size_ - cur_size_.load());
      *total = std::min(*actual_total, limit_size_);
      return *total < *actual_total;
    } else {
      *avail = *actual_avail;
      *total = *actual_total;
      return false;
    }
  }

  inline bool NeedRecord() const { return limit_size_ != 0; }

  uint64_t RecordedSize() const { return cur_size_.load(); }

  uint64_t LimitSize() const { return limit_size_; }

 private:
  const int dev_id_;
  const uint64_t limit_size_;
  std::atomic<uint64_t> cur_size_{0};

  mutable std::unique_ptr<std::mutex> mtx_;

  static std::once_flag once_flag_;
  static std::vector<std::unique_ptr<RecordedCudaMallocHelper>> instances_;
};  // NOLINT

std::once_flag RecordedCudaMallocHelper::once_flag_;
std::vector<std::unique_ptr<RecordedCudaMallocHelper>>
    RecordedCudaMallocHelper::instances_;

gpuError_t RecordedCudaMalloc(void **ptr, size_t size, int dev_id) {
  return RecordedCudaMallocHelper::Instance(dev_id)->Malloc(ptr, size);
}

void RecordedCudaFree(void *p, size_t size, int dev_id) {
  return RecordedCudaMallocHelper::Instance(dev_id)->Free(p, size);
}

bool RecordedCudaMemGetInfo(size_t *avail, size_t *total, size_t *actual_avail,
                            size_t *actual_total, int dev_id) {
  return RecordedCudaMallocHelper::Instance(dev_id)->GetMemInfo(
      avail, total, actual_avail, actual_total);
}

uint64_t RecordedCudaMallocSize(int dev_id) {
  return RecordedCudaMallocHelper::Instance(dev_id)->RecordedSize();
}

bool IsCudaMallocRecorded(int dev_id) {
  return RecordedCudaMallocHelper::Instance(dev_id)->NeedRecord();
}

void EmptyCache(void) {
  std::vector<int> devices = GetSelectedDevices();
  for (auto device : devices) {
    memory::Release(CUDAPlace(device));
  }
}

}  // namespace platform
}  // namespace paddle