gpu_info.cc

/* 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 <algorithm>
#include <cstdlib>
#include <memory>

#include "gflags/gflags.h"
#include "paddle/fluid/platform/cuda_device_guard.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/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;

USE_GPU_MEM_STAT;
namespace paddle {
namespace platform {

/* 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/
*/

static int GetCUDADeviceCountImpl() {
  int driverVersion = 0;
  cudaError_t status = cudaDriverGetVersion(&driverVersion);

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

  const auto *cuda_visible_devices = std::getenv("CUDA_VISIBLE_DEVICES");
  if (cuda_visible_devices != nullptr) {
    std::string cuda_visible_devices_str(cuda_visible_devices);
    if (std::all_of(cuda_visible_devices_str.begin(),
                    cuda_visible_devices_str.end(),
                    [](char ch) { return ch == ' '; })) {
      VLOG(2) << "CUDA_VISIBLE_DEVICES is set to be empty. No GPU detected.";
      return 0;
    }
  }
  int count;
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaGetDeviceCount(&count));
  return count;
}

int GetCUDADeviceCount() {
  static auto dev_cnt = GetCUDADeviceCountImpl();
  return dev_cnt;
}

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;

  auto major_error_code =
      cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, id);
  auto minor_error_code =
      cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, id);
  PADDLE_ENFORCE_CUDA_SUCCESS(major_error_code);
  PADDLE_ENFORCE_CUDA_SUCCESS(minor_error_code);
  return major * 10 + minor;
}

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;
  auto error_code_x = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimX, id);
  PADDLE_ENFORCE_CUDA_SUCCESS(error_code_x);
  ret.x = size;

  auto error_code_y = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimY, id);
  PADDLE_ENFORCE_CUDA_SUCCESS(error_code_y);
  ret.y = size;

  auto error_code_z = cudaDeviceGetAttribute(&size, cudaDevAttrMaxGridDimZ, id);
  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;
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaRuntimeGetVersion(&runtime_version));
  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;
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaDriverGetVersion(&driver_version));
  return driver_version;
}

bool TensorCoreAvailable() {
#if 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;
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaDeviceGetAttribute(&count, cudaDevAttrMultiProcessorCount, id));
  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;
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaDeviceGetAttribute(
      &count, cudaDevAttrMaxThreadsPerMultiProcessor, id));
  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;
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaDeviceGetAttribute(&count, cudaDevAttrMaxThreadsPerBlock, id));
  return count;
}

int GetCurrentDeviceId() {
  int device_id;
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaGetDevice(&device_id));
  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;
}

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()));
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaSetDevice(id));
}

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;
}

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));
}

void GpuMemcpySync(void *dst, const void *src, size_t count,
                   enum cudaMemcpyKind kind) {
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemcpy(dst, src, count, kind));
}

void GpuMemcpyPeerAsync(void *dst, int dst_device, const void *src,
                        int src_device, size_t count, cudaStream_t stream) {
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaMemcpyPeerAsync(dst, dst_device, src, src_device, count, stream));
}

void GpuMemcpyPeerSync(void *dst, int dst_device, const void *src,
                       int src_device, size_t count) {
  PADDLE_ENFORCE_CUDA_SUCCESS(
      cudaMemcpyPeer(dst, dst_device, src, src_device, count));
}

void GpuMemsetAsync(void *dst, int value, size_t count, cudaStream_t stream) {
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaMemsetAsync(dst, value, count, stream));
}

void GpuStreamSync(cudaStream_t stream) {
  PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamSynchronize(stream));
}

static void RaiseNonOutOfMemoryError(cudaError_t *status) {
  if (*status == cudaErrorMemoryAllocation) {
    *status = cudaSuccess;
  }
  PADDLE_ENFORCE_CUDA_SUCCESS(*status);

  *status = cudaGetLastError();
  if (*status == cudaErrorMemoryAllocation) {
    *status = cudaSuccess;
  }
  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.
   */
  cudaError_t Malloc(void **ptr, size_t size) {
    LockGuardPtr<std::mutex> lock(mtx_);
    if (UNLIKELY(NeedRecord() && cur_size_ + size > limit_size_)) {
      return cudaErrorMemoryAllocation;
    }

    CUDADeviceGuard guard(dev_id_);
    auto result = cudaMalloc(ptr, size);
    if (result == cudaSuccess) {
      if (NeedRecord()) {
        cur_size_ += size;
      }
      STAT_INT_ADD("STAT_gpu" + std::to_string(dev_id_) + "_mem_size", size);
      return cudaSuccess;
    } else {
      RaiseNonOutOfMemoryError(&result);
      // Non out of memory error would be raised inside
      // RaiseNonOutOfMemoryError. Therefore, we can
      // return cudaErrorMemoryAllocation directly here.
      return cudaErrorMemoryAllocation;
    }
  }

  /**
   * 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_);
    auto err = cudaFree(ptr);
    if (err != cudaErrorCudartUnloading) {
      PADDLE_ENFORCE_CUDA_SUCCESS(err);
      if (NeedRecord()) {
        std::lock_guard<std::mutex> guard(*mtx_);
        cur_size_ -= size;
      }
      STAT_INT_SUB("STAT_gpu" + std::to_string(dev_id_) + "_mem_size", size);
    } else {
      cudaGetLastError();  // clear the error flag when cudaErrorCudartUnloading
    }
  }

  bool GetMemInfo(size_t *avail, size_t *total, size_t *actual_avail,
                  size_t *actual_total) {
    {
      CUDADeviceGuard guard(dev_id_);
      auto result = cudaMemGetInfo(actual_avail, actual_total);
      if (result != cudaSuccess) {
        *actual_avail = 0;
      }
      RaiseNonOutOfMemoryError(&result);
    }

    if (NeedRecord()) {
      std::lock_guard<std::mutex> guard(*mtx_);
      *avail = std::min(*actual_avail, limit_size_ - cur_size_);
      *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 {
    LockGuardPtr<std::mutex> lock(mtx_);
    return NeedRecord() ? cur_size_ : 0;
  }

  uint64_t LimitSize() const { return limit_size_; }

 private:
  const int dev_id_;
  const uint64_t limit_size_;
  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_;
};

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

cudaError_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();
}

}  // namespace platform
}  // namespace paddle