libtorch选择指定GPU

libtorch 选择指定GPU

一. 简介

当我们的环境下拥有多块GPU时，有时候需要指定某一块GPU来运行

二. 默认是使用第一块GPU，即gpu_id=0的那块

这样默认是使用第一块GPU：

torch::jit::script::Module module = torch::jit::load(filename);
module->to(at::kCUDA);

三. 选择其他GPU

以选择gpuid = 1 的GPU为例：

int gpu_id = 1;	
torch::jit::script::Module module = torch::jit::load(filename,torch::Device(torch::DeviceType::CUDA,gpu_id));
module->to(at::kCUDA);

四. libtorch 源码

/// Loads a serialized `Module` from the given `istream`.
///
/// The istream must contain a serialized `Module`, exported via
/// `torch::jit::ExportModule` in C++.
TORCH_API Module load(
    std::istream& in,
    c10::optional<c10::Device> device = c10::nullopt,
    ExtraFilesMap& extra_files = default_extra_files);

/// Loads a serialized `Module` from the given `filename`.
///
/// The file stored at the location given in `filename` must contain a
/// serialized `Module`, exported either via `ScriptModule.save()` in
/// Python or `torch::jit::ExportModule` in C++.
TORCH_API Module load(
    const std::string& filename,
    c10::optional<c10::Device> device = c10::nullopt,
    ExtraFilesMap& extra_files = default_extra_files);

/// Loads a serialized `Module` from the given `rai`.
///
/// The reader adapter, which is for customized input stream, must contain a
/// serialized `Module`, exported either via `ScriptModule.save()` in
/// Python or `torch::jit::ExportModule` in C++.
TORCH_API Module load(
    std::unique_ptr<caffe2::serialize::ReadAdapterInterface> rai,
    c10::optional<c10::Device> device = c10::nullopt,
    ExtraFilesMap& extra_files = default_extra_files);

libtorch 加载torchscript模型有三个重载函数，我们这里用模型文件的方式，所以选第二个。

第一个参数传模型文件路径，第二个参数传一个 Device类，看源码：

namespace c10 {

/// An index representing a specific device; e.g., the 1 in GPU 1.
/// A DeviceIndex is not independently meaningful without knowing
/// the DeviceType it is associated; try to use Device rather than
/// DeviceIndex directly.
using DeviceIndex = int16_t;

/// Represents a a compute device on which a tensor is located. A device is
/// uniquely identified by a type, which specifies the type of machine it is
/// (e.g. CPU or CUDA GPU), and a device index or ordinal, which identifies the
/// specific compute device when there is more than one of a certain type. The
/// device index is optional, and in its defaulted state represents (abstractly)
/// "the current device". Further, there are two constraints on the value of the
/// device index, if one is explicitly stored:
/// 1. A negative index represents the current device, a non-negative index
/// represents a specific, concrete device,
/// 2. When the device type is CPU, the device index must be zero.
struct C10_API Device final {
  using Type = DeviceType;

  /// Constructs a new `Device` from a `DeviceType` and an optional device
  /// index.
  /* implicit */ Device(DeviceType type, DeviceIndex index = -1)
      : type_(type), index_(index) {
    validate();
  }

  /// Constructs a `Device` from a string description, for convenience.
  /// The string supplied must follow the following schema:
  /// `(cpu|cuda)[:]`
  /// where `cpu` or `cuda` specifies the device type, and
  /// `:` optionally specifies a device index.
  /* implicit */ Device(const std::string& device_string);

  /// Returns true if the type and index of this `Device` matches that of
  /// `other`.
  bool operator==(const Device& other) const noexcept {
    return this->type_ == other.type_ && this->index_ == other.index_;
  }

  /// Returns true if the type or index of this `Device` differs from that of
  /// `other`.
  bool operator!=(const Device& other) const noexcept {
    return !(*this == other);
  }

  /// Sets the device index.
  void set_index(DeviceIndex index) {
    index_ = index;
  }

  /// Returns the type of device this is.
  DeviceType type() const noexcept {
    return type_;
  }

  /// Returns the optional index.
  DeviceIndex index() const noexcept {
    return index_;
  }

  /// Returns true if the device has a non-default index.
  bool has_index() const noexcept {
    return index_ != -1;
  }

  /// Return true if the device is of CUDA type.
  bool is_cuda() const noexcept {
    return type_ == DeviceType::CUDA;
  }

  /// Return true if the device is of CPU type.
  bool is_cpu() const noexcept {
    return type_ == DeviceType::CPU;
  }

  /// Same string as returned from operator<<.
  std::string str() const;

 private:
  DeviceType type_;
  DeviceIndex index_ = -1;
  void validate() {
    TORCH_CHECK(index_ == -1 || index_ >= 0,
        "Device index must be -1 or non-negative, got ", index_);
    TORCH_CHECK(!is_cpu() || index_ <= 0,
        "CPU device index must be -1 or zero, got ", index_);
  }
};

看第一个构造函数，第一个参数设备类型，第二个参数设备索引

设备类型是个枚举:

enum class DeviceType : int16_t {
  CPU = 0,
  CUDA = 1, // CUDA.
  MKLDNN = 2, // Reserved for explicit MKLDNN
  OPENGL = 3, // OpenGL
  OPENCL = 4, // OpenCL
  IDEEP = 5, // IDEEP.
  HIP = 6, // AMD HIP
  FPGA = 7, // FPGA
  MSNPU = 8, // MSNPU
  XLA = 9, // XLA / TPU
  Vulkan = 10, // Vulkan
  // NB: If you add more devices:
  //  - Change the implementations of DeviceTypeName and isValidDeviceType
  //    in DeviceType.cpp
  //  - Change the number below
  COMPILE_TIME_MAX_DEVICE_TYPES = 11,
  ONLY_FOR_TEST = 20901, // This device type is only for test.
};

我们这里是nvidia显卡，选择torch::DeviceType::CUDA
第二个参数就是显卡id，0代表第一块显卡,１代表第二块显卡