使用自定义 C++ 类扩展 TorchScript

原文：https://pytorch.org/tutorials/advanced/torch_script_custom_classes.html

本教程是自定义运算符教程的后续教程，并介绍了我们为将 C++ 类同时绑定到 TorchScript 和 Python 而构建的 API。 该 API 与pybind11非常相似，如果您熟悉该系统，则大多数概念都将转移过来。

用 C++ 实现和绑定类

在本教程中，我们将定义一个简单的 C++ 类，该类在成员变量中保持持久状态。

// This header is all you need to do the C++ portions of this
// tutorial
#include <torch/script.h>
// This header is what defines the custom class registration
// behavior specifically. script.h already includes this, but
// we include it here so you know it exists in case you want
// to look at the API or implementation.
#include <torch/custom_class.h>
#include <string>
#include <vector>
template <class T>
struct MyStackClass : torch::CustomClassHolder {
  std::vector<T> stack_;
  MyStackClass(std::vector<T> init) : stack_(init.begin(), init.end()) {}
  void push(T x) {
    stack_.push_back(x);
  }
  T pop() {
    auto val = stack_.back();
    stack_.pop_back();
    return val;
  }
  c10::intrusive_ptr<MyStackClass> clone() const {
    return c10::make_intrusive<MyStackClass>(stack_);
  }
  void merge(const c10::intrusive_ptr<MyStackClass>& c) {
    for (auto& elem : c->stack_) {
      push(elem);
    }
  }
};

有几件事要注意：

• torch/custom_class.h是您需要使用自定义类扩展 TorchScript 的标头。
• 注意，无论何时使用自定义类的实例，我们都通过c10::intrusive_ptr<>的实例来实现。 可以将intrusive_ptr视为类似于std::shared_ptr的智能指针，但是引用计数直接存储在对象中，而不是单独的元数据块（如std::shared_ptr中所做的。torch::Tensor内部使用相同的指针类型 ;和自定义类也必须使用此指针类型，以便我们可以一致地管理不同的对象类型。
• 注意的第二件事是用户定义的类必须继承torch::CustomClassHolder。 这样可以确保自定义类具有存储引用计数的空间。

现在让我们看一下如何使该类对 TorchScript 可见，该过程称为绑定该类：

// Notice a few things:
// - We pass the class to be registered as a template parameter to
//   `torch::class_`. In this instance, we've passed the
//   specialization of the MyStackClass class ``MyStackClass<std::string>``.
//   In general, you cannot register a non-specialized template
//   class. For non-templated classes, you can just pass the
//   class name directly as the template parameter.
// - The arguments passed to the constructor make up the "qualified name"
//   of the class. In this case, the registered class will appear in
//   Python and C++ as `torch.classes.my_classes.MyStackClass`. We call
//   the first argument the "namespace" and the second argument the
//   actual class name.
TORCH_LIBRARY(my_classes, m) {
  m.class_<MyStackClass<std::string>>("MyStackClass")
    // The following line registers the contructor of our MyStackClass
    // class that takes a single `std::vector<std::string>` argument,
    // i.e. it exposes the C++ method `MyStackClass(std::vector<T> init)`.
    // Currently, we do not support registering overloaded
    // constructors, so for now you can only `def()` one instance of
    // `torch::init`.
    .def(torch::init<std::vector<std::string>>())
    // The next line registers a stateless (i.e. no captures) C++ lambda
    // function as a method. Note that a lambda function must take a
    // `c10::intrusive_ptr<YourClass>` (or some const/ref version of that)
    // as the first argument. Other arguments can be whatever you want.
    .def("top", [](const c10::intrusive_ptr<MyStackClass<std::string>>& self) {
      return self->stack_.back();
    })
    // The following four lines expose methods of the MyStackClass<std::string>
    // class as-is. `torch::class_` will automatically examine the
    // argument and return types of the passed-in method pointers and
    // expose these to Python and TorchScript accordingly. Finally, notice
    // that we must take the *address* of the fully-qualified method name,
    // i.e. use the unary `&` operator, due to C++ typing rules.
    .def("push", &MyStackClass<std::string>::push)
    .def("pop", &MyStackClass<std::string>::pop)
    .def("clone", &MyStackClass<std::string>::clone)
    .def("merge", &MyStackClass<std::string>::merge)
  ;
}

使用 CMake 将示例构建为 C++ 项目

现在，我们将使用 CMake 构建系统来构建上述 C++ 代码。 首先，将到目前为止介绍的所有 C++ 代码放入class.cpp文件中。 然后，编写一个简单的CMakeLists.txt文件并将其放在同一目录中。 CMakeLists.txt应该是这样的：

cmake_minimum_required(VERSION 3.1 FATAL_ERROR)
project(custom_class)
find_package(Torch REQUIRED)
# Define our library target
add_library(custom_class SHARED class.cpp)
set(CMAKE_CXX_STANDARD 14)
# Link against LibTorch
target_link_libraries(custom_class "${TORCH_LIBRARIES}")

另外，创建一个build目录。 您的文件树应如下所示：

custom_class_project/
  class.cpp
  CMakeLists.txt
  build/

我们假设您已经按照上一教程中所述的相同方式设置了环境。 继续并调用cmake，然后进行构建项目：

$ cd build
$ cmake -DCMAKE_PREFIX_PATH="$(python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)')" ..
  -- The C compiler identification is GNU 7.3.1
  -- The CXX compiler identification is GNU 7.3.1
  -- Check for working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc
  -- Check for working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc -- works
  -- Detecting C compiler ABI info
  -- Detecting C compiler ABI info - done
  -- Detecting C compile features
  -- Detecting C compile features - done
  -- Check for working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++
  -- Check for working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++ -- works
  -- Detecting CXX compiler ABI info
  -- Detecting CXX compiler ABI info - done
  -- Detecting CXX compile features
  -- Detecting CXX compile features - done
  -- Looking for pthread.h
  -- Looking for pthread.h - found
  -- Looking for pthread_create
  -- Looking for pthread_create - not found
  -- Looking for pthread_create in pthreads
  -- Looking for pthread_create in pthreads - not found
  -- Looking for pthread_create in pthread
  -- Looking for pthread_create in pthread - found
  -- Found Threads: TRUE
  -- Found torch: /torchbind_tutorial/libtorch/lib/libtorch.so
  -- Configuring done
  -- Generating done
  -- Build files have been written to: /torchbind_tutorial/build
$ make -j
  Scanning dependencies of target custom_class
  [ 50%] Building CXX object CMakeFiles/custom_class.dir/class.cpp.o
  [100%] Linking CXX shared library libcustom_class.so
  [100%] Built target custom_class

您会发现，构建目录中现在有一个动态库文件。 在 Linux 上，它可能名为libcustom_class.so。 因此，文件树应如下所示：

custom_class_project/
  class.cpp
  CMakeLists.txt
  build/
    libcustom_class.so

从 Python 和 TorchScript 使用 C++ 类

现在我们已经将我们的类及其注册编译为.so文件，我们可以将.so加载到 Python 中并进行尝试。 这是一个演示脚本的脚本：

import torch
# `torch.classes.load_library()` allows you to pass the path to your .so file
# to load it in and make the custom C++ classes available to both Python and
# TorchScript
torch.classes.load_library("build/libcustom_class.so")
# You can query the loaded libraries like this:
print(torch.classes.loaded_libraries)
# prints {'/custom_class_project/build/libcustom_class.so'}
# We can find and instantiate our custom C++ class in python by using the
# `torch.classes` namespace:
#
# This instantiation will invoke the MyStackClass(std::vector<T> init)
# constructor we registered earlier
s = torch.classes.my_classes.MyStackClass(["foo", "bar"])
# We can call methods in Python
s.push("pushed")
assert s.pop() == "pushed"
# Returning and passing instances of custom classes works as you'd expect
s2 = s.clone()
s.merge(s2)
for expected in ["bar", "foo", "bar", "foo"]:
    assert s.pop() == expected
# We can also use the class in TorchScript
# For now, we need to assign the class's type to a local in order to
# annotate the type on the TorchScript function. This may change
# in the future.
MyStackClass = torch.classes.my_classes.MyStackClass
@torch.jit.script
def do_stacks(s: MyStackClass):  # We can pass a custom class instance
    # We can instantiate the class
    s2 = torch.classes.my_classes.MyStackClass(["hi", "mom"])
    s2.merge(s)  # We can call a method on the class
    # We can also return instances of the class
    # from TorchScript function/methods
    return s2.clone(), s2.top()
stack, top = do_stacks(torch.classes.my_classes.MyStackClass(["wow"]))
assert top == "wow"
for expected in ["wow", "mom", "hi"]:
    assert stack.pop() == expected

使用自定义类保存，加载和运行 TorchScript 代码

我们还可以在使用 libtorch 的 C++ 进程中使用自定义注册的 C++ 类。 举例来说，让我们定义一个简单的nn.Module，它实例化并调用MyStackClass类上的方法：

import torch
torch.classes.load_library('build/libcustom_class.so')
class Foo(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, s: str) -> str:
        stack = torch.classes.my_classes.MyStackClass(["hi", "mom"])
        return stack.pop() + s
scripted_foo = torch.jit.script(Foo())
print(scripted_foo.graph)
scripted_foo.save('foo.pt')

我们文件系统中的foo.pt现在包含我们刚刚定义的序列化 TorchScript 程序。

现在，我们将定义一个新的 CMake 项目，以展示如何加载此模型及其所需的.so文件。 有关如何执行此操作的完整说明，请查看在 C++ 中加载 TorchScript 模型的教程。

与之前类似，让我们创建一个包含以下内容的文件结构：

cpp_inference_example/
  infer.cpp
  CMakeLists.txt
  foo.pt
  build/
  custom_class_project/
    class.cpp
    CMakeLists.txt
    build/

请注意，我们已经复制了序列化的foo.pt文件以及上面custom_class_project的源代码树。 我们将把custom_class_project作为依赖项添加到此 C++ 项目中，以便可以将自定义类构建到二进制文件中。

让我们用以下内容填充infer.cpp：

#include <torch/script.h>
#include <iostream>
#include <memory>
int main(int argc, const char* argv[]) {
  torch::jit::Module module;
  try {
    // Deserialize the ScriptModule from a file using torch::jit::load().
    module = torch::jit::load("foo.pt");
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }
  std::vector<c10::IValue> inputs = {"foobarbaz"};
  auto output = module.forward(inputs).toString();
  std::cout << output->string() << std::endl;
}

同样，让我们​​定义CMakeLists.txt文件：

cmake_minimum_required(VERSION 3.1 FATAL_ERROR)
project(infer)
find_package(Torch REQUIRED)
add_subdirectory(custom_class_project)
# Define our library target
add_executable(infer infer.cpp)
set(CMAKE_CXX_STANDARD 14)
# Link against LibTorch
target_link_libraries(infer "${TORCH_LIBRARIES}")
# This is where we link in our libcustom_class code, making our
# custom class available in our binary.
target_link_libraries(infer -Wl,--no-as-needed custom_class)

您知道练习：cd build，cmake和make：

$ cd build
$ cmake -DCMAKE_PREFIX_PATH="$(python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)')" ..
  -- The C compiler identification is GNU 7.3.1
  -- The CXX compiler identification is GNU 7.3.1
  -- Check for working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc
  -- Check for working C compiler: /opt/rh/devtoolset-7/root/usr/bin/cc -- works
  -- Detecting C compiler ABI info
  -- Detecting C compiler ABI info - done
  -- Detecting C compile features
  -- Detecting C compile features - done
  -- Check for working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++
  -- Check for working CXX compiler: /opt/rh/devtoolset-7/root/usr/bin/c++ -- works
  -- Detecting CXX compiler ABI info
  -- Detecting CXX compiler ABI info - done
  -- Detecting CXX compile features
  -- Detecting CXX compile features - done
  -- Looking for pthread.h
  -- Looking for pthread.h - found
  -- Looking for pthread_create
  -- Looking for pthread_create - not found
  -- Looking for pthread_create in pthreads
  -- Looking for pthread_create in pthreads - not found
  -- Looking for pthread_create in pthread
  -- Looking for pthread_create in pthread - found
  -- Found Threads: TRUE
  -- Found torch: /local/miniconda3/lib/python3.7/site-packages/torch/lib/libtorch.so
  -- Configuring done
  -- Generating done
  -- Build files have been written to: /cpp_inference_example/build
$ make -j
  Scanning dependencies of target custom_class
  [ 25%] Building CXX object custom_class_project/CMakeFiles/custom_class.dir/class.cpp.o
  [ 50%] Linking CXX shared library libcustom_class.so
  [ 50%] Built target custom_class
  Scanning dependencies of target infer
  [ 75%] Building CXX object CMakeFiles/infer.dir/infer.cpp.o
  [100%] Linking CXX executable infer
  [100%] Built target infer

现在我们可以运行令人兴奋的 C++ 二进制文件：

$ ./infer
  momfoobarbaz

难以置信！

将自定义类移入或移出IValue

也可能需要将自定义类从自定义 C++ 类实例移入或移出IValue, such as when you take or return IValues from TorchScript methods or you want to instantiate a custom class attribute in C++. For creating an IValue：

• torch::make_custom_class<T>()提供类似于c10::intrusive_ptr<T>的 API，因为它将采用您提供给它的任何参数集，调用与该参数集匹配的T的构造器，并包装该实例，然后返回。 但是，它不仅返回指向自定义类对象的指针，还返回包装对象的IValue。 然后，您可以将此IValue直接传递给 TorchScript。
• 如果您已经有一个指向类的intrusive_ptr，则可以使用构造器IValue(intrusive_ptr<T>)直接从其构造IValue。

要将IValue转换回自定义类：

• IValue::toCustomClass<T>()将返回一个intrusive_ptr<T>，指向IValue包含的自定义类。 在内部，此函数正在检查T是否已注册为自定义类，并且IValue实际上确实包含一个自定义类。 您可以通过调用isCustomClass()来手动检查IValue是否包含自定义类。

为自定义 C++ 类定义序列化/反序列化方法

如果您尝试将具有自定义绑定 C++ 类的ScriptModule保存为属性，则会出现以下错误：

# export_attr.py
import torch
torch.classes.load_library('build/libcustom_class.so')
class Foo(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.stack = torch.classes.my_classes.MyStackClass(["just", "testing"])
    def forward(self, s: str) -> str:
        return self.stack.pop() + s
scripted_foo = torch.jit.script(Foo())
scripted_foo.save('foo.pt')
loaded = torch.jit.load('foo.pt')
print(loaded.stack.pop())

$ python export_attr.py
RuntimeError: Cannot serialize custom bound C++ class __torch__.torch.classes.my_classes.MyStackClass. Please define serialization methods via def_pickle for this class. (pushIValueImpl at ../torch/csrc/jit/pickler.cpp:128)

这是因为 TorchScript 无法自动找出 C++ 类中保存的信息。 您必须手动指定。 这样做的方法是使用class_上的特殊def_pickle方法在类上定义__getstate__和__setstate__方法。

注意

TorchScript 中__getstate__和__setstate__的语义与 Python pickle模块的语义相同。 您可以阅读更多有关如何使用这些方法的信息。

这是def_pickle调用的示例，我们可以将其添加到MyStackClass的注册中以包括序列化方法：

    // class_<>::def_pickle allows you to define the serialization
    // and deserialization methods for your C++ class.
    // Currently, we only support passing stateless lambda functions
    // as arguments to def_pickle
    .def_pickle(
          // __getstate__
          // This function defines what data structure should be produced
          // when we serialize an instance of this class. The function
          // must take a single `self` argument, which is an intrusive_ptr
          // to the instance of the object. The function can return
          // any type that is supported as a return value of the TorchScript
          // custom operator API. In this instance, we've chosen to return
          // a std::vector<std::string> as the salient data to preserve
          // from the class.
          [](const c10::intrusive_ptr<MyStackClass<std::string>>& self)
              -> std::vector<std::string> {
            return self->stack_;
          },
          // __setstate__
          // This function defines how to create a new instance of the C++
          // class when we are deserializing. The function must take a
          // single argument of the same type as the return value of
          // `__getstate__`. The function must return an intrusive_ptr
          // to a new instance of the C++ class, initialized however
          // you would like given the serialized state.
          [](std::vector<std::string> state)
              -> c10::intrusive_ptr<MyStackClass<std::string>> {
            // A convenient way to instantiate an object and get an
            // intrusive_ptr to it is via `make_intrusive`. We use
            // that here to allocate an instance of MyStackClass<std::string>
            // and call the single-argument std::vector<std::string>
            // constructor with the serialized state.
            return c10::make_intrusive<MyStackClass<std::string>>(std::move(state));
          });

注意

我们在 Pickle API 中采用与pybind11不同的方法。pybind11作为传递给class_::def()的特殊函数pybind11::pickle()，为此我们有一个单独的方法def_pickle。 这是因为torch::jit::pickle这个名称已经被使用了，我们不想引起混淆。

以这种方式定义（反）序列化行为后，脚本现在可以成功运行：

$ python ../export_attr.py
testing

定义接受或返回绑定 C++ 类的自定义运算符

定义自定义 C++ 类后，您还可以将该类用作自变量或从自定义运算符返回（即自由函数）。 假设您具有以下自由函数：

c10::intrusive_ptr<MyStackClass<std::string>> manipulate_instance(const c10::intrusive_ptr<MyStackClass<std::string>>& instance) {
  instance->pop();
  return instance;
}

您可以在TORCH_LIBRARY块中运行以下代码来注册它：

    m.def(
      "foo::manipulate_instance(__torch__.torch.classes.my_classes.MyStackClass x) -> __torch__.torch.classes.my_classes.MyStackClass Y",
      manipulate_instance
    );

有关注册 API 的更多详细信息，请参见自定义操作教程。

完成此操作后，您可以像以下示例一样使用操作：

class TryCustomOp(torch.nn.Module):
    def __init__(self):
        super(TryCustomOp, self).__init__()
        self.f = torch.classes.my_classes.MyStackClass(["foo", "bar"])
    def forward(self):
        return torch.ops.foo.manipulate_instance(self.f)

注意

注册使用 C++ 类作为参数的运算符时，要求已注册自定义类。 您可以通过确保自定义类注册和您的自由函数定义在同一TORCH_LIBRARY块中，并确保自定义类注册位于第一位来强制实现此操作。 将来，我们可能会放宽此要求，以便可以按任何顺序进行注册。

总结

本教程向您介绍了如何向 TorchScript（以及扩展为 Python）公开 C++ 类，如何注册其方法，如何从 Python 和 TorchScript 使用该类以及如何使用该类保存和加载代码以及运行该代码。 在独立的 C++ 过程中。 现在，您可以使用与第三方 C++ 库连接的 C++ 类扩展 TorchScript 模型，或实现需要 Python，TorchScript 和 C++ 之间的界线平滑融合的任何其他用例。

与往常一样，如果您遇到任何问题或疑问，可以使用我们的论坛或 GitHub ISSUE 进行联系。 另外，我们的常见问题解答（FAQ）页面可能包含有用的信息。