Using CppInterop¶

C++ Language Interoperability Layer¶

Loading Dynamic shared library¶

The CppInterop comes with using it is a dynamic shared library, libclangCppInterOp.so which resides in the CppInterOp/build/lib/.

libInterop = ctypes.CDLL("./libclangCppInterOp.so")

The above method of usage is for Python; for C, we can include the headers of the library. Including this library in our program enables the user to use the abilities of CppInterOp. CppInterOp helps programmers with multiple verifications such as isClass, isBoolean, isStruct, and many more in different languages. With the interop layer, we can access the scopes, namespaces of classes and members that are being used. The interoperability layer helps us with the instantiation of templates, diagnostic interaction, creation of objects, and many more things.

Using LLVM as external library¶

In CppInterOp, we are leveraging Clang as a library for interoperability purposes. To use Clang, we need to pass the Clang configuration to the CMake build system, so that the build system recognizes the configuration and enables usage of Clang and LLVM. We can consider clang-repl as a state manager, where CppInterOp allows you to query the state from the state manager. Thereafter, cppyy uses this to create Python objects for C++.

# Apply the patches for development
compgen -G "../patches/llvm/clang16-*.patch" > /dev/null
&& find ../patches/llvm/clang16-*.patch -printf "%f\n"
&& git apply ../patches/llvm/clang16-*.patch

# Use the versions of llvm with above patches applied
-DClang_DIR=/usr/lib/llvm-16/build/lib/cmake/clang\
-DLLVM_DIR=/usr/lib/llvm-16/build/lib/cmake/llvm

This section briefly describes all the key features offered by CppInterop. If you are just getting started with CppInterop, then this is the best place to start.

Incremental Adoption¶

CppInterOp can be adopted incrementally. While the rest of the framework is the same, a small part of CppInterOp can be utilized. More components may be adopted over time.

Minimalist by design¶

While the library includes some tricky code, it is designed to be simple and robust (simple function calls, no inheritance, etc.). The goal is to make it as close to the compiler API as possible, and each routine should do just one thing. that it was designed for.

How cppyy leverages CppInterOp¶

cppyy is a run-time Python-C++ bindings generator for calling C++ from Python and Python from C++. Interestingly, it uses C++ interactively by using the compiler as a service. This is made possible by the CppInterOp library. Following are some of the ways cppyy leverages CppInterOp for better performance and usability.

CppInterOp enables interoperability with C++ code: CppInterOp provides a minimalist and robust interface for language interoperability on the fly, which helps CPPYY generate dynamic Python-C++ bindings by using a C++ interpreter (e.g., Clang-REPL/Cling) and LLVM.
Reducing dependencies: Reducing domain-specific dependencies of cppyy (e.g., on the Cling interpreter and the ROOT framework) to enable more generalized usage.
LLVM Integration: CppInterOp is designed to be used as a part of the LLVM toolchain (as part of Clang-REPL) that can then be used as a runtime compiler for CPPYY. This simplifies the codebase of CPPYY and enhances its performance.

Making C++ More Social: CppInterOp and cppyy help data scientists that are working with legacy C++ code experiment with simpler, more interactive languages, while also interacting with larger communities.

CppInterOp enables interoperability with C++ code

cppyy is a major use case for CppInterOp. cppyy is an automatic run-time bindings generator for Python and C++, and supports a wide range of C++ features, including template instantiation. It operates on demand and generates only what is necessary. It requires a compiler (Cling or Clang-REPL) that can be available during program runtime.

Reducing Dependencies

Recent work done on cppyy has been focused on reducing dependencies on domain-specific infrastructure (e.g., the ROOT framework). Using an independent library such as CppInterOp helps accomplish that, while also improving the code consistency in cppyy.

The CppInterOp library can be configured to use the newly developed Clang-Repl backend available in LLVM upstream (or to use the Cling legacy backend, for compatibility with High Energy Physics applications).

Only a small set of APIs are needed to connect to the interpreter (Clang-Repl/ Cling), since other APIs are already available in the standard compiler. This is one of the reasons that led to the creation of CppInterOp (a library of helper functions), that can help extract out things that are unnecessary for for core cppyy functionality.

The cppyy API surface is now incomparably smaller and simpler than what it used to be.

LLVM Integration

Once CppInterOp is integrated with LLVM’s Clang-REPL component (that can then be used as a runtime compiler for cppyy), it will further enhance cppyy’s performance in the following ways:

Simpler codebase: The removal of string parsing logic will lead to a simpler code base.
Built into the LLVM toolchain: The CppInterOp depends only on the LLVM toolchain (as part of Clang-REPL).
Better C++ Support: Finer-grained control over template instantiation is available through CppInterOp.
Fewer Lines of Code: A lot of dependencies and workarounds will be removed, reducing the lines of code required to execute cppyy.
Well tested interoperability Layer: The CppInterOp interfaces have full unit test coverage.

Making C++ More Social

cppyy is the first use case demonstrating how CppInterOp can enable C++ to be more easily interoperable with other languages. This helps many data scientists that are working with legacy C++ code and would like to use simpler, more interactive languages.

The goal of these enhancements is to eventually land these interoperability tools (including CppInterOp) to broader communities like LLVM and Clang, to enable C++ to interact with other languages besides Python.

Example: Template Instantiation¶

The developmental cppyy version can run basic examples such as the one here. Features such as standalone functions and basic classes are also supported.

C++ code (Tmpl.h)

template <typename T>
struct Tmpl {
  T m_num;
  T add (T n) {
    return m_num + n;
}
};

Python Interpreter

>>> import cppyy
>>> cppyy.include("Tmpl.h")
>>> tmpl = Tmpl[int]()
>>> tmpl.m_num = 4
>>> print(tmpl.add(5))
9
>>> tmpl = Tmpl[float]()
>>> tmpl.m_num = 3.0
>>> print(tmpl.add(4.0))
7.0

Where does the cppyy code reside?¶

Following are the main components where cppyy logic (with Compiler Research Organization’s customizations started by sudo-panda) resides:

Note: These are forks of the upstream cppyy repos created by wlav.

CppInterOp is a separate library that helps these packages communicate with C++ code.

CppInterOp

How cppyy components interact with each other¶

cppyy is made up of the following packages:

A frontend: cppyy,
A backend: cppyy-backend, and
An extension: CPyCppyy.

Besides these, the CppInterOp library serves as an additional layer on top of Cling/Clang-REPL that helps these packages in communicating with C++ code.

1. cppyy-backend

The `cppyy-backend`_ package forms a layer over cppyy, for example, modifying some functionality to provide the functions required for CPyCppyy.

`CPyCppyy`_ is a CPython extension module built on top of the same backend API as PyPy/_cppyy. It thus requires the installation of the cppyy-backend for use, which will pull in Cling.

cppyy-backend also adds some utilities to help with repackaging and redistribution.

For example, cppyy-backend initializes the interpreter (using the clingwrapper::ApplicationStarter function), adds the required include paths, and adds the headers required for cppyy to work. It also adds some checks and combines two or more functions to help CPyCppyy work.

These changes help ensure that any change in cppyy doesn’t directly affect CPyCppyy, and the API for CPyCppyy remains unchanged.

2. CPyCppyy

The CPyCppyy package uses the functionality provided by cppyy-backend and provides Python objects for C++ entities. CPyCppyy uses separate proxy classes for each type of object. It also includes helper classes, for example, Converters.cxx helps convert Python type objects to C++ type objects, while Executors.cxx is used to execute a function and convert its return value to a Python object, so that it can be used inside Python.

3. cppyy

The cppyy package provides the front-end for Python. It is included in code (using import cppyy) to import cppyy in Python. It initializes things on the backend side, provides helper functions (e.g., cppdef(), cppexec(), etc.) that the user can utilize, and it calls the relevant backend functions required to initialize cppyy.

CppInterOp

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