HPy overview

Motivation and goals

The superpower of the Python ecosystem is its libraries, which are developed by users. Over time, these libraries have grown in number, quality, and applicability. While it is possible to write python libraries entirely in python, many of them, especially in the scientific community, are written in C and exposed to Python using the Python/C API. The existence of these C extensions using the Python/C API leads to some issues:

  1. Usually, alternative implementation of the Python programming language want to support C extensions. To do so, they must implement the same Python/C API or provide a compatibility layer.

  2. CPython developers cannot experiment with new designs or refactoring without breaking compatibility with existing extensions.

Over the years, it has become evident that emulating the Python/C API in an efficient way is challenging, if not impossible. To summarize, it is mainly due to leaking of implementation details of CPython into the C/API - which makes it difficult to make different design choices than those made by CPython. As such - the main goal of HPy is to provide a C API which makes as few assumptions as possible about the design decisions of any implementation of Python, allowing diverse implementations to support it efficiently and without compromise. In particular, reference counting is not part of the API: we want a more generic way of managing resources that is possible to impelement with different strategies, including the existing reference counting and/or with a moving Garbage Collector (like the ones used by PyPy, GraalPython or Java, for example). Moreover, each implementation can experiment with new memory layout of objects, add optimizations, etc. The following is a list of sub-goals.

Performance on CPython

HPy is usable on CPython from day 1 with no performance impact compared to the existing Python/C API.

Incremental adoption

It is possible to port existing C extensions piece by piece and to use the old and the new API side-by-side during the transition.

Easy migration

It should be easy to migrate existing C extensions to HPy. Thanks to an appropriate and regular naming convention it should be obvious what the HPy equivalent of any existing Python/C API is. When a perfect replacement does not exist, the documentation explains what the alternative options are.

Better debugging

In debug mode, you get early and precise errors and warnings when you make some specific kind of mistakes and/or violate the API rules and assumptions. For example, you get an error if you try to use a handle (see Handles) which has already been closed. It is possible to turn on the debug mode at startup time, without needing to recompile.

Simplicity

The HPy API aims to be smaller and easier to study/use/manage than the existing Python/C API. Sometimes there is a trade-off between this goal and the others above, in particular Performance on CPython and Easy migration. The general approach is to have an API which is “as simple as possible” while not violating the other goals.

Universal binaries

It is possible to compile extensions to a single binary which is ABI-compatible across multiple Python versions and/or multiple implementation. See Target ABIs.

Opt-in low level data structures

Internal details might still be available, but in a opt-in way: for example, if Cython wants to iterate over a list of integers, it can ask if the implementation provides a direct low-level access to the content (e.g. in the form of a int64_t[] array) and use that. But at the same time, be ready to handle the generic fallback case.

API vs ABI

HPy defines both an API and an ABI. Before digging further into details, let’s distinguish them:

  • The API works at the level of source code: it is the set of functions, macros, types and structs which developers can use to write their own extension modules. For C programs, the API is generally made available through one or more header files (*.h).

  • The ABI works at the level of compiled code: it is the interface between the host interpreter and the compiled DLL. Given a target CPU and operating system it defines things like the set of exported symbols, the precise memory layout of objects, the size of types, etc.

In general it is possible to compile the same source into multiple compiled libraries, each one targeting a different ABI. PEP 3149 states that the filename of the compiled extension should contain the ABI tag to specify what the target ABI is. For example, if you compile an extension called simple.c on CPython 3.7, you get a DLL called simple.cpython-37m-x86_64-linux-gnu.so:

  • cpython-37m is the ABI tag, in this case CPython 3.7

  • x86_64 is the CPU architecture

  • linux-gnu is the operating system

The same source code compiled on PyPy3.6 7.2.0 results in a file called simple.pypy3-72-x86_64-linux-gnu.so:

  • pypy3-72 is the ABI tag, in this case “PyPy3.x”, version “7.2.x”

The HPy C API is exposed to the user by including hpy.h and it is explained in its own section of the documentation.

Target ABIs

Depending on the compilation options, an HPy extension can target three different ABIs:

CPython ABI

In this mode, HPy is implemented as a set of C macros and static inline functions which translate the HPy API into the CPython API at compile time. The result is a compiled extension which is indistinguishable from a “normal” one and can be distributed using all the standard tools and will run at the very same speed. The ABI tag is defined by the version of CPython which is used to compile it (e.g., cpython-37m).

HPy Universal ABI

As the name suggests, the HPy Universal ABI is designed to be loaded and executed by a variety of different Python implementations. Compiled extensions can be loaded unmodified on all the interpreters which support it. PyPy and GraalPython support it natively. CPython supports it by using the hpy.universal package, and there is a small speed penalty 1 compared to the CPython ABI. The ABI tag has not been formally defined yet.

HPy Hybrid ABI

To allow an incremental transition to HPy, it is possible to use both HPy and Python/C API calls in the same extension. In this case, it is not possible to target the Universal ABI because the resulting compiled library also needs to be compatible with a specific CPython version. The ABI tag will be something like hpy-1_cpython-37m. Note: the tag is not implemented yet. Currently, the approach to use HPy in hybrid mode is to build the extension in HPy universal mode, which, for now, still allows mixing the HPy and CPython APIs. Extensions mixing the HPy and CPython APIs will not work on Pythons that do not support the hybrid ABI.

Moreover, each alternative Python implementation could decide to implement its own non-universal ABI if it makes sense for them. For example, a hypothetical project DummyPython could decide to ship its own hpy.h which implements the HPy API but generates a DLL which targets the DummyPython ABI.

This means that to compile an extension for CPython, you can choose whether to target the CPython ABI or the Universal ABI. The advantage of the former is that it runs at native speed, while the advantage of the latter is that you can distribute a single binary, although with a small speed penalty on CPython. Obviously, nothing stops you compiling and distributing both versions: this is very similar to what most projects are already doing, since they automatically compile and distribute extensions for many different CPython versions.

From the user point of view, extensions compiled for the CPython ABI can be distributed and installed as usual, while those compiled for the HPy Universal or HPy Hybrid ABIs require installing the hpy.universal package on CPython and have no further requirements on Pythons that support HPy natively.

Benefits for the Python ecosystem

The HPy project offers some benefits to the python ecosystem, both to Python users and to library developers.

  • C extensions can achieve much better speed on alternative implementions, including PyPy and GraalPython: according to early Early benchmarks, an extension written in HPy can be ~3x faster than the equivalent extension written in Python/C.

  • Improved debugging: when you load extensions in Debug Mode, many common mistakes are checked and reported automatically.

  • Universal binaries: libraries can choose to distribute only Universal ABI binaries. By doing so, they can support all Python implementations and version of CPython (like PyPy, GraalPython, CPython 3.10, CPython 3.11, etc) for which an HPy loader exists, including those that do not yet exist! This currently comes with a small speed penalty on CPython, but for non-performance critical libraries it might still be a good tradeoff.

  • Python environments: With general availability of universal ABI binaries for popular packages, users can create equivalent python environments that target different Python implementations. Thus, Python users can try their workload against different implementations and pick the one best suited for their usage.

  • In a situation where most or all popular Python extensions target the universal ABI, it will be more feasible for CPython to make breaking changes to its C/API for performance or maintainability reasons.

Cython extensions

If you use Cython, you can’t use HPy directly. There is a work in progress to add Cython backend which emits HPy code instead of Python/C code: once this is done, you will get the benefits of HPy automatically.

Extensions in other languages

On the API side, HPy is designed with C in mind, so it is not directly useful if you want to write an extension in a language other than C.

However, Python bindings for other languages could decide to target the HPy Universal ABI instead of the CPython ABI, and generate extensions which can be loaded seamlessly on all Python implementations which supports it. This is the route taken, for example, by Rust.

Benefits for alternative Python implementations

If you are writing an alternative Python implementation, there is a good chance that you already know how painful it is to support the Python/C API. HPy is designed to be both faster and easier to implement!

You have two choices:

  • support the Universal ABI: in this case, you just need to export the needed functions and to add a hook to dlopen() the desired libraries

  • use a custom ABI: in this case, you have to write your own replacement for hpy.h and recompile the C extensions with it.

Current status and roadmap

HPy is still in the early stages of development, but many big pieces are already in place. As on April 2022, the following milestones have been reached:

  • some real-world Python packages have been ported to HPy API. The ports will be published soon.

  • one can write extensions which expose module-level functions, with all the various kinds of calling conventions.

  • there is support for argument parsing (i.e., the equivalents of PyArg_ParseTuple and PyArg_ParseTupleAndKeywords), and a convenient complex value building (i.e., the equivalent Py_BuildValue).

  • one can implement custom types, whose struct may contain references to other Python objects using HPyField.

  • there is a support for globally accessible Python object handles: HPyGlobal, which can still provide isolation for subinterpreters if needed.

  • there is support for raising and catching exceptions.

  • debug mode has been implemented and can be activated at run-time without recompiling. It can detect leaked handles or handles used after being closed.

  • wheels can be build for HPy extensions with python setup.py bdist_wheel and can be installed with pip install.

  • it is possible to choose between the CPython ABI and the HPy Universal ABI when compiling an extension module.

  • extensions compiled with the CPython ABI work out of the box on CPython.

  • it is possible to load HPy Universal extensions on CPython, thanks to the hpy.universal package.

  • it is possible to load HPy Universal extensions on PyPy (using the PyPy hpy branch).

  • it is possible to load HPy Universal extensions on GraalPython.

However, there is still a long road before HPy is usable for the general public. In particular, the following features are on our roadmap but have not been implemented yet:

  • many of the original Python/C functions have not been ported to HPy yet. Porting most of them is straightforward, so for now the priority is to test HPy with real-world Python packages and primarily resolve the “hard” features to prove that the HPy approach works.

  • add C-level module state to complement the HPyGlobal approach. While HPyGlobal is easier to use, it will make the migration simpler for existing extensions that use CPython module state.

  • the integration with Cython is work in progress

  • it is not clear yet how to approach pybind11 and similar C++ bindings. They serve two use-cases:

    • As C++ wrappers for CPython API. HPy is fundamentally different in some ways, so fully compatible pybind11 port of this API to HPy does not make sense. There can be a similar or even partially pybind11 compatible C++ wrapper for HPy adhering to the HPy semantics and conventions (e.g., passing the HPyContext pointer argument around, no reference stealing, etc.).

    • Way to expose (or “bind”) mostly pure C++ functions as Python functions where the C++ templating machinery takes care of the conversion between the Python world, i.e., PyObject*, and the C++ types. Porting this abstraction to HPy is possible and desired in the future. To determine the priority or such effort, we need to get more knowledge about existing pybind11 use-cases.

Early benchmarks

To validate our approach, we ported a simple yet performance critical module to HPy. We chose ultrajson because it is simple enough to require porting only a handful of API functions, but at the same time it is performance critical and performs many API calls during the parsing of a JSON file.

This blog post explains the results in more detail, but they can be summarized as follows:

  • ujson-hpy compiled with the CPython ABI is as fast as the original ujson.

  • A bit surprisingly, ujson-hpy compiled with the HPy Universal ABI is only 10% slower on CPython. We need more evidence than a single benchmark of course, but if the overhead of the HPy Universal ABI is only 10% on CPython, many projects may find it small enough that the benefits of distributing extensions using only the HPy Universal ABI out weight the performance costs.

  • On PyPy, ujson-hpy runs 3x faster than the original ujson. Note the HPy implementation on PyPy is not fully optimized yet, so we expect even bigger speedups eventually.

Projects involved

HPy was born during EuroPython 2019, were a small group of people started to discuss the problems of the Python/C API and how it would be nice to have a way to fix them. Since then, it has gathered the attention and interest of people who are involved in many projects within the Python ecosystem. The following is a (probably incomplete) list of projects whose core developers are involved in HPy, in one way or the other. The mere presence in this list does not mean that the project as a whole endorse or recognize HPy in any way, just that some of the people involved contributed to the code/design/discussions of HPy:

  • PyPy

  • CPython

  • Cython

  • GraalPython

  • RustPython

  • rust-hpy (fork of the cpython crate)