HPy overview

Motivation and goals

The biggest quality of the Python ecosystem is to have a huge number of high quality libraries for all kind of jobs. Many of them, especially in the scientific community, are written in C and exposed to Python using the Python/C API. However, the Python/C API exposes a number of CPython’s implementation details and low-level data structure layouts. This has two important consequences:

  1. any alternative implementation which wants to support C extensions needs to either follow the same low-level layout or to 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. The main goal of HPy is provide a C API which is possible to implement in an efficient way on a number of very diverse implementations. 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.

Hide internal details

The API is designed to allow a lot of flexibility for Python implementations, allowing the possibility to explore different choices to the ones used by CPython. In particular, reference counting is not part of the API: we want a more generic way of managing resources which is possible to implement with different strategies, including the existing reference counting and/or with a moving Garbage Collector (like the ones used by PyPy or Java, for example).

Moreover, we want to avoid exposing internal details of a specific implementation, so that each implementation can experiment with new memory layout of objects, add optimizations, etc.


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.


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, and 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 supports it. PyPy supports it natively. CPython supports it by using the hpy.universal package, and there is a small speed penalty compared to the CPython ABI. The ABI tag has not been formally defined yet, but it will be something like hpy-1, where 1 is the version of the API.

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.

Moreover, each alternative Python implementation could decide to implement its own non-universal ABI if it makes sense for them. For example, a hypotetical 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.

C extensions

If you are writing a Python extension in C, you are a consumer of the HPy API. There are three big advantages in using HPy instead of the old Python/C API:

  • Speed on PyPy and other alternative implementations: 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 debugging mode, many common mistakes are checked and reported automatically.

  • Universal binaries: you can choose to distribute only Universal ABI binaries. This comes with a small speed penalty on CPython, but for non-performance critical libraries it might still be a good tradeoff.

Cython extensions

If you use Cython, you can’t use HPy directly. The plan is to write a 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

At the moment of writing, HPy is still in its early stages of development. The following milestones have been reached:

  • it is possible to write extensions which expose module-level functions, with all the various kinds of calling conventions

  • there is a limited support for argument parsing (only a couple of basic types actually work)

  • there is support for raising and catching exceptions

  • 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:

  • it is not possible to write custom types (like NumPy’s ndarray) in C. There is already a WIP branch to address this issue

  • only a handful of the original Python/C functions have been ported to HPy. Porting most of them is straighforward, so for now the priority is to work on the “hard” features to prove that the HPy approach works, and we will port new functions as needed

  • the debug mode simply does not exist (yet!)

  • there is no standard/easy way to integrate HPy into a distutils/setuptools based workflow. The only way to compile HPy extensions right now is to manually clone the git repo and tweak your setup.py. Eventually, we plan to offer a workflow which integrates seamlessly with pip, setuptools, etc.

  • there is no integration with Cython. The medium-term plan is to extend Cython to automatically generate HPy-compatible C code

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)