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:
any alternative implementation which wants to support C extensions needs to either follow the same low-level layout or to provide a compatibility layer.
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, GraalPython 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_tarray) 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 (
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
cpython-37mis the ABI tag, in this case CPython 3.7
x86_64is the CPU architecture
linux-gnuis the operating system
The same source code compiled on PyPy3.6 7.2.0 results in a file called
pypy3-72is 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.
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 inlinefunctions 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.,
- 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.universalpackage, 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
1is 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
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
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, GraalPython 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 Debug 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.
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.hand 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 2021, the following milestones have been reached:
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).
one can implement custom types.
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.
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
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 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
handles are intended to be short-lived, but sometimes one needs a long-lived reference to a Python object. In HPy, we call this long-lived reference an HPyField, but we still need to implement it. We also need HPy_Store and HPy_Load to save and load these fields. This will allow alternative implementations to use a moving GC.
add C-level module state. Often an extension needs module state that is accessible from C (e.g. if a module implements a new type ArrayType, many of the module methods written in C may need access to ArrayType) but HPy does not have convenient support for this yet.
improve the debug mode so that it returns a C traceback showing where a handle was leaked or a closed handle was used.
there is no integration with Cython. The medium-term plan is to extend Cython to automatically generate HPy-compatible C code.
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-hpycompiled with the CPython ABI is as fast as the original
A bit surprisingly,
ujson-hpycompiled 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.
ujson-hpyruns 3x faster than the original
ujson. Note the HPy implementation on PyPy is not fully optimized yet, so we expect even bigger speedups eventually.
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:
rust-hpy (fork of the cpython crate)