PyPy Status Blog: December 2019

Recently Antonio, Armin and Ronan had a small internal sprint in the beautiful city of Gdańsk to kick-off the development of HPy. Here is a brief report of what was accomplished during the sprint.

What is HPy?

The TL;DR answer is "a better way to write C extensions for Python".

The idea of HPy was born during EuroPython 2019 in Basel, where there was an informal meeting which included core developers of PyPy, CPython (Victor Stinner and Mark Shannon) and Cython (Stefan Behnel). The ideas were later also discussed with Tim Felgentreff of GraalPython, to make sure they would also be applicable to this very different implementation, Windel Bouwman of RustPython is following the project as well.

All of us agreed that the current design of the CPython C API is problematic for various reasons and, in particular, because it is too tied to the current internal design of CPython. The end result is that:

alternative implementations of Python (such as PyPy, but not only) have a hard time loading and executing existing C extensions;
CPython itself is unable to change some of its internal implementation details without breaking the world. For example, as of today it would be impossible to switch from using reference counting to using a real GC, which in turns make it hard for example to remove the GIL, as gilectomy attempted.

HPy tries to address these issues by following two major design guidelines:

objects are referenced and passed around using opaque handles, which are similar to e.g., file descriptors in spirit. Multiple, different handles can point to the same underlying object, handles can be duplicated and each handle must be released independently of any other duplicate.
The internal data structures and C-level layout of objects are not visible nor accessible using the API, so each implementation if free to use what fits best.

The other major design goal of HPy is to allow incremental transition and porting, so existing modules can migrate their codebase one method at a time. Moreover, Cython is considering to optionally generate HPy code, so extension module written in Cython would be able to benefit from HPy automatically.

More details can be found in the README of the official HPy repository.

Target ABI

When compiling an HPy extension you can choose one of two different target ABIs:

HPy/CPython ABI: in this case, hpy.h contains a set of macros and static inline functions. At compilation time this translates the HPy API into the standard C-API. The compiled module will have no performance penalty, and it will have a "standard" filename like foo.cpython-37m-x86_64-linux-gnu.so.
Universal HPy ABI: as the name implies, extension modules compiled this way are "universal" and can be loaded unmodified by multiple Python interpreters and versions. Moreover, it will be possible to dynamically enable a special debug mode which will make it easy to find e.g., open handles or memory leaks, without having to recompile the extension.

Universal modules can also be loaded on CPython, thanks to the hpy_universal module which is under development. An extra layer of indirection enables loading extensions compiled with the universal ABI. Users of hpy_universal will face a small performance penalty compared to the ones using the HPy/CPython ABI.

This setup gives several benefits:

Extension developers can use the extra debug features given by the Universal ABI with no need to use a special debug version of Python.
Projects which need the maximum level of performance can compile their extension for each relevant version of CPython, as they are doing now.
Projects for which runtime speed is less important will have the choice of distributing a single binary which will work on any version and implementation of Python.

A simple example

The HPy repo contains a proof of concept module. Here is a simplified version which illustrates what a HPy module looks like:

#include "hpy.h"

HPy_DEF_METH_VARARGS(add_ints)
static HPy add_ints_impl(HPyContext ctx, HPy self, HPy *args, HPy_ssize_t nargs)
{
    long a, b;
    if (!HPyArg_Parse(ctx, args, nargs, "ll", &a, &b))
        return HPy_NULL;
    return HPyLong_FromLong(ctx, a+b);
}


static HPyMethodDef PofMethods[] = {
    {"add_ints", add_ints, HPy_METH_VARARGS, ""},
    {NULL, NULL, 0, NULL}
};

static HPyModuleDef moduledef = {
    HPyModuleDef_HEAD_INIT,
    .m_name = "pof",
    .m_doc = "HPy Proof of Concept",
    .m_size = -1,
    .m_methods = PofMethods
};


HPy_MODINIT(pof)
static HPy init_pof_impl(HPyContext ctx)
{
    HPy m;
    m = HPyModule_Create(ctx, &moduledef);
    if (HPy_IsNull(m))
        return HPy_NULL;
    return m;
}

People who are familiar with the current C-API will surely notice many similarities. The biggest differences are:

Instead of PyObject *, objects have the type HPy, which as explained above represents a handle.
You need to explicitly pass an HPyContext around: the intent is primary to be future-proof and make it easier to implement things like sub- interpreters.
HPy_METH_VARARGS is implemented differently than CPython's METH_VARARGS: in particular, these methods receive an array of HPy and its length, instead of a fully constructed tuple: passing a tuple makes sense on CPython where you have it anyway, but it might be an unnecessary burden for alternate implementations. Note that this is similar to the new METH_FASTCALL which was introduced in CPython.
HPy relies a lot on C macros, which most of the time are needed to support the HPy/CPython ABI compilation mode. For example, HPy_DEF_METH_VARARGS expands into a trampoline which has the correct C signature that CPython expects (i.e., PyObject (*)(PyObject *self, *PyObject *args)) and which calls add_ints_impl.

Sprint report and current status

After this long preamble, here is a rough list of what we accomplished during the week-long sprint and the days immediatly after.

On the HPy side, we kicked-off the code in the repo: at the moment of writing the layout of the directories is a bit messy because we moved things around several times, but we identified several main sections:

A specification of the API which serves both as documentation and as an input for parts of the projects which are automatically generated. Currently, this lives in public_api.h.
A set of header files which can be used to compile extension modules: depending on whether the flag -DHPY_UNIVERSAL_ABI is passed to the compiler, the extension can target the HPy/CPython ABI or the HPy Universal ABI
A CPython extension module called hpy_universal which makes it possible to import universal modules on CPython
A set of tests which are independent of the implementation and are meant to be an "executable specification" of the semantics. Currently, these tests are run against three different implementations of the HPy API:
- the headers which implements the "HPy/CPython ABI"
- the hpy_universal module for CPython
- the hpy_universal module for PyPy (these tests are run in the PyPy repo)

Moreover, we started a PyPy branch in which to implement the hpy_univeral module: at the moment of writing PyPy can pass all the HPy tests apart the ones which allow conversion to and from PyObject *. Among the other things, this means that it is already possible to load the very same binary module in both CPython and PyPy, which is impressive on its own :).

Finally, we wanted a real-life use case to show how to port a module to HPy and to do benchmarks. After some searching, we choose ultrajson, for the following reasons:

it is a real-world extension module which was written with performance in mind
when parsing a JSON file it does a lot of calls to the Python API to construct the various parts of the result message
it uses only a small subset of the Python API

This repo contains the HPy port of ultrajson. This commit shows an example of what the porting looks like.

ujson_hpy is also a very good example of incremental migration: so far only ujson.loads is implemented using the HPy API, while ujson.dumps is still implemented using the old C-API, and both can coexist nicely in the same compiled module.

Benchmarks

Once we have a fully working ujson_hpy module, we can finally run benchmarks! We tested several different versions of the module:

ujson: this is the vanilla implementation of ultrajson using the C-API. On PyPy this is executed by the infamous cpyext compatibility layer, so we expect it to be much slower than on CPython
ujson_hpy: our HPy port compiled to target the HPy/CPython ABI. We expect it to be as fast as ujson
ujson_hpy_universal: same as above but compiled to target the Universal HPy ABI. We expect it to be slightly slower than ujson on CPython, and much faster on PyPy.

Finally, we also ran the benchmark using the builtin json module. This is not really relevant to HPy, but it might still be an interesting as a reference data point.

The benchmark is very simple and consists of parsing a big JSON file 100 times. Here is the average time per iteration (in milliseconds) using the various versions of the module, CPython 3.7 and the latest version of the hpy PyPy branch:

	CPython	PyPy
ujson	154.32	633.97
ujson_hpy	152.19
ujson_hpy_universal	168.78	207.68
json	224.59	135.43

As expected, the benchmark proves that when targeting the HPy/CPython ABI, HPy doesn't impose any performance penalty on CPython. The universal version is ~10% slower on CPython, but gives an impressive 3x speedup on PyPy! It it worth noting that the PyPy hpy module is not fully optimized yet, and we expect to be able to reach the same performance as CPython for this particular example (or even more, thanks to our better GC).

All in all, not a bad result for two weeks of intense hacking :)

It is also worth noting than PyPy's builtin json module does really well in this benchmark, thanks to the recent optimizations that were described in an earlier blog post.

Conclusion and future directions

We think we can be very satisfied about what we have got so far. The development of HPy is quite new, but these early results seem to indicate that we are on the right track to bring Python extensions into the future.

At the moment, we can anticipate some of the next steps in the development of HPy:

Think about a proper API design: what we have done so far has been a "dumb" translation of the API we needed to run ujson. However, one of the declared goal of HPy is to improve the design of the API. There will be a trade-off between the desire of having a clean, fresh new API and the need to be not too different than the old one, to make porting easier. Finding the sweet spot will not be easy!
Implement the "debug" mode, which will help developers to find bugs such as leaking handles or using invalid handles.
Instruct Cython to emit HPy code on request.
Eventually, we will also want to try to port parts of numpy to HPy to finally solve the long-standing problem of sub-optimal numpy performance in PyPy.

Stay tuned!

PyPy Status Blog

Tuesday, December 24, 2019

PyPy 7.3.0 released

What is PyPy?

What else is new?

Wednesday, December 18, 2019