PyPy Status Blog

PyPy's blog has moved

2021年03月10日T07:16:00.000+01:00

For many years, PyPy has been publishing blog posts here at https://morepypy.blogspot.com. From now on, the posts will be at https://pypy.org/blog. The RSS feed has moved to https://pypy.org/rss.xml. The original content has been migrated to the newer site, including comments.

Among the motivations for the move were:

One site to rule them all

Adding the blog posts on pypy.org seems like a natural extension of the web site rather than outsourcing it to a third-party. Since the site is generated using the static site generator nikola from the github repo https://github.com/pypy/pypy.org, we now have good source control for the content.

CI previews, and github

Those of you who follow PyPy may note something new in the URL for the repo: until now PyPy has been using mercurial as hosted on https://foss.heptapod.net. While heptapod (a community driven effort to bring mercurial support to GitLabTM) does provide a GitLab CI runner for the open source offering, it is easier to use netlify for previews on github. Hopefully the move to the more popular github platform will encourage new contributors to publish their success stories around using PyPy and the RPython toolchain.

Comments

Comments to blog posts are generated via the utterances javascript plugin. The comments appear as issues in the repo. When viewing the site, a query is made to fetch the comments to the issue with that name. To comment, users must authorize the utterances app to post on their behalf using the GitHub OAuth flow. Alternatively, users can comment on the GitHub issue directly. The interaction with github for authentication and moderation seems more natural than the manual moderation required on blogspot.

Please prove to us that the move is worth it

Help us with guest blog posts, and PRs to improve the styling of the site. One already open issue is that the navbar needlessly uses javascript, help to keep the responsive style in pure CSS is welcome.

The PyPy Team

Mac meets Arm64

2020年12月31日T10:53:00.002+01:00

Looking for sponsorship

Apple now ships Macs which are running on an arm64 variant machine with the latest version of MacOS, Big Sur M1. We are getting requests for PyPy to support this new architecture. Here is our position on this topic (or at least mine, Armin Rigo's), and how you can help.

Porting PyPy is harder than just re-running the compiler, because PyPy contains a few big architecture-dependent "details", like the JIT compiler and the foreign function interfaces (CFFI and ctypes).

Fixing the JIT compiler should not be too much work: we already support arm64, just the Linux one. But Apple made various details different (like the calling conventions). A few other parts need to be fixed too, notably CFFI and ctypes, again because of the calling conventions.

Fixing that would be a reasonable amount of work. I would do it myself for a small amount of money. However, the story doesn't finish here. Obviously, the start of the story would be to get ssh access to a Big Sur M1 machine. (If at this point you're thinking "sure, I can give you ssh access for three months", then please read on.) The next part of the story is that we need a machine available long term. It can be either a machine provided and maintained by a third party, or alternatively a pot of money big enough to support the acquision of a machine and ongoing work of one of us.

If we go with the provided-machine solution: What we need isn't a lot of resources. Our CI requires maybe 10 GB of disk space, and a few hours of CPU per run. It should fit into 8 GB of RAM. We normally do a run every night but we can certainly lower the frequency a bit if that would help. However, we'd ideally like some kind of assurance that you are invested into maintaining the machine for the next 3-5 years (I guess, see below). We had far too many machines that disappeared after a few months.

If we go with the money-supported solution: it's likely that after 3-5 years the whole Mac base will have switched to arm64, we'll drop x86-64 support for Mac, and we'll be back to the situation of the past where there was only one kind of Mac machine to care about. In the meantime, we are looking at 3-5 years of lightweight extra maintenance. We have someone that has said he would do it, but not for free.

If either of these two solutions occurs, we'll still have, I quote, "probably some changes in distutils-type stuff to make python happy", and then some packaging/deployment changes to support the "universal2" architecture, i.e. including both versions inside a single executable (which will not be just an extra switch to clang, because the two versions need a different JIT backend and so must be translated separately).

So, now all the factors are on the table. We won't do the minimal "just the JIT compiler fixes" if we don't have a plan that goes farther. Either we get sufficient money, and maybe support, and then we can do it quickly; or PyPy will just remain not natively available on M1 hardware for the next 3-5 years. We are looking forward to supporting M1, and view resources contributed by the community as a vote of confidence in assuring the future of PyPy on this hardware. Contact us: pypy-dev@python.org, or our private mailing list pypy-z@python.org.

Thanks for reading!

Armin Rigo

PyPy 7.3.3 triple release: python 3.7, 3.6, and 2.7

2020年11月21日T20:32:00.000+01:00

The PyPy team is proud to release the version 7.3.3 of PyPy, which includes three different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.18 (updated from the previous version)
PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.12 (updated from the previous version).
PyPy3.7 beta: which is our second release of an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.9. We call this beta quality software, there may be issues about compatibility with new and changed features in CPython 3.7. Please let us know what is broken or missing. We have not implemented the documented changes in the re module, and a few other pieces are also missing. For more information, see the PyPy 3.7 wiki page

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the 7.3 releases, but read on to find out what is new.

Several issues found in the 7.3.2 release were fixed. Many of them came from the great work by conda-forge to ship PyPy binary packages. A big shout out to them for taking this on.

Development of PyPy has moved to https://foss.heptapod.net/pypy/pypy. This was covered more extensively in this blog post. We have seen an increase in the number of drive-by contributors who are able to use gitlab + mercurial to create merge requests.

The CFFI backend has been updated to version 1.14.3. We recommend using CFFI rather than c-extensions to interact with C, and using cppyy for performant wrapping of C++ code for Python.

A new contributor took us up on the challenge to get windows 64-bit support. The work is proceeding on the win64 branch, more help in coding or sponsorship is welcome. In anticipation of merging this large change, we fixed many test failures on windows.

As always, this release fixed several issues and bugs. We strongly recommend updating. Many of the fixes are the direct result of end-user bug reports, so please continue reporting issues as they crop up.

You can find links to download the v7.3.3 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on pypy, or general help with making RPython’s JIT even better. Since the previous release, we have accepted contributions from 2 new contributors, thanks for pitching in.

If you are a python library maintainer and use c-extensions, please consider making a cffi / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy.

What is PyPy?

PyPy is a Python interpreter, a drop-in replacement for CPython 2.7, 3.6, and 3.7. It’s fast (PyPy and CPython 3.7.4 performance comparison) due to its integrated tracing JIT compiler.

We also welcome developers of other dynamic languages to see what RPython can do for them.

This PyPy release supports:

x86 machines on most common operating systems (Linux 32/64 bits, Mac OS X 64 bits, Windows 32 bits, OpenBSD, FreeBSD)
big- and little-endian variants of PPC64 running Linux,
s390x running Linux
64-bit ARM machines running Linux.

PyPy does support ARM 32 bit processors, but does not release binaries.

What else is new?

For more information about the 7.3.3 release, see the full changelog.

Please update, and continue to help us make PyPy better.

Cheers,
The PyPy team

PyPy 7.3.2 triple release: python 2.7, 3.6, and 3.7

2020年09月25日T07:45:00.000+02:00

The PyPy team is proud to release version 7.3.2 of PyPy, which includes three different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13
PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9.
PyPy3.7 alpha: which is our first release of an interpreter supporting the syntax and the features of Python 3.7, including the stdlib for CPython 3.7.9. We call this an alpha release since it is our first. It is based off PyPy 3.6 so issues should be around compatibility and not stability. Please try it out and let us know what is broken or missing. We have not implemented some of the documented changes in the re module, and other pieces are also missing. For more information, see the PyPy 3.7 wiki page

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, all APIs are compatible with the 7.3.0 (Dec 2019) and 7.3.1 (April 2020) releases, but read on to find out what is new.

Conda Forge now supports PyPy as a python interpreter. The support is quite complete for linux and macOS. This is the result of a lot of hard work and good will on the part of the Conda Forge team. A big shout out to them for taking this on.

Development of PyPy has transitioning to https://foss.heptapod.net/pypy/pypy. This move was covered more extensively in this blog post. We have seen an increase in the number of drive-by contributors who are able to use gitlab + mercurial to create merge requests.

The CFFI backend has been updated to version 1.14.2. We recommend using CFFI rather than c-extensions to interact with C, and using cppyy for performant wrapping of C++ code for Python.

NumPy has begun shipping wheels on PyPI for PyPy, currently for linux 64-bit only. Wheels for PyPy windows will be available from the next NumPy release. Thanks to NumPy for their support.

A new contributor took us up on the challenge to get windows 64-bit support. The work is proceeding on the win64 branch, more help in coding or sponsorship is welcome.

You can find links to download the v7.3.2 releases here:

https://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. Please help support us at Open Collective. If PyPy is not yet good enough for your needs, we are available for direct consulting work.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7, 3.6, and 3.7. It’s fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.

We also welcome developers of other dynamic languages to see what RPython can do for them.

This PyPy release supports:

x86 machines on most common operating systems (Linux 32/64 bits, Mac OS X 64 bits, Windows 32 bits, OpenBSD, FreeBSD)
big- and little-endian variants of PPC64 running Linux,
s390x running Linux
64-bit ARM machines running Linux.

PyPy does support ARM 32 bit processors, but does not release binaries.

What else is new?

For more information about the 7.3.2 release, see the full changelog.

Please update, and continue to help us make PyPy better.

Cheers,
The PyPy team

PyPy is on Open Collective

2020年08月29日T12:53:00.000+02:00

Hi all,

PyPy is now a member of Open Collective, a fiscal host. We have been thinking about switching to this organization for a couple of years; we like it for various reasons, like the budget transparency and the lightweight touch. We can now officially announce our membership!

With this, we are now again free to use PyPy for all financial issues, like receiving funds professionally, paying parts of sprint budgets as we like, and so on. We will shortly be reintroducing buttons that link to Open Collective from the PyPy web site.

Although the old donation buttons were removed last year, we believe that there are still a few people that send regularly money to the SFC, the not-for-profit charity we were affiliated with. If you do, please stop doing it now (and, if you like to do so, please set up an equivalent donation to PyPy on Open Collective).

And by the way, sorry for all of you who were getting mixed feelings from the previous blog post (co-written with the SFC). PyPy is committed to continue being Open Source just like before. This was never in question. What these two blog posts mean is only that we switched to a different organization for our internal finances.

We're looking forward to how this new relationship will go!

Armin Rigo, for the PyPy team

A new chapter for PyPy

2020年08月12日T20:00:00.000+02:00

PyPy winds down its membership in the Software Freedom Conservancy

Conservancy and PyPy's great work together

PyPy joined Conservancy in the second half of 2010, shortly after the release of PyPy 1.2, the first version to contain a fully functional JIT. In 2013, PyPy started supporting ARM, bringing its just-in-time speediness to many more devices and began working toward supporting NumPy to help scientists crunch their numbers faster. Together, PyPy and Conservancy ran successful fundraising drives and facilitated payment and oversight for contractors and code sprints.

Conservancy supported PyPy's impressive growth as it expanded support for different hardware platforms, greatly improved the performance of C extensions, and added support for Python 3 as the language itself evolved.

The road ahead

Conservancy provides a fiscal and organizational home for projects that find the freedoms and guardrails that come along with a charitable home advantageous for their community goals. While this framework was a great fit for the early PyPy community, times change and all good things must come to an end.

PyPy will remain a free and open source project, but the community's structure and organizational underpinnings will be changing and the PyPy community will be exploring options outside of the charitable realm for its next phase of growth ("charitable" in the legal sense -- PyPy will remain a community project).

During the last year PyPy and Conservancy have worked together to properly utilise the generous donations made by stalwart PyPy enthusiats over the years and to wrap up PyPy's remaining charitable obligations. PyPy is grateful for the Conservancy's help in shepherding the project toward its next chapter.

Thank yous

From Conservancy:

"We are happy that Conservancy was able to help PyPy bring important software for the public good during a critical time in its history. We wish the community well and look forward to seeing it develop and succeed in new ways."

— Karen Sandler, Conservancy's Executive Director

From PyPy:

"PyPy would like to thank Conservancy for their decade long support in building the community and wishes Conservancy continued success in their journey promoting, improving, developing and defending free and open source sofware."

— Simon Cross & Carl Friedrich Bolz-Tereick, on behalf of PyPy.

About

PyPy is a multi-layer python interpreter with a built-in JIT compiler that runs Python quickly across different computing environments. Software Freedom Conservancy (Conservancy) is a charity that provides a home to over forty free and open source software projects.

PyPy 7.3.1 released

2020年04月10日T15:29:00.000+02:00

The PyPy team is proud to release the version 7.3.1 of PyPy, which includes two different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13

PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9.

The interpreters are based on much the same codebase, thus the multiple release. This is a micro release, no APIs have changed since the 7.3.0 release in December, but read on to find out what is new.

Conda Forge now supports PyPy as a Python interpreter. The support right now is being built out. After this release, many more c-extension-based packages can be successfully built and uploaded. This is the result of a lot of hard work and good will on the part of the Conda Forge team. A big shout out to them for taking this on.

We have worked with the Python packaging group to support tooling around building third party packages for Python, so this release updates the pip and setuptools installed when executing pypy -mensurepip to pip>=20. This completes the work done to update the PEP 425 python tag from pp373 to mean "PyPy 7.3 running python3" to pp36 meaning "PyPy running Python 3.6" (the format is recommended in the PEP). The tag itself was changed in 7.3.0, but older pip versions build their own tag without querying PyPy. This means that wheels built for the previous tag format will not be discovered by pip from this version, so library authors should update their PyPy-specific wheels on PyPI.

Development of PyPy is transitioning to https://foss.heptapod.net/pypy/pypy. This move was covered more extensively in the blog post from last month.

The CFFI backend has been updated to version 14.0. We recommend using CFFI rather than c-extensions to interact with C, and using cppyy for performant wrapping of C++ code for Python. The cppyy backend has been enabled experimentally for win32, try it out and let use know how it works.

Enabling cppyy requires a more modern C compiler, so win32 is now built with MSVC160 (Visual Studio 2019). This is true for PyPy 3.6 as well as for 2.7.

We have improved warmup time by up to 20%, performance of io.StringIO to match if not be faster than CPython, and improved JIT code generation for generators (and generator expressions in particular) when passing them to functions like sum, map, and map that consume them. Performance of closures has also be improved in certain situations.

As always, this release fixed several issues and bugs raised by the growing community of PyPy users. We strongly recommend updating. Many of the fixes are the direct result of end-user bug reports, so please continue reporting issues as they crop up.
You can find links to download the v7.3.1 releases here:

http://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython’s JIT even better. Since the previous release, we have accepted contributions from 13 new contributors, thanks for pitching in.

If you are a Python library maintainer and use c-extensions, please consider making a cffi / cppyy version of your library that would be performant on PyPy. In any case both cibuildwheel and the multibuild system support building wheels for PyPy wheels.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7, 3.6, and soon 3.7. It’s fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.

We also welcome developers of other dynamic languages to see what RPython can do for them.

This PyPy release supports:

x86 machines on most common operating systems (Linux 32/64 bits, Mac OS X 64 bits, Windows 32 bits, OpenBSD, FreeBSD)

big- and little-endian variants of PPC64 running Linux,

s390x running Linux

64-bit ARM machines running Linux.

What else is new?

For more information about the 7.3.1 release, see the full changelog.

Please update, and continue to help us make PyPy better.

Cheers,
The PyPy team

The PyPy Team

Leysin 2020 Sprint Report

2020年03月17日T22:57:00.001+01:00

At the end of February ten of us gathered in Leysin, Switzerland to work on
a variety of topics including HPy, PyPy Python 3.7 support and the PyPy
migration to Heptapod.

We had a fun and productive week. The snow was beautiful. There was skiing
and lunch at the top of Berneuse, cooking together, some late nights at
the pub next door, some even later nights coding, and of course the
obligatory cheese fondue outing.

There were a few of us participating in a PyPy sprint for the first time
and a few familiar faces who had attended many sprints. Many different
projects were represented including PyPy, HPy, GraalPython,
Heptapod, and rust-cpython. The atmosphere was relaxed and welcoming, so if
you're thinking of attending the next one -- please do!

Topics worked on:

HPy

HPy is a new project to design and implement a better API for extending
Python in C. If you're unfamiliar with it you can read more about it at
HPy.

A lot of attention was devoted to the Big HPy Design Discussion which
took up two full mornings. So much was decided that this will likely
get its own detailed write-up, but bigger topics included:

the HPy GetAttr, SetAttr, GetItem and SetItem methods,
HPy_FromVoidP and HPy_AsVoidP for passing HPy handles to C functions
that pass void* pointers to callbacks,
avoiding having va_args as part of the ABI,
exception handling,
support for creating custom types.

Quite a few things got worked on too:

implemented support for writing methods that take keyword arguments with
HPy_METH_KEYWORDS,
implemented HPy_GetAttr, HPy_SetAttr, HPy_GetItem, and HPy_SetItem,
started implementing support for adding custom types,
started implementing dumping JSON objects in ultrajson-hpy,
refactored the PyPy GIL to improve the interaction between HPy and
PyPy's cpyext,
experimented with adding HPy support to rust-cpython.

And there was some discussion of the next steps of the HPy initiative
including writing documentation, setting up websites and funding, and
possibly organising another HPy gathering later in the year.

PyPy

Georges gave a presentation on the Heptapod topic and branch workflows
and showed everyone how to use hg-evolve.
Work was done on improving the PyPy CI buildbot post the move to
heptapod, including a light-weight pre-merge CI and restricting
when the full CI is run to only branch commits.
A lot of work was done improving the -D tests.

Miscellaneous

Armin demoed VRSketch and NaN Industries in VR, including an implementation
of the Game of Life within NaN Industries!
Skiing!

Aftermath

Immediately after the sprint large parts of Europe and the world were
hit by the COVID-19 epidemic. It was good to spend time together before
travelling ceased to be a sensible idea and many gatherings were cancelled.

Keep safe out there everyone.

The HPy & PyPy Team & Friends

In joke for those who attended the sprint: Please don't replace this blog post
with its Swedish translation (or indeed a translation to any other language :).

PyPy and CFFI have moved to Heptapod

2020年02月16日T15:36:00.000+01:00

It has been a very busy month, not so much because of deep changes in the JIT of PyPy but more around the development, deployment, and packaging of the project.

Hosting

The biggest news is that we have moved the center of our development off Bitbucket and to the new https://foss.heptapod.net/pypy. This is a friendly fork of Gitlab called heptapod that understands Mercurial and is hosted by Clever Cloud. When Atlassian decided to close down Mercurial hosting on bitbucket.org, PyPy debated what to do. Our development model is based on long-lived branches, and we want to keep the ability to immediately see which branch each commit came from. Mercurial has this, git does not (see our FAQ). Octobus, whose business is Mercurial, developed a way to use Mercurial with Gitlab called heptapod. The product is still under development, but quite usable (i.e., it doesn't get in the way). Octobus partnered with Clever Cloud hosting to offer community FOSS projects hosted on Bitbucket who wish to remain with Mercurial a new home. PyPy took them up on the offer, and migrated its repos to https://foss.heptapod.net/pypy. We were very happy with how smooth it was to import the repos to heptapod/GitLab, and are learning the small differences between Bitbucket and GitLab. All the pull requests, issues, and commits kept the same ids, but work is still being done to attribute the issues, pull requests, and comments to the correct users. So from now on, when you want to contribute to PyPy, you do so at the new home.

CFFI, which previously was also hosted on Bitbucket, has joined the PyPy group at https://foss.heptapod.net/pypy/cffi.

Website

Secondly, thanks to work by https://baroquesoftware.com/ in leading a redesign and updating the logo, the https://www.pypy.org website has undergone a facelift. It should now be easier to use on small-screen devices. Thanks also to the PSF for hosting the site.

Packaging

Also, building PyPy from source takes a fair amount of time. While we provide downloads in the form of tarballs or zipfiles, and some platforms such as debian and Homebrew provide packages, traditionally the downloads have only worked on a specific flavor of operating system. A few years ago squeaky-pl started providing portable builds. We have adopted that build system for our linux offerings, so the nightly downloads and release downloads should now work on any glibc platform that has not gone EndOfLife. So there goes another excuse not to use PyPy. And the "but does it run scipy" excuse also no longer holds, although "does it speed up scipy" still has the wrong answer. For that we are working on HPy, and will be sprinting soon.
The latest versions of pip, wheel, and setuptools, together with the manylinux2010 standard for linux wheels and tools such as multibuild or cibuildwheels (well, from the next version) make it easier for library developers to build binary wheels for PyPy. If you are having problems getting going with this, please reach out.

Give it a try

Thanks to all the folks who provide the infrastructure PyPy depends on. We hope the new look will encourage more involvement and engagement. Help prove us right!

The PyPy Team

Leysin Winter sprint 2020: Feb 29 - March 8th

2020年01月17日T11:36:00.000+01:00

The next PyPy sprint will be in Leysin, Switzerland, for the fourteenth time. This is a fully public sprint: newcomers and topics other than those proposed below are welcome.

Goals and topics of the sprint

The list of topics is open. For reference, we would like to work at least partially on the following topics:

HPy
Python 3.7 support (buildbot status)

As usual, the main side goal is to have fun in winter sports :-) We can take a day off (for ski or anything else).

Times and accomodation

The sprint will occur for one week starting on Saturday, the 29th of February, to Sunday, the 8th of March 2020 (dates were pushed back one day!) It will occur in Les Airelles, a different bed-and-breakfast place from the traditional one in Leysin. It is a nice old house at the top of the village.

We have a 4- or 5-people room as well as up to three double-rooms. Please register early! These rooms are not booked for the sprint in advance, and might be already taken if you end up announcing yourself late. We have a big room for up to 7 people with nice view, which might be split in two or three sub-rooms; plus possibly separately-booked double rooms if needed. (But it is of course always possible to book at a different place in Leysin.)

For more information, see our repository or write to me directly at armin.rigo@gmail.com.

PyPy 7.3.0 released

2019年12月24日T14:55:00.004+01:00

The PyPy team is proud to release the version 7.3.0 of PyPy, which includes two different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13
PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9.

The interpreters are based on much the same codebase, thus the double release.

We have worked with the python packaging group to support tooling around building third party packages for python, so this release changes the ABI tag for PyPy.

Based on the great work done in portable-pypy, the linux downloads we provide are now built on top of the manylinux2010 CentOS6 docker image. The tarballs include the needed shared objects to run on any platform that supports manylinux2010 wheels, which should include all supported versions of debian- and RedHat-based distributions (including Ubuntu, CentOS, and Fedora).

The CFFI backend has been updated to version 1.13.1. We recommend using CFFI rather than c-extensions to interact with C.
The built-in cppyy module was upgraded to 1.10.6, which provides, among others, better template resolution, stricter enum handling, anonymous struct/unions, cmake fragments for distribution, optimizations for PODs, and faster wrapper calls. We reccomend using cppyy for performant wrapping of C++ code for Python.

The vendored pyrepl package for interaction inside the REPL was updated.

Support for codepage encoding and decoding was added for Windows.

As always, this release fixed several issues and bugs raised by the growing community of PyPy users. We strongly recommend updating. Many of the fixes are the direct result of end-user bug reports, so please continue reporting issues as they crop up.
You can download the v7.3 releases here:

http://pypy.org/download.html

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work.

We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular packages to run on pypy, or general help with making RPython’s JIT even better. Since the previous release, we have accepted contributions from 3 new contributors, thanks for pitching in.

If you are a python library maintainer and use c-extensions, please consider making a cffi / cppyy version of your library that would be performant on PyPy. If you are stuck with using the C-API, you can use docker images with PyPy built in or the multibuild system to build wheels.

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7, 3.6. It’s fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.

We also welcome developers of other dynamic languages to see what RPython can do for them.

This PyPy release supports:

x86 machines on most common operating systems (Linux 32/64 bit, Mac OS X 64-bit, Windows 32-bit, OpenBSD, FreeBSD)

big- and little-endian variants of PPC64 running Linux

s390x running Linux

64-bit ARM machines running Linux

Unfortunately at the moment of writing our ARM buildbots are out of service, so for now we are not releasing any binary for the ARM architecture (32-bit), although PyPy does support ARM 32-bit processors.

What else is new?

PyPy 7.2 was released in October, 2019. There are many incremental improvements to RPython and PyPy, For more information about the 7.3.0 release, see the full changelog.

Please update, and continue to help us make PyPy better.

Cheers,
The PyPy team

HPy kick-off sprint report

2019年12月18日T14:38:00.000+01:00

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 v7.2 released

2019年10月14日T19:46:00.000+02:00

The PyPy team is proud to release the version 7.2.0 of PyPy, which includes two different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7 including the stdlib for CPython 2.7.13

PyPy3.6: which is an interpreter supporting the syntax and the features of Python 3.6, including the stdlib for CPython 3.6.9.

The interpreters are based on much the same codebase, thus the double release.

As always, this release is 100% compatible with the previous one and fixed several issues and bugs raised by the growing community of PyPy users. We strongly recommend updating. Many of the fixes are the direct result of end-user bug reports, so please continue reporting issues as they crop up.

You can download the v7.2 releases here:

http://pypy.org/download.html

With the support of Arm Holdings Ltd. and Crossbar.io, this release supports the 64-bit aarch64 ARM architecture. More about the work and the performance data around this welcome development can be found in the blog post.

This release removes the "beta" tag from PyPy3.6. While there may still be some small corner-case incompatibilities (around the exact error messages in exceptions and the handling of faulty codec errorhandlers) we are happy with the quality of the 3.6 series and are looking forward to working on a Python 3.7 interpreter.

We updated our benchmark runner at https://speed.pypy.org to a more modern machine and updated the baseline python to CPython 2.7.11. Thanks to Baroque Software for maintaining the benchmark runner.

The CFFI-based _ssl module was backported to PyPy2.7 and updated to use cryptography version 2.7. Additionally, the _hashlib, and crypt (or _crypt on Python3) modules were converted to CFFI. This has two consequences: end users and packagers can more easily update these libraries for their platform by executing (cd lib_pypy; ../bin/pypy _*_build.py). More significantly, since PyPy itself links to fewer system shared objects (DLLs), on platforms with a single runtime namespace like linux, different CFFI and c-extension modules can load different versions of the same shared object into PyPy without collision (issue 2617).

Until downstream providers begin to distribute c-extension builds with PyPy, we have made packages for some common packages available as wheels.

The CFFI backend has been updated to version 1.13.0. We recommend using CFFI rather than c-extensions to interact with C, and cppyy for interacting with C++ code.

Thanks to Anvil, we revived the PyPy Sandbox, (soon to be released) which allows total control over a Python interpreter’s interactions with the external world.

We implemented a new JSON decoder that is much faster, uses less memory, and uses a JIT-friendly specialized dictionary. More about that in the recent blog post

We would like to thank our donors for the continued support of the PyPy project. If PyPy is not quite good enough for your needs, we are available for direct consulting work.
We would also like to thank our contributors and encourage new people to join the project. PyPy has many layers and we need help with all of them: PyPy and RPython documentation improvements, tweaking popular modules to run on PyPy, or general help with making RPython’s JIT even better. Since the previous release, we have accepted contributions from 27 new contributors, so thanks for pitching in.

What is PyPy?

x86 machines on most common operating systems (Linux 32/64 bit, Mac OS X 64-bit, Windows 32-bit, OpenBSD, FreeBSD)

big- and little-endian variants of PPC64 running Linux

s390x running Linux

64-bit ARM machines running Linux

What else is new?

PyPy 7.1 was released in March, 2019. There are many incremental improvements to RPython and PyPy, For more information about the 7.2.0 release, see the full changelog.

Please update, and continue to help us make PyPy better.

Cheers,
The PyPy team

PyPy's new JSON parser

2019年10月08日T13:37:00.000+02:00

Introduction

In the last year or two I have worked on and off on making PyPy's JSON faster, particularly when parsing large JSON files. In this post I am going to document those techniques and measure their performance impact. Note that I am quite a lot more constrained in what optimizations I can apply here, compared to some of the much more advanced approaches like Mison, Sparser or SimdJSON because I don't want to change the json.loads API that Python programs expect, and because I don't want to only support CPUs with wide SIMD extensions. With a more expressive API, more optimizations would be possible.
There are a number of problems of working with huge JSON files: deserialization takes a long time on the one hand, and the resulting data structures often take a lot of memory (usually they can be many times bigger than the size of the file they originated from). Of course these problems are related, because allocating and initializing a big data structure takes longer than a smaller data structure. Therefore I always tried to attack both of these problems at the same time.
One common theme of the techniques I am describing is that of optimizing the parser for how JSON files are typically used, not how they could theoretically be used. This is a similar approach to the way dynamic languages are optimized more generally: most JITs will optimize for typical patterns of usage, at the cost of less common usage patterns, which might even become slower as a result of the optimizations.

Maps

The first technique I investigated is to use maps in the JSON parser. Maps, also called hidden classes or shapes, are a fairly common way to (generally, not just in the context of JSON parsing) optimize instances of classes in dynamic language VMs. Maps exploit the fact that while it is in theory possible to add arbitrary fields to an instance, in practice most instances of a class are going to have the same set of fields (or one of a small number of different sets). Since JSON dictionaries or objects often come from serialized instances of some kind, this property often holds in JSON files as well: dictionaries often have the same fields in the same order, within a JSON file.
This property can be exploited in two ways: on the one hand, it can be used to again store the deserialized dictionaries in a more memory efficient way by not using a hashmap in most cases, but instead splitting the dictionary into a shared description of the set of keys (the map) and an array of storage with the values. This makes the deserialized dictionaries smaller if the same set of keys is repeated a lot. This is completely transparent to the Python programmer, the dictionary will look completely normal to the Python program but its internal representation is different.
One downside of using maps is that sometimes files will contain many dictionaries that have unique key sets. Since maps themselves are quite large data structures and since dictionaries that use maps contain an extra level of indirection we want to fall back to using normal hashmaps to represent the dictionaries where that is the case. To prevent this we perform some statistics at runtime, how often every map (i.e. set of keys) is used in the file. For uncommonly used maps, the map is discarded and the dictionaries that used the map converted into using a regular hashmap.

Using Maps to Speed up Parsing

Another benefit of using maps to store deserialized dictionaries is that we can use them to speed up the parsing process itself. To see how this works, we need to understand maps a bit better. All the maps produced as a side-effect of parsing JSON form a tree. The tree root is a map that describes the object without any attributes. From every tree node we have a number of edges going to other nodes, each edge for a specific new attribute added:

This map tree is the result of parsing a file that has dictionaries with the keys a, b, c many times, the keys a, b, f less often, and also some objects with the keys x, y.
When parsing a dictionary we traverse this tree from the root, according to the keys that we see in the input file. While doing this, we potentially add new nodes, if we get key combinations that we have never seen before. The set of keys of a dictionary parsed so far are represented by the current tree node, while we can store the values into an array. We can use the tree of nodes to speed up parsing. A lot of the nodes only have one child, because after reading the first few keys of an object, the remaining ones are often uniquely determined in a given file. If we have only one child map node, we can speculatively parse the next key by doing a memcmp between the key that the map tree says is likely to come next and the characters that follow the ',' that started the next entry in the dictionary. If the memcmp returns true this means that the speculation paid off, and we can transition to the new map that the edge points to, and parse the corresponding value. If not, we fall back to general code that parses the string, handles escaping rules etc. This trick was explained to me by some V8 engineers, the same trick is supposedly used as part of the V8 JSON parser.
This scheme doesn't immediately work for map tree nodes that have more than one child. However, since we keep statistics anyway about how often each map is used as the map of a parsed dictionary, we can speculate that the most common map transition is taken more often than the others in the future, and use that as the speculated next node.
So for the example transition tree shown in the figure above the key speculation would succeed for objects with keys a, b, c. For objects with keys a, b, f the speculation would succeed for the first two keys, but not for the third key f. For objects with the keys x, y the speculation would fail for the first key x but succeed for the second key y.
For real-world datasets these transition trees can become a lot more complicated, for example here is a visualization of a part of the transition tree generated for parsing a New York Times dataset:

Caching Strings

A rather obvious observation we can use to improve performance of the parser is the fact that string values repeat a lot in most JSON files. For strings that are used as dictionary keys this is pretty obvious. However it happens also for strings that are used as values in dictionaries (or are stored in lists). We can use this fact to intern/memoize strings and save memory. This is an approach that many JSON parsers use, including CPython's. To do this, I keep a dictionary of strings that we have seen so far during parsing and look up new strings that are deserialized. If we have seen the string before, we can re-use the deserialized previous string. Right now I only consider utf-8 strings for caching that do not contain any escapes (whether stuff like \", \n or escaped unicode chars).
This simple approach works extremely well for dictionary keys, but needs a number of improvements to be a win in general. The first observation is that computing the hash to look up the string in the dictionary of strings we've seen so far is basically free. We can compute the hash while scanning the input for the end of the string we are currently deserializing. Computing the hash while scanning doesn't increase the time spent scanning much. This is not a new idea, I am sure many other parsers do the same thing (but CPython doesn't seem to).
Another improvement follows from the observation that inserting every single deserialized non-key string into a hashmap is too expensive. Instead, we insert strings into the cache more conservatively, by keeping a small ring buffer of hashes of recently deserialized strings. The hash is looked for in the ring buffer, and only if the hash is present we insert the string into the memoization hashmap. This has the effect of only inserting strings into the memoization hashmap that re-occur a second time not too far into the file. This seems to give a good trade-off between still re-using a lot of strings but keeping the time spent updating and the size of the memoization hashmap low.
Another twist is that in a lot of situations caching strings is not useful at all, because it will almost never succeed. Examples of this are UUIDs (which are unique), or the content of a tweet in a JSON file with many tweets (which is usually unique). However, in the same file it might be useful to cache e.g. the user name of the Twitter user, because many tweets from the same person could be in such a file. Therefore the usefulness of the string cache depends on which fields of objects we are deserializing the value off. Therefore we keep statistics per map field and disable string memoization per individual field if the cache hit rate falls below a certain threshold. This gives the best of both worlds: in the cases where string values repeat a lot in certain fields we use the cache to save time and memory. But for those fields that mostly contain unique strings we don't waste time looking up and adding strings in the memoization table. Strings outside of dictionaries are quite rare anyway, so we just always try to use the cache for them.
The following pseudocode sketches the code to deserialize a string in the input at a given position. The function also takes a map, which is the point in the map tree that we are currently deserializing a field off (if we are deserializing a string in another context, some kind of dummy map can be used there).


def deserialize_string(pos, input, map):
 # input is the input string, pos is the position of the starting " of
 # the string
 # find end of string, check whether it contains escape codes,
 # compute hash, all at the same time
 end, escapes, hash = find_end_of_string(pos + 1, input)
 if end == -1:
 raise ParseError
 if escapes:
 # need to be much more careful with escaping
 return deserialize_string_escapes(pos, input)
 
 # should we cache at all?
 if map.cache_disabled():
 return input[pos + 1:end]
 # if string is in cache, return it
 if hash in cache:
 map.cache_hit += 1
 return cache[hash]
 result = input[pos + 1:end]
 map.cache_miss += 1
 # if hash is in the ring buffer of recently seen hashes,
 # add the string to the cache
 if hash in ring_buffer:
 cache[hash] = result
 else:
 ring_buffer.write(hash)
 return result

Evaluation

To find out how much the various techniques help, I implemented a number of JSON parsers in PyPy with different combinations of the techniques enabled. I compared the numbers with the JSON parser of CPython 3.7.3 (simplejson), with ujson, with the JSON parser of Node 12.11.1 (V8) and with RapidJSON (in DOM mode).
I collected a number of medium-to-large JSON files to try the JSON parsers on:

Censys: A subset of the Censys port and protocol scan data for websites in the Alexa top million domains
Gharchive: Github activity from January 15-23, 2015 from Github Archive
Reddit: Reddit comments from May 2009
Rosie: The nested matches produced using the Rosie pattern language all.things pattern on a log file
Nytimes: Metadata of a collection of New York Times articles
Tpch: The TPC-H database benchmark's deals table as a JSON file
Twitter: A JSON export of the @pypyproject Twitter account data
Wikidata: A file storing a subset of the Wikidata fact dump from Nov 11, 2014
Yelp: A file of yelp businesses

Here are the file sizes of the benchmarks:

Benchmark	File Size [MiB]
Censys	898.45
Gharchive	276.34
NYTimes	12.98
Reddit	931.65
Rosie	388.88
TPCH	173.86
Wikidata	119.75
Yelp	167.61

I measured the times of each benchmark with a number of variations of the improved PyPy algorithms:

PyPyBaseline: The PyPy JSON parser as it was before my work with JSON parsing started (PyPy version 5.8)
PyPyKeyStringCaching: Memoizing the key strings of dictionaries, but not the other strings in a json file, and not using maps to represent dictionaries (this is the JSON parser that PyPy has been shipping since version 5.9, in the benchmarks I used 7.1).
PyPyMapNoCache: Like PyPyKeyStringCaching, but using maps to represent dictionaries. This includes speculatively parsing the next key using memcmp, but does not use string caching of non-key strings.
PyPyFull: Like PyPyMapNoCache but uses a string cache for all strings, not just keys. This is equivalent to what will be released soon as part of PyPy 7.2

In addition to wall clock time of parsing, I also measured the increase in memory use of each implementation after the input string has been deserialized, i.e. the size of the in-memory representation of every JSON file.

Contributions of Individual Optimizations

Let's first look at the contributions of the individual optimizations to the overall performance and memory usage.

All the benchmarks were run 30 times in new processes, all the numbers are normalized to PyPyFull.
The biggest individual improvement to both parsing time and memory used comes from caching just the keys in parsed dictionaries. This is the optimization in PyPy's JSON parser that has been implemented for a while already. To understand why this optimization is so useful, let's look at some numbers about each benchmark, namely the number of total keys across all dictionaries in each file, as well as the number of unique keys. As we can see, for all benchmarks the number of unique keys is significantly smaller than the number of keys in total.

Benchmark	Number of keys	Number of unique keys
Censys	14 404 234	163
Gharchive	6 637 881	169
NYTimes	417 337	60
Reddit	25 226 397	21
Rosie	28 500 101	5
TPCH	6 700 000	45
Wikidata	6 235 088	1 602
Yelp	5 133 914	61

The next big jump in deserialization time and memory comes from introducing maps to represent deserialized dictionaries. With PyPyMapNoCache deserialization time goes down because it's much cheaper to walk the tree of maps and store all deserialized objects into an array of values than to build hashmaps with the same keys again and again. Memory use goes down for the same reason: it takes a lot less memory to store the shared structure of each set of keys in the map, as opposed to repeating it again and again in every hashmap.
We can look at some numbers about every benchmark again. The table shows how many map-based dictionaries are deserialized for every benchmark, and how many hashmap-backed dictionaries. We see that the number of hashmap-backed dictionaries is often zero, or at most a small percentage of all dictionaries in each benchmark. Yelp has the biggest number of hashmap-backed dictionaries. The reason for this is that the input file contains hashmaps that store combinations of various features of Yelp businesses, and a lot of these combinations are totally unique to a business. Therefore the heuristics determine that it's better to store these using hashmaps.

Benchmark	Map Dicts	Regular Dicts	% Regular Dicts
Censys	4 049 235	1 042	0.03
Gharchive	955 301	0	0.00
NYTimes	80 393	0	0.00
Reddit	1 201 257	0	0.00
Rosie	6 248 966	0	0.00
TPCH	1 000 000	0	0.00
Wikidata	1 923 460	46 905	2.38
Yelp	443 140	52 051	10.51

We can also look at numbers about how often the memcmp-based speculative parsing of the next key of a given map succeeds. Looking at statistics about each benchmark, we can see that the speculation of what key we expect next pays off in a significant percentage of cases, between 63% for Wikidata where the dictionary structures are quite irregular, and 99% for Reddit, where all the dictionaries have the same set of keys.

Benchmark	Number of Keys	Map Transitions	% Successful Speculation
Censys	14 404 234	14 403 243	65.79
Gharchive	6 637 881	6 637 881	86.71
NYTimes	417 337	417 337	79.85
Reddit	25 226 397	25 226 397	100.00
Rosie	28 500 101	28 500 101	90.37
TPCH	6 700 000	6 700 000	86.57
Wikidata	6 235 088	5 267 744	63.68
Yelp	5 133 914	4 593 980	90.43
geomean			82.04

General string caching is the most unclear optimization. On the one hand its impact on memory usage is quite substantial, leading to a 20% reduction for Gharchive and Reddit, up to a ×ばつ improvement for Yelp. On the other hand, the effect on performance is less clear, since it even leads to a slowdown in Gharchive and Reddit, and generally only a small improvement. Choosing the right heuristic for when to disable the cache also has somewhat unclear effects and is definitely a topic worthy of further investigation.

Comparison against other JSON Decoders

To get a more general feeling of the performance and memory usage of the improved PyPy parser, we compare it against CPython's built-in json parser, ujson for CPython, Node's (V8) JSON parser and RapidJSON. For better context for the memory usage I also show the file size of the input files.
These benchmarks are not really an apples-to-apple comparison. All of the implementations use different in-memory representations of strings in the deserialized data-structure (Node uses two bytes per character in a string, in CPython it depends but 4 bytes on my machine), PyPyBaseline uses four bytes, PyPy and RapidJSON use utf-8). But it's still interesting to get some ballpark numbers. The results are as follows:

As we can see, PyPyFull handily beats CPython and ujson, with a geometric mean of the improvement of about ×ばつ. The memory improvement can be even more extreme, with an improvement of over ×ばつ against CPython/ujson in some cases (CPython gives better memory sizes, because its parser caches the keys of dictionaries as well). Node is often more than 50% slower, whereas RapidJSON beats us easily, by a factor of ×ばつ on average.

Conclusions

While the speedup I managed to achieve over the course of this project is nice and I am certainly happy to beat both CPython and Node, I am ultimately still annoyed that RapidJSON manages to maintain such a clear lead over PyPyFull, and would like to get closer to it. One problem that PyPy suffers compared to RapidJSON is the overhead of garbage collection. Deserializing large JSON files is pretty much the worst case for the generational GC that PyPy uses, since none of the deserialized objects die young (and the GC expects that most objects do). That means that a lot of the deserialization time of PyPy is wasted allocating the resulting objects in the nursery, and then copying them into the old generation. Somehow, this should be done in better ways, but all my attempts to not have to do the copy did not seem to help much. So maybe more improvements are possible, if I can come up with more ideas.
On the memory side of things, Node/V8 is beating PyPy clearly which might indicate more general problems in how we represent Python objects in memory. On the other hand, I think it's cool that we are competitive with RapidJSON in terms of memory and often within ×ばつ of the file size.
An effect that I didn't consider at all in this blog post is the fact that accessing the deserialized objects with constants strings is also faster than with regular dictionaries, due to them being represented with maps. More benchmarking work to do in the future!
If you have your own programs that run on PyPy and use the json parser a lot, please measure them on the new code and let me know whether you see any difference!

A second life for the Sandbox

2019年08月07日T19:31:00.001+02:00

Hi all,

Anvil is a UK-based company sponsoring one month of work to revive PyPy's "sandbox" mode and upgrade it to PyPy3. Thanks to them, sandboxing will be given a second life!

The sandboxed PyPy is a special version of PyPy that runs fully isolated. It gives a safe way to execute arbitrary Python programs (whole programs, not small bits of code inside your larger Python program). Such scripts can be fully untrusted, and they can try to do anything—there are no syntax-based restrictions, for example—but whatever they do, any communication with the external world is not actually done but delegated to the parent process. This is similar but much more flexible than Linux's Seccomp approach, and it is more lightweight than setting up a full virtual machine. It also works without operating system support.

However, during the course of the years the sandbox mode of PyPy has been mostly unmaintained and unsupported by the core developers, mostly because of a lack of interest by users and because it took too much effort to maintain it.

Now we have found that we have an actual user, Anvil. As far as I can tell they are still using a very old version of PyPy, the last one that supported sandboxing. This is where this contract comes from: the goal is to modernize sandboxing and port it to PyPy3.

Part of my motivation for accepting this work is that I may have found a way to tweak the protocol on the pipe between the sandboxed PyPy and the parent controller process. This should make the sandboxed PyPy more resilient against future developments and easier to maintain; at most, in the future some tweaks will be needed in the controller process but hopefully not deep inside the guts of the sandboxed PyPy. Among the advantages, such a more robust solution should mean that we can actually get a working sandboxed PyPy—or sandboxed PyPy3 or sandboxed version of any other interpreter written in RPython—with just an extra argument when calling rpython to translate this interpreter. If everything works as planned, sandboxing may be given a second life.

Armin Rigo

PyPy JIT for Aarch64

2019年07月25日T15:41:00.001+02:00

Hello everyone.

We are pleased to announce the availability of the new PyPy for AArch64. This port brings PyPy's high-performance just-in-time compiler to the AArch64 platform, also known as 64-bit ARM. With the addition of AArch64, PyPy now supports a total of 6 architectures: x86 (32 & 64bit), ARM (32 & 64bit), PPC64, and s390x. The AArch64 work was funded by ARM Holdings Ltd. and Crossbar.io.

PyPy has a good record of boosting the performance of Python programs on the existing platforms. To show how well the new PyPy port performs, we compare the performance of PyPy against CPython on a set of benchmarks. As a point of comparison, we include the results of PyPy on x86_64.

Note, however, that the results presented here were measured on a Graviton A1 machine from AWS, which comes with a very serious word of warning: Graviton A1's are virtual machines, and, as such, they are not suitable for benchmarking. If someone has access to a beefy enough (16G) ARM64 server and is willing to give us access to it, we are happy to redo the benchmarks on a real machine. One major concern is that while a virtual CPU is 1-to-1 with a real CPU, it is not clear to us how CPU caches are shared across virtual CPUs. Also, note that by no means is this benchmark suite representative enough to average the results. Read the numbers individually per benchmark.

The following graph shows the speedups on AArch64 of PyPy (hg id 2417f925ce94) compared to CPython (2.7.15), as well as the speedups on a x86_64 Linux laptop comparing the most recent release, PyPy 7.1.1, to CPython 2.7.16.

In the majority of benchmarks, the speedups achieved on AArch64 match those achieved on the x86_64 laptop. Over CPython, PyPy on AArch64 achieves speedups between 0.6x to 44.9x. These speedups are comparable to x86_64, where the numbers are between 0.6x and 58.9x.

The next graph compares between the speedups achieved on AArch64 to the speedups achieved on x86_64, i.e., how great the speedup is on AArch64 vs. the same benchmark on x86_64. This comparison should give a rough idea about the quality of the generated code for the new platform.

Note that we see a large variance: There are generally three groups of benchmarks - those that run at more or less the same speed, those that run at 2x the speed, and those that run at 0.5x the speed of x86_64.

The variance and disparity are likely related to a variety of issues, mostly due to differences in architecture. What is however interesting is that, compared to measurements performed on older ARM boards, the branch predictor on the Graviton A1 machine appears to have improved. As a result, the speedups achieved by PyPy over CPython are smaller than on older ARM boards: sufficiently branchy code, like CPython itself, simply runs a lot faster. Hence, the advantage of the non-branchy code generated by PyPy's just-in-time compiler is smaller.

One takeaway here is that many possible improvements for PyPy have yet to be implemented. This is true for both of the above platforms, but probably more so for AArch64, which comes with a large number of CPU registers. The PyPy backend was written with x86 (the 32-bit variant) in mind, which has a really low number of registers. We think that we can improve in the area of emitting more modern machine code, which may have a higher impact on AArch64 than on x86_64. There is also a number of missing features in the AArch64 backend. These features are currently implemented as expensive function calls instead of inlined native instructions, something we intend to improve.

Best,

Maciej Fijalkowski, Armin Rigo and the PyPy team

PyPy 7.1.1 Bug Fix Release

2019年04月18日T16:24:00.002+02:00

The PyPy team is proud to release a bug-fix release version 7.1.1 of PyPy, which includes two different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.
PyPy3.6-beta: the second official release of PyPy to support 3.6 features.

The interpreters are based on much the same codebase, thus the double release.

This bugfix fixes bugs related to large lists, dictionaries, and sets, some corner cases with unicode, and PEP 3118 memory views of ctype structures. It also fixes a few issues related to the ARM 32-bit backend. For the complete list see the changelog.

You can download the v7.1.1 releases here:

http://pypy.org/download.html

As always, this release is 100% compatible with the previous one and fixed several issues and bugs raised by the growing community of PyPy users. We strongly recommend updating.

The PyPy3.6 release is rapidly maturing, but is still considered beta-quality.

The PyPy team

An RPython JIT for LPegs

2019年04月04日T22:26:00.002+02:00

The following is a guest post by Stefan Troost, he describes the work he did in his bachelor thesis:

In this project we have used the RPython infrastructure to generate an RPython JIT for a less-typical use-case: string pattern matching. The work in this project is based on Parsing Expression Grammars and LPeg, an implementation of PEGs designed to be used in Lua. In this post I will showcase some of the work that went into this project, explain PEGs in general and LPeg in particular, and show some benchmarking results.

Parsing Expression Grammars

Parsing Expression Grammas (PEGs) are a type of formal grammar similar to context-free grammars, with the main difference being that they are unambiguous. This is achieved by redefining the ambiguous choice operator of CFGs (usually noted as |) as an ordered choice operator. In practice this means that if a rule in a PEG presents a choice, a PEG parser should prioritize the leftmost choice. Practical uses include parsing and pattern-searching. In comparison to regular expressions PEGs stand out as being able to be parsed in linear time, being strictly more powerful than REs, as well as being arguably more readable.

LPeg

LPeg is an implementation of PEGs written in C to be used in the Lua programming language. A crucial detail of this implementation is that it parses high level function calls, translating them to bytecode, and interpreting that bytecode. Therefore, we are able to improve that implementation by replacing LPegs C-interpreter with an RPython JIT. I use a modified version of LPeg to parse PEGs and pass the generated Intermediate Representation, the LPeg bytecode, to my VM.

The LPeg Library

The LPeg Interpreter executes bytecodes created by parsing a string of commands using the LPeg library. Our JIT supports a subset of the LPeg library, with some of the more advanced or obscure features being left out. Note that this subset is still powerful enough to do things like parse JSON.

Operator	Description
lpeg.P(string)	Matches string literally
lpeg.P(n)	Matches exactly n characters
lpeg.P(-n)	Matches at most n characters
lpeg.S(string)	Matches any character in string (Set)
lpeg.R("xy")	Matches any character between x and y (Range)
pattern^n	Matches at least n repetitions of pattern
pattern^-n	Matches at most n repetitions of pattern
pattern1 * pattern2	Matches pattern1 followed by pattern2
pattern1 + pattern2	Matches pattern1 or pattern2 (ordered choice)
pattern1 - pattern2	Matches pattern1 if pattern2 does not match
-pattern	Equivalent to ("" - pattern)

As a simple example, the pattern lpeg.P"ab"+lpeg.P"cd" would match either the string ab or the string cd.

To extract semantic information from a pattern, captures are needed. These are the following operations supported for capture creation.

Operation	What it produces
lpeg.C(pattern)	the match for patten plus all captures made by pattern
lpeg.Cp()	the current position (matches the empty string)

(tables taken from the LPeg documentation)

These patterns are translated into bytecode by LPeg, at which point we are able to pass them into our own VM.

The VM

The state of the VM at any point is defined by the following variables:

PC: program counter indicating the current instruction
fail: an indicator that some match failed and the VM must backtrack
index: counter indicating the current character of the input string
stackentries: stack of return addresses and choice points
captures: stack of capture objects

The execution of bytecode manipulates the values of these variables in order to produce some output. How that works and what that output looks like will be explained now.

The Bytecode

For simplicity’s sake I will not go over every individual bytecode, but instead choose some that exemplify the core concepts of the bytecode set.

generic character matching bytecodes

any: Checks if there’s any characters left in the inputstring. If it succeeds it advances the index and PC by 1, if not the bytecode fails.
char c: Checks if there is another bytecode in the input and if that character is equal to c. Otherwise the bytecode fails.
set c1-c2: Checks if there is another bytecode in the input and if that character is between (including) c1 and c2. Otherwise the bytecode fails.

These bytecodes are the easiest to understand with very little impact on the VM. What it means for a bytecode to fail will be explained when we get to control flow bytecodes.

To get back to the example, the first half of the pattern lpeg.P"ab" could be compiled to the following bytecodes:

char a
char b

control flow bytecodes

jmp n: Sets PC to n, effectively jumping to the n’th bytecode. Has no defined failure case.
testchar c n: This is a lookahead bytecode. If the current character is equal to c it advances the PC but not the index. Otherwise it jumps to n.
call n: Puts a return address (the current PC + 1) on the stackentries stack and sets the PC to n. Has no defined failure case.
ret: Opposite of call. Removes the top value of the stackentries stack (if the string of bytecodes is valid this will always be a return address) and sets the PC to the removed value. Has no defined failure case.
choice n: Puts a choice point on the stackentries stack. Has no defined failure case.
commit n: Removes the top value of the stackentries stack (if the string of bytecodes is valid this will always be a choice point) and jumps to n. Has no defined failure case.

Using testchar we can implement the full pattern lpeg.P"ab"+lpeg.P"cd" with bytecode as follows:

testchar a -> L1
any
char b
end
any
L1: char c
char d
end

The any bytecode is needed because testchar does not consume a character from the input.

Failure Handling, Backtracking and Choice Points

A choice point consist of the VM’s current index and capturestack as well as a PC. This is not the VM’s PC at the time of creating the choicepoint, but rather the PC where we should continue trying to find matches when a failure occurs later.

Now that we have talked about choice points, we can talk about how the VM behaves in the fail state. If the VM is in the fail state, it removed entries from the stackentries stack until it finds a choice point. Then it backtracks by restoring the VM to the state defined by the choice point. If no choice point is found this way, no match was found in the string and the VM halts.

Using choice points we could implement the example lpeg.P"ab" + lpeg.P"cd" in bytecodes in a different way (LPEG uses the simpler way shown above, but for more complex patterns it can’t use the lookahead solution using testchar):

choice L1
char a
char b
commit
end
L1: char c
char d
end

Captures

Some patterns require the VM to produce more output than just "the pattern matched" or "the pattern did not match". Imagine searching a document for an IPv4 address and all your program responded was "I found one". In order to recieve additional information about our inputstring, captures are used.

The capture object

In my VM, two types of capture objects are supported, one of them being the position capture. It consists of a single index referencing the point in the inputstring where the object was created.

The other type of capture object is called simplecapture. It consists of an index and a size value, which are used to reference a substring of the inputstring. In addition, simplecaptures have a variable status indicating they are either open or full. If a simplecapture object is open, that means that its size is not yet determined, since the pattern we are capturing is of variable length.

Capture objects are created using the following bytecodes:

Fullcapture Position: Pushes a positioncapture object with the current index value to the capture stack.
Fullcapture Simple n: Pushes a simplecapture object with current index value and size=n to the capture stack.
Opencapture Simple: Pushes an open simplecapture object with current index value and undetermined size to the capture stack.
closecapture: Sets the top element of the capturestack to full and sets its size value using the difference between the current index and the index of the capture object.

The RPython Implementation

These, and many more bytecodes were implemented in an RPython-interpreter. By adding jit hints, we were able to generate an efficient JIT. We will now take a closer look at some implementations of bytecodes.

...
 elif instruction.name == "any":
 if index >= len(inputstring):
 fail = True
 else:
 pc += 1
 index += 1
...

The code for the any-bytecode is relatively straight-forward. It either advances the pc and index or sets the VM into the fail state, depending on whether the end of the inputstring has been reached or not.

...
 if instruction.name == "char":
 if index >= len(inputstring):
 fail = True
 elif instruction.character == inputstring[index]:
 pc += 1
 index += 1
 else:
 fail = True
...

The char-bytecode also looks as one would expect. If the VM’s string index is out of range or the character comparison fails, the VM is put into the fail state, otherwise the pc and index are advanced by 1. As you can see, the character we’re comparing the current inputstring to is stored in the instruction object (note that this code-example has been simplified for clarity, since the actual implementation includes a jit-optimization that allows the VM to execute multiple successive char-bytecodes at once).

...
 elif instruction.name == "jmp":
 pc = instruction.goto
...

The jmp-bytecode comes with a goto value which is a pc that we want execution to continue at.

...
 elif instruction.name == "choice":
 pc += 1
 choice_points = choice_points.push_choice_point(
 instruction.goto, index, captures)
...

As we can see here, the choice-bytecode puts a choice point onto the stack that may be backtracked to if the VM is in the fail-state. This choice point consists of a pc to jump to which is determined by the bytecode. But it also includes the current index and captures values at the time the choice point was created. An ongoing topic of jit optimization is which data structure is best suited to store choice points and return addresses. Besides naive implementations of stacks and single-linked lists, more case-specific structures are also being tested for performance.

Benchmarking Result

In order to find out how much it helps to JIT LPeg patterns we ran a small number of benchmarks. We used an otherwise idle Intel Core i5-2430M CPU with 3072 KiB of cache and 8 GiB of RAM, running with 2.40GHz. The machine was running Ubuntu 14.04 LTS, Lua 5.2.3 and we used GNU grep 2.16 as a point of comparison for one of the benchmarks. The benchmarks were run 100 times in a new process each. We measured the full runtime of the called process, including starting the process.

Now we will take a look at some plots generated by measuring the runtime of different iterations of my JIT compared to lua and using bootstrapping to generate a sampling distribution of mean values. The plots contain a few different variants of pypeg, only the one called "fullops" is important for this blog post, however.

This is the plot for a search pattern that searches a text file for valid URLs. As we can see, if the input file is as small as 100 kb, the benefits of JIT optimizations do not outweigh the time required to generate the machine code. As a result, all of our attempts perform significantly slower than LPeg.

This is the plot for the same search pattern on a larger input file. As we can see, for input files as small as 500 kb our VM already outperforms LPeg’s. An ongoing goal of continued development is to get this lower boundary as small as possible.

The benefits of a JIT compared to an Interpreter become more and more relevant for larger input files. Searching a file as large as 5 MB makes this fairly obvious and is exactly the behavior we expect.

This time we are looking at a different more complicated pattern, one that parses JSON used on a 50 kb input file. As expected, LPeg outperforms us, however, something unexpected happens as we increase the filesize.

Since LPeg has a defined maximum depth of 400 for the choicepoints and returnaddresses Stack, LPeg by default refuses to parse files as small as 100kb. This raises the question if LPeg was intended to be used for parsing. Until a way to increase LPeg’s maximum stack depth is found, no comparisons to LPeg can be performed at this scale. This has been a low priority in the past but may be addressed in the future.

To conclude, we see that at sufficiently high filesizes, our JIT outperforms the native LPeg-interpreter. This lower boundary is currently as low as 100kb in filesize.

Conclusion

Writing a JIT for PEG’s has proven itself to be a challenge worth pursuing, as the expected benefits of a JIT compared to an Interpreter have been achieved. Future goals include getting LPeg to be able to use parsing patterns on larger files, further increasing the performance of our JIT and comparing it to other well-known programs serving a similar purpose, like grep.

The prototype implementation that I described in this post can be found on Github (it's a bit of a hack in some places, though).

PyPy v7.1 released; now uses utf-8 internally for unicode strings

2019年03月24日T20:14:00.000+01:00

The PyPy team is proud to release version 7.1.0 of PyPy, which includes two different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7

PyPy3.6-beta: this is the second official release of PyPy to support 3.6 features, although it is still considered beta quality.

The interpreters are based on much the same codebase, thus the double release.

This release, coming fast on the heels of 7.0 in February, finally merges the internal refactoring of unicode representation as UTF-8. Removing the conversions from strings to unicode internally lead to a nice speed bump. We merged the utf-8 changes to the py3.5 branch (Python3.5.3) but will concentrate on 3.6 going forward.

We also improved the ability to use the buffer protocol with ctype structures and arrays.

The CFFI backend has been updated to version 1.12.2. We recommend using CFFI rather than c-extensions to interact with C, and cppyy for interacting with C++ code.
You can download the v7.1 releases here:

http://pypy.org/download.html

What is PyPy?

x86 machines on most common operating systems (Linux 32/64 bits, Mac OS X 64 bits, Windows 32 bits, OpenBSD, FreeBSD)
big- and little-endian variants of PPC64 running Linux
ARM32 although we do not supply downloadable binaries at this time
s390x running Linux

What else is new?

PyPy 7.0 was released in February, 2019. There are many incremental improvements to RPython and PyPy, for more information see the changelog.

Please update, and continue to help us make PyPy better.

Cheers, The PyPy team

PyPy v7.0.0: triple release of 2.7, 3.5 and 3.6-alpha

2019年02月11日T11:55:00.001+01:00

The PyPy team is proud to release the version 7.0.0 of PyPy, which includes three different interpreters:

PyPy2.7, which is an interpreter supporting the syntax and the features of Python 2.7

PyPy3.5, which supports Python 3.5

PyPy3.6-alpha: this is the first official release of PyPy to support 3.6 features, although it is still considered alpha quality.

All the interpreters are based on much the same codebase, thus the triple release.
Until we can work with downstream providers to distribute builds with PyPy, we have made packages for some common packages available as wheels.
The GC hooks , which can be used to gain more insights into its performance, has been improved and it is now possible to manually manage the GC by using a combination of gc.disable and gc.collect_step. See the GC blog post.
We updated the cffi module included in PyPy to version 1.12, and the cppyy backend to 1.4. Please use these to wrap your C and C++ code, respectively, for a JIT friendly experience.
As always, this release is 100% compatible with the previous one and fixed several issues and bugs raised by the growing community of PyPy users. We strongly recommend updating.
The PyPy3.6 release and the Windows PyPy3.5 release are still not production quality so your mileage may vary. There are open issues with incomplete compatibility and c-extension support.
The utf8 branch that changes internal representation of unicode to utf8 did not make it into the release, so there is still more goodness coming. You can download the v7.0 releases here:

http://pypy.org/download.html

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython 2.7, 3.5 and 3.6. It's fast (PyPy and CPython 2.7.x performance comparison) due to its integrated tracing JIT compiler.
We also welcome developers of other dynamic languages to see what RPython can do for them.
The PyPy release supports:

x86 machines on most common operating systems (Linux 32/64 bits, Mac OS X 64 bits, Windows 32 bits, OpenBSD, FreeBSD)

big- and little-endian variants of PPC64 running Linux,

s390x running Linux

Unfortunately at the moment of writing our ARM buildbots are out of service, so for now we are not releasing any binary for the ARM architecture.

What else is new?

PyPy 6.0 was released in April, 2018. There are many incremental improvements to RPython and PyPy, the complete listing is here.

Please update, and continue to help us make PyPy better.

Cheers, The PyPy team

Düsseldorf Sprint Report 2019

2019年02月09日T17:13:00.000+01:00

Hello everyone!

We are happy to report a successful and well attended sprint that is wrapping up in Düsseldorf, Germany. In the last week we had eighteen people sprinting at the Heinrich-Heine-Universität Düsseldorf on various topics.

Totally serious work going on here constantly.

A big chunk of the sprint was dedicated to various discussions, since we did not manage to gather the core developers in one room in quite a while. Discussion topics included:

Funding and general sustainability of open source.
Catching up with CPython 3.7/3.8 – we are planning to release 3.6 some time in the next few months and we will continue working on 3.7/3.8.
What to do with VMprof
How can we support Cython inside PyPy in a way that will be understood by the JIT, hence fast.
The future of supporting the numeric stack on pypy – we have made significant progress in the past few years and most of the numeric stack works out of the box, but deployment and performance remain problems. Improving on those problems remains a very important focus for PyPy as a project.
Using the presence of a CPython developer (Łukasz Langa) and a Graal Python developer (Tim Felgentreff) we discussed ways to collaborate in order to improve Python ecosystem across implementations.
Pierre-Yves David and Georges Racinet from octobus gave us an exciting demo on Heptapod, which adds mercurial support to gitlab.
Maciej and Armin gave demos of their current (non-PyPy-related) project VRSketch.

Visiting the Landschaftspark Duisburg Nord on the break day

Some highlights of the coding tasks worked on:

Aarch64 (ARM64) JIT backend work has been started, we are able to run the first test! Tobias Oberstein from Crossbar GmbH and Rodolph Perfetta from ARM joined the sprint to help kickstart the project.
The long running math-improvements branch that was started by Stian Andreassen got merged after bugfixes done by Alexander Schremmer. It should improve operations on large integers.
The arcane art of necromancy was used to revive long dormant regalloc branch started and nearly finished by Carl Friedrich Bolz-Tereick. The branch got merged and gives some modest speedups across the board.
Andrew Lawrence worked on MSI installer for PyPy on windows.
Łukasz worked on improving failing tests on the PyPy 3.6 branch. He knows very obscure details of CPython (e.g. how pickling works), hence we managed to progress very quickly.
Matti Picus set up a new benchmarking server for PyPy 3 branches.
The Utf8 branch, which changes the internal representation of unicode might be finally merged at some point very soon. We discussed and improved upon the last few blockers. It gives significant speedups in a lot of cases handling strings.
Zlib was missing couple methods, which were added by Ronan Lamy and Julian Berman.
Manuel Jacob fixed RevDB failures.
Antonio Cuni and Matti Picus worked on 7.0 release which should happen in a few days.

Now we are all quite exhausted, and are looking forward to catching up on sleep.

Best regards, Maciej Fijałkowski, Carl Friedrich Bolz-Tereick and the whole PyPy team.

PyPy for low-latency systems

2019年01月03日T15:21:00.001+01:00

PyPy for low-latency systems

Recently I have merged the gc-disable branch, introducing a couple of features which are useful when you need to respond to certain events with the lowest possible latency. This work has been kindly sponsored by Gambit Research (which, by the way, is a very cool and geeky place where to work, in case you are interested). Note also that this is a very specialized use case, so these features might not be useful for the average PyPy user, unless you have the same problems as described here.

The PyPy VM manages memory using a generational, moving Garbage Collector. Periodically, the GC scans the whole heap to find unreachable objects and frees the corresponding memory. Although at a first look this strategy might sound expensive, in practice the total cost of memory management is far less than e.g. on CPython, which is based on reference counting. While maybe counter-intuitive, the main advantage of a non-refcount strategy is that allocation is very fast (especially compared to malloc-based allocators), and deallocation of objects which die young is basically for free. More information about the PyPy GC is available here.

As we said, the total cost of memory managment is less on PyPy than on CPython, and it's one of the reasons why PyPy is so fast. However, one big disadvantage is that while on CPython the cost of memory management is spread all over the execution of the program, on PyPy it is concentrated into GC runs, causing observable pauses which interrupt the execution of the user program.
To avoid excessively long pauses, the PyPy GC has been using an incremental strategy since 2013. The GC runs as a series of "steps", letting the user program to progress between each step.

The following chart shows the behavior of a real-world, long-running process:

The orange line shows the total memory used by the program, which increases linearly while the program progresses. Every ~5 minutes, the GC kicks in and the memory usage drops from ~5.2GB to ~2.8GB (this ratio is controlled by the PYPY_GC_MAJOR_COLLECT env variable).
The purple line shows aggregated data about the GC timing: the whole collection takes ~1400 individual steps over the course of ~1 minute: each point represent the maximum time a single step took during the past 10 seconds. Most steps take ~10-20 ms, although we see a horrible peak of ~100 ms towards the end. We have not investigated yet what it is caused by, but we suspect it is related to the deallocation of raw objects.

These multi-millesecond pauses are a problem for systems where it is important to respond to certain events with a latency which is both low and consistent. If the GC kicks in at the wrong time, it might causes unacceptable pauses during the collection cycle.

Let's look again at our real-world example. This is a system which continuously monitors an external stream; when a certain event occurs, we want to take an action. The following chart shows the maximum time it takes to complete one of such actions, aggregated every minute:

You can clearly see that the baseline response time is around ~20-30 ms. However, we can also see periodic spikes around ~50-100 ms, with peaks up to ~350-450 ms! After a bit of investigation, we concluded that most (although not all) of the spikes were caused by the GC kicking in at the wrong time.

The work I did in the gc-disable branch aims to fix this problem by introducing two new features to the gc module:

gc.disable(), which previously only inhibited the execution of finalizers without actually touching the GC, now disables the GC major collections. After a call to it, you will see the memory usage grow indefinitely.

gc.collect_step() is a new function which you can use to manually execute a single incremental GC collection step.

It is worth to specify that gc.disable() disables only the major collections, while minor collections still runs. Moreover, thanks to the JIT's virtuals, many objects with a short and predictable lifetime are not allocated at all. The end result is that most objects with short lifetime are still collected as usual, so the impact of gc.disable() on memory growth is not as bad as it could sound.

Combining these two functions, it is possible to take control of the GC to make sure it runs only when it is acceptable to do so. For an example of usage, you can look at the implementation of a custom GC inside pypytools. The peculiarity is that it also defines a "with nogc():" context manager which you can use to mark performance-critical sections where the GC is not allowed to run.

The following chart compares the behavior of the default PyPy GC and the new custom GC, after a careful placing of nogc() sections:

The yellow line is the same as before, while the purple line shows the new system: almost all spikes have gone, and the baseline performance is about 10% better. There is still one spike towards the end, but after some investigation we concluded that it was not caused by the GC.

Note that this does not mean that the whole program became magically faster: we simply moved the GC pauses in some other place which is not shown in the graph: in this specific use case this technique was useful because it allowed us to shift the GC work in places where pauses are more acceptable.

All in all, a pretty big success, I think. These functionalities are already available in the nightly builds of PyPy, and will be included in the next release: take this as a New Year present :)

Antonio Cuni and the PyPy team

PyPy Winter Sprint Feb 4-9 in Düsseldorf

2018年12月17日T12:40:00.000+01:00

PyPy Sprint February 4th-9th 2019 in Düsseldorf

The next PyPy sprint will be held in the Computer Science department of Heinrich-Heine Universität Düsseldorf from the 4th to the 9st of February 2019 (nine years after the last sprint there). This is a fully public sprint, everyone is welcome to join us.

Topics and goals

improve Python 3.6 support
discuss benchmarking situation
progress on utf-8 branches
cpyext performance and completeness
packaging: are we ready to upload to PyPI?

issue 2617 - we expose too many functions from lib-pypy.so
manylinux2010 - will it solve our build issues?
formulate an ABI name and upgrade policy

memoryview(ctypes.Structure) does not create the correct format string
discussing the state and future of PyPy and the wider Python ecosystem

Location

The sprint will take place in seminar room 25.12.02.55 of the computer science department. It is in the building 25.12 of the university campus, second floor. Travel instructions

Exact times

Work days: starting February 4th (10:00), ending February 9th (~afternoon). The break day will probably be Thursday.

Registration

Please register by Mercurial::
https://bitbucket.org/pypy/extradoc/

https://bitbucket.org/pypy/extradoc/src/extradoc/sprintinfo/ddorf2019/people.txt

or on the pypy-dev mailing list if you do not yet have check-in rights:

http://mail.python.org/mailman/listinfo/pypy-dev

Looking forward to seeing everyone there!

Funding for 64-bit Armv8-a support in PyPy

2018年11月29日T13:09:00.000+01:00

Hello everyone

At PyPy we are trying to support a relatively wide range of platforms. We have PyPy working on OS X, Windows and various flavors of linux (and unofficially various flavors of BSD) on the software side, with hardware side having x86, x86_64, PPC, 32-bit Arm (v7) and even zarch. This is harder than for other projects, since PyPy emits assembler on the fly from the just in time compiler and it requires significant amount of work to port it to a new platform.

We are pleased to inform that Arm Limited, together with Crossbar.io GmbH, are sponsoring the development of 64-bit Armv8-a architecture support through Baroque Software OU, which would allow PyPy to run on a new variety of low-power, high-density servers with that architecture. We believe this will be beneficial for the funders, for the PyPy project as well as to the wider community.

The work will commence soon and will be done some time early next year with expected speedups either comparable to x86 speedups or, if our current experience with ARM holds, more significant than x86 speedups.

Best,
Maciej Fijalkowski and the PyPy team

Guest Post: Implementing a Calculator REPL in RPython

2018年11月15日T09:06:00.001+01:00

This is a tutorial style post that walks through using the RPython translation toolchain to create a REPL that executes basic math expressions.

We will do that by scanning the user's input into tokens, compiling those tokens into bytecode and running that bytecode in our own virtual machine. Don't worry if that sounds horribly complicated, we are going to explain it step by step.

This post is a bit of a diversion while on my journey to create a compliant lox implementation using the RPython translation toolchain. The majority of this work is a direct RPython translation of the low level C guide from Bob Nystrom (@munificentbob) in the excellent book craftinginterpreters.com specifically the chapters 14 – 17.

The road ahead

As this post is rather long I'll break it into a few major sections. In each section we will have something that translates with RPython, and at the end it all comes together.

REPL
Virtual Machine
Scanning the source
Compiling Expressions
End to end

A REPL

So if you're a Python programmer you might be thinking this is pretty trivial right?

I mean if we ignore input errors, injection attacks etc couldn't we just do something like this:

"""
A pure python REPL that can parse simple math expressions
"""
while True:
 print(eval(raw_input("> ")))

Well it does appear to do the trick:

$ python2 section-1-repl/main.py
> 3 + 4 * ((1.0/(2 * 3 * 4)) + (1.0/(4 * 5 * 6)) - (1.0/(6 * 7 * 8)))
3.1880952381

So can we just ask RPython to translate this into a binary that runs magically faster?

Let's see what happens. We need to add two functions for RPython to get its bearings (entry_point and target) and call the file targetXXX:

targetrepl1.py

def repl():
 while True:
 print eval(raw_input('> '))
def entry_point(argv):
 repl()
 return 0
def target(driver, *args):
 return entry_point, None

Which at translation time gives us this admonishment that accurately tells us we are trying to call a Python built-in raw_input that is unfortunately not valid RPython.

$ rpython ./section-1-repl/targetrepl1.py
...SNIP...
[translation:ERROR] AnnotatorError: 
object with a __call__ is not RPython: <built-in function raw_input>
Processing block:
 block@18 is a <class 'rpython.flowspace.flowcontext.SpamBlock'> 
 in (target1:2)repl 
 containing the following operations: 
 v0 = simple_call((builtin_function raw_input), ('> ')) 
 v1 = simple_call((builtin_function eval), v0) 
 v2 = str(v1) 
 v3 = simple_call((function rpython_print_item), v2) 
 v4 = simple_call((function rpython_print_newline))

Ok so we can't use raw_input or eval but that doesn't faze us. Let's get the input from a stdin stream and just print it out (no evaluation).

targetrepl2.py

from rpython.rlib import rfile
LINE_BUFFER_LENGTH = 1024
def repl(stdin):
 while True:
 print "> ",
 line = stdin.readline(LINE_BUFFER_LENGTH)
 print line
def entry_point(argv):
 stdin, stdout, stderr = rfile.create_stdio()
 try:
 repl(stdin)
 except:
 return 0
def target(driver, *args):
 return entry_point, None

Translate targetrepl2.py – we can add an optimization level if we are so inclined:

$ rpython --opt=2 section-1-repl/targetrepl2.py
...SNIP...
[Timer] Timings:
[Timer] annotate --- 1.2 s
[Timer] rtype_lltype --- 0.9 s
[Timer] backendopt_lltype --- 0.6 s
[Timer] stackcheckinsertion_lltype --- 0.0 s
[Timer] database_c --- 15.0 s
[Timer] source_c --- 1.6 s
[Timer] compile_c --- 1.9 s
[Timer] =========================================
[Timer] Total: --- 21.2 s

No errors!? Let's try it out:

$ ./target2-c 
1 + 2
> 1 + 2
^C

Ahh our first success – let's quickly deal with the flushing fail by using the stdout stream directly as well. Let's print out the input in quotes:

from rpython.rlib import rfile
LINE_BUFFER_LENGTH = 1024
def repl(stdin, stdout):
 while True:
 stdout.write("> ")
 line = stdin.readline(LINE_BUFFER_LENGTH)
 print '"%s"' % line.strip()
def entry_point(argv):
 stdin, stdout, stderr = rfile.create_stdio()
 try:
 repl(stdin, stdout)
 except:
 pass
 return 0
def target(driver, *args):
 return entry_point, None

Translation works, and the test run too:

$ ./target3-c 
> hello this seems better
"hello this seems better"
> ^C

So we are in a good place with taking user input and printing output... What about the whole math evaluation thing we were promised? For that we are can probably leave our RPython REPL behind for a while and connect it up at the end.

A virtual machine

A virtual machine is the execution engine of our basic math interpreter. It will be very simple, only able to do simple tasks like addition. I won't go into any depth to describe why we want a virtual machine, but it is worth noting that many languages including Java and Python make this decision to compile to an intermediate bytecode representation and then execute that with a virtual machine. Alternatives are compiling directly to native machine code like (earlier versions of) the V8 JavaScript engine, or at the other end of the spectrum executing an abstract syntax tree – which is what the Truffle approach to building VMs is based on.

We are going to keep things very simple. We will have a stack where we can push and pop values, we will only support floats, and our VM will only implement a few very basic operations.

OpCodes

In fact our entire instruction set is:

OP_CONSTANT
OP_RETURN
OP_NEGATE
OP_ADD
OP_SUBTRACT
OP_MULTIPLY
OP_DIVIDE

Since we are targeting RPython we can't use the nice enum module from the Python standard library, so instead we just define a simple class with class attributes.

We should start to get organized, so we will create a new file opcodes.py and add this:

class OpCode:
 OP_CONSTANT = 0
 OP_RETURN = 1
 OP_NEGATE = 2
 OP_ADD = 3
 OP_SUBTRACT = 4
 OP_MULTIPLY = 5
 OP_DIVIDE = 6

Chunks

To start with we need to get some infrastructure in place before we write the VM engine.

Following craftinginterpreters.com we start with a Chunk object which will represent our bytecode. In RPython we have access to Python-esq lists so our code object will just be a list of OpCode values – which are just integers. A list of ints, couldn't get much simpler.

section-2-vm/chunk.py

class Chunk:
 code = None
 def __init__(self):
 self.code = []
 def write_chunk(self, byte):
 self.code.append(byte)
 def disassemble(self, name):
 print "== %s ==\n" % name
 i = 0
 while i < len(self.code):
 i = disassemble_instruction(self, i)

From here on I'll only present minimal snippets of code instead of the whole lot, but I'll link to the repository with the complete example code. For example the various debugging including disassemble_instruction isn't particularly interesting to include verbatim. See the github repo for full details

We need to check that we can create a chunk and disassemble it. The quickest way to do this is to use Python during development and debugging then every so often try to translate it.

Getting the disassemble part through the RPython translator was a hurdle for me as I quickly found that many str methods such as format are not supported, and only very basic % based formatting is supported. I ended up creating helper functions for string manipulation such as:

def leftpad_string(string, width, char=" "):
 l = len(string)
 if l > width:
 return string
 return char * (width - l) + string

Let's write a new entry_point that creates and disassembles a chunk of bytecode. We can set the target output name to vm1 at the same time:

targetvm1.py

def entry_point(argv):
 bytecode = Chunk()
 bytecode.write_chunk(OpCode.OP_ADD)
 bytecode.write_chunk(OpCode.OP_RETURN)
 bytecode.disassemble("hello world")
 return 0
def target(driver, *args):
 driver.exe_name = "vm1"
 return entry_point, None

Running this isn't going to be terribly interesting, but it is always nice to know that it is doing what you expect:

$ ./vm1 
== hello world ==
0000 OP_ADD 
0001 OP_RETURN

Chunks of data

Ref: http://www.craftinginterpreters.com/chunks-of-bytecode.html#constants

So our bytecode is missing a very crucial element – the values to operate on!

As with the bytecode we can store these constant values as part of the chunk directly in a list. Each chunk will therefore have a constant data component, and a code component.

Edit the chunk.py file and add the new instance attribute constants as an empty list, and a new method add_constant.

 def add_constant(self, value):
 self.constants.append(value)
 return len(self.constants) - 1

Now to use this new capability we can modify our example chunk to write in some constants before the OP_ADD:

 bytecode = Chunk()
 constant = bytecode.add_constant(1.0)
 bytecode.write_chunk(OpCode.OP_CONSTANT)
 bytecode.write_chunk(constant)
 constant = bytecode.add_constant(2.0)
 bytecode.write_chunk(OpCode.OP_CONSTANT)
 bytecode.write_chunk(constant)
 bytecode.write_chunk(OpCode.OP_ADD)
 bytecode.write_chunk(OpCode.OP_RETURN)
 bytecode.disassemble("adding constants")

Which still translates with RPython and when run gives us the following disassembled bytecode:

== adding constants ==
0000 OP_CONSTANT (00) '1'
0002 OP_CONSTANT (01) '2'
0004 OP_ADD 
0005 OP_RETURN

We won't go down the route of serializing the bytecode to disk, but this bytecode chunk (including the constant data) could be saved and executed on our VM later – like a Java .class file. Instead we will pass the bytecode directly to our VM after we've created it during the compilation process.

Emulation

So those four instructions of bytecode combined with the constant value mapping 00 -> 1.0 and 01 -> 2.0 describes individual steps for our virtual machine to execute. One major point in favor of defining our own bytecode is we can design it to be really simple to execute – this makes the VM really easy to implement.

As I mentioned earlier this virtual machine will have a stack, so let's begin with that. Now the stack is going to be a busy little beast – as our VM takes instructions like OP_ADD it will pop off the top two values from the stack, and push the result of adding them together back onto the stack. Although dynamically resizing Python lists are marvelous, they can be a little slow. RPython can take advantage of a constant sized list which doesn't make our code much more complicated.

To do this we will define a constant sized list and track the stack_top directly. Note how we can give the RPython translator hints by adding assertions about the state that the stack_top will be in.

class VM(object):
 STACK_MAX_SIZE = 256
 stack = None
 stack_top = 0
 def __init__(self):
 self._reset_stack()
 def _reset_stack(self):
 self.stack = [0] * self.STACK_MAX_SIZE
 self.stack_top = 0
 def _stack_push(self, value):
 assert self.stack_top < self.STACK_MAX_SIZE
 self.stack[self.stack_top] = value
 self.stack_top += 1
 def _stack_pop(self):
 assert self.stack_top >= 0
 self.stack_top -= 1
 return self.stack[self.stack_top]
 def _print_stack(self):
 print " ",
 if self.stack_top <= 0:
 print "[]",
 else:
 for i in range(self.stack_top):
 print "[ %s ]" % self.stack[i],
 print

Now we get to the main event, the hot loop, the VM engine. Hope I haven't built it up to much, it is actually really simple! We loop until the instructions tell us to stop (OP_RETURN), and dispatch to other simple methods based on the instruction.

 def _run(self):
 while True:
 instruction = self._read_byte()
 if instruction == OpCode.OP_RETURN:
 print "%s" % self._stack_pop()
 return InterpretResultCode.INTERPRET_OK
 elif instruction == OpCode.OP_CONSTANT:
 constant = self._read_constant()
 self._stack_push(constant)
 elif instruction == OpCode.OP_ADD:
 self._binary_op(self._stack_add)

Now the _read_byte method will have to keep track of which instruction we are up to. So add an instruction pointer (ip) to the VM with an initial value of 0. Then _read_byte is simply getting the next bytecode (int) from the chunk's code:

 def _read_byte(self):
 instruction = self.chunk.code[self.ip]
 self.ip += 1
 return instruction

If the instruction is OP_CONSTANT we take the constant's address from the next byte of the chunk's code, retrieve that constant value and add it to the VM's stack.

 def _read_constant(self):
 constant_index = self._read_byte()
 return self.chunk.constants[constant_index]

Finally our first arithmetic operation OP_ADD, what it has to achieve doesn't require much explanation: pop two values from the stack, add them together, push the result. But since a few operations all have the same template we introduce a layer of indirection – or abstraction – by introducing a reusable _binary_op helper method.

 @specialize.arg(1)
 def _binary_op(self, operator):
 op2 = self._stack_pop()
 op1 = self._stack_pop()
 result = operator(op1, op2)
 self._stack_push(result)
 @staticmethod
 def _stack_add(op1, op2):
 return op1 + op2

Note we tell RPython to specialize _binary_op on the first argument. This causes RPython to make a copy of _binary_op for every value of the first argument passed, which means that each copy contains a call to a particular operator, which can then be inlined.

To be able to run our bytecode the only thing left to do is to pass in the chunk and call _run():

 def interpret_chunk(self, chunk):
 if self.debug_trace:
 print "== VM TRACE =="
 self.chunk = chunk
 self.ip = 0
 try:
 result = self._run()
 return result
 except:
 return InterpretResultCode.INTERPRET_RUNTIME_ERROR

targetvm3.py connects the pieces:

def entry_point(argv):
 bytecode = Chunk()
 constant = bytecode.add_constant(1)
 bytecode.write_chunk(OpCode.OP_CONSTANT)
 bytecode.write_chunk(constant)
 constant = bytecode.add_constant(2)
 bytecode.write_chunk(OpCode.OP_CONSTANT)
 bytecode.write_chunk(constant)
 bytecode.write_chunk(OpCode.OP_ADD)
 bytecode.write_chunk(OpCode.OP_RETURN)
 vm = VM()
 vm.interpret_chunk(bytecode)
 return 0

I've added some trace debugging so we can see what the VM and stack is doing.

The whole thing translates with RPython, and when run gives us:

./vm3
== VM TRACE ==
 []
0000 OP_CONSTANT (00) '1'
 [ 1 ]
0002 OP_CONSTANT (01) '2'
 [ 1 ] [ 2 ]
0004 OP_ADD 
 [ 3 ]
0005 OP_RETURN 
3

Yes we just computed the result of 1+2. Pat yourself on the back.

At this point it is probably valid to check that the translated executable is actually faster than running our program directly in Python. For this trivial example under Python2/pypy this targetvm3.py file runs in the 20ms – 90ms region, and the compiled vm3 runs in <5ms. Something useful must be happening during the translation.

I won't go through the code adding support for our other instructions as they are very similar and straightforward. Our VM is ready to execute our chunks of bytecode, but we haven't yet worked out how to take the entered expression and turn that into this simple bytecode. This is broken into two steps, scanning and compiling.

Scanning the source

All the source for this section can be found in section-3-scanning.

The job of the scanner is to take the raw expression string and transform it into a sequence of tokens. This scanning step will strip out whitespace and comments, catch errors with invalid token and tokenize the string. For example the input "( 1 + 2 ) would get tokenized into LEFT_PAREN, NUMBER(1), PLUS, NUMBER(2), RIGHT_PAREN.

As with our OpCodes we will just define a simple Python class to define an int for each type of token:

class TokenTypes:
 ERROR = 0
 EOF = 1
 LEFT_PAREN = 2
 RIGHT_PAREN = 3
 MINUS = 4
 PLUS = 5
 SLASH = 6
 STAR = 7
 NUMBER = 8

A token has to keep some other information as well – keeping track of the location and length of the token will be helpful for error reporting. The NUMBER token clearly needs some data about the value it is representing: we could include a copy of the source lexeme (e.g. the string 2.0), or parse the value and store that, or – what we will do in this blog – use the location and length information as pointers into the original source string. Every token type (except perhaps ERROR) will use this simple data structure:

class Token(object):
 def __init__(self, start, length, token_type):
 self.start = start
 self.length = length
 self.type = token_type

Our soon to be created scanner will create these Token objects which refer back to addresses in some source. If the scanner sees the source "( 1 + 2.0 )" it would emit the following tokens:

Token(0, 1, TokenTypes.LEFT_PAREN)
Token(2, 1, TokenTypes.NUMBER)
Token(4, 1, TokenTypes.PLUS)
Token(6, 3, TokenTypes.NUMBER)
Token(10, 1, TokenTypes.RIGHT_PAREN)

Scanner

Let's walk through the scanner implementation method by method. The scanner will take the source and pass through it once, creating tokens as it goes.

class Scanner(object):
 def __init__(self, source):
 self.source = source
 self.start = 0
 self.current = 0

The start and current variables are character indices in the source string that point to the current substring being considered as a token.

For example in the string "(51.05+2)" while we are tokenizing the number 51.05 we will have start pointing at the 5, and advance current character by character until the character is no longer part of a number. Midway through scanning the number the start and current values might point to 1 and 4 respectively:

0	1	2	3	4	5	6	7	8
"("	"5"	"1"	"."	"0"	"5"	"+"	"2"	")"
	^			^

From current=4 the scanner peeks ahead and sees that the next character (5) is a digit, so will continue to advance.

0	1	2	3	4	5	6	7	8
"("	"5"	"1"	"."	"0"	"5"	"+"	"2"	")"
	^				^

When the scanner peeks ahead and sees the "+" it will create the number token and emit it. The method that carry's out this tokenizing is _number:

 def _number(self):
 while self._peek().isdigit():
 self.advance()
 # Look for decimal point
 if self._peek() == '.' and self._peek_next().isdigit():
 self.advance()
 while self._peek().isdigit():
 self.advance()
 return self._make_token(TokenTypes.NUMBER)

It relies on a few helpers to look ahead at the upcoming characters:

 def _peek(self):
 if self._is_at_end():
 return '0円'
 return self.source[self.current]
 def _peek_next(self):
 if self._is_at_end():
 return '0円'
 return self.source[self.current+1]
 def _is_at_end(self):
 return len(self.source) == self.current

If the character at current is still part of the number we want to call advance to move on by one character.

 def advance(self):
 self.current += 1
 return self.source[self.current - 1]

Once the isdigit() check fails in _number() we call _make_token() to emit the token with the NUMBER type.

 def _make_token(self, token_type):
 return Token(
 start=self.start,
 length=(self.current - self.start),
 token_type=token_type
 )

Note again that the token is linked to an index address in the source, rather than including the string value.

Our scanner is pull based, a token will be requested via scan_token. First we skip past whitespace and depending on the characters emit the correct token:

 def scan_token(self):
 # skip any whitespace
 while True:
 char = self._peek()
 if char in ' \r\t\n':
 self.advance()
 break
 self.start = self.current
 if self._is_at_end():
 return self._make_token(TokenTypes.EOF)
 char = self.advance()
 if char.isdigit():
 return self._number()
 if char == '(':
 return self._make_token(TokenTypes.LEFT_PAREN)
 if char == ')':
 return self._make_token(TokenTypes.RIGHT_PAREN)
 if char == '-':
 return self._make_token(TokenTypes.MINUS)
 if char == '+':
 return self._make_token(TokenTypes.PLUS)
 if char == '/':
 return self._make_token(TokenTypes.SLASH)
 if char == '*':
 return self._make_token(TokenTypes.STAR)
 return ErrorToken("Unexpected character", self.current)

If this was a real programming language we were scanning, this would be the point where we add support for different types of literals and any language identifiers/reserved words.

At some point we will need to parse the literal value for our numbers, but we leave that job for some later component, for now we'll just add a get_token_string helper. To make sure that RPython is happy to index arbitrary slices of source we add range assertions:

 def get_token_string(self, token):
 if isinstance(token, ErrorToken):
 return token.message
 else:
 end_loc = token.start + token.length
 assert end_loc < len(self.source)
 assert end_loc > 0
 return self.source[token.start:end_loc]

A simple entry point can be used to test our scanner with a hard coded source string:

targetscanner1.py

from scanner import Scanner, TokenTypes, TokenTypeToName
def entry_point(argv):
 source = "( 1 + 2.0 )"
 scanner = Scanner(source)
 t = scanner.scan_token()
 while t.type != TokenTypes.EOF and t.type != TokenTypes.ERROR:
 print TokenTypeToName[t.type],
 if t.type == TokenTypes.NUMBER:
 print "(%s)" % scanner.get_token_string(t),
 print
 t = scanner.scan_token()
 return 0

RPython didn't complain, and lo it works:

$ ./scanner1 
LEFT_PAREN
NUMBER (1)
PLUS
NUMBER (2.0)
RIGHT_PAREN

Let's connect our REPL to the scanner.

targetscanner2.py

from rpython.rlib import rfile
from scanner import Scanner, TokenTypes, TokenTypeToName
LINE_BUFFER_LENGTH = 1024
def repl(stdin, stdout):
 while True:
 stdout.write("> ")
 source = stdin.readline(LINE_BUFFER_LENGTH)
 scanner = Scanner(source)
 t = scanner.scan_token()
 while t.type != TokenTypes.EOF and t.type != TokenTypes.ERROR:
 print TokenTypeToName[t.type],
 if t.type == TokenTypes.NUMBER:
 print "(%s)" % scanner.get_token_string(t),
 print
 t = scanner.scan_token()
def entry_point(argv):
 stdin, stdout, stderr = rfile.create_stdio()
 try:
 repl(stdin, stdout)
 except:
 pass
 return 0

With our REPL hooked up we can now scan tokens from arbitrary input:

$ ./scanner2
> (3 *4) - -3
LEFT_PAREN
NUMBER (3)
STAR
NUMBER (4)
RIGHT_PAREN
MINUS
MINUS
NUMBER (3)
> ^C

Compiling expressions

References

https://www.craftinginterpreters.com/compiling-expressions.html
http://effbot.org/zone/simple-top-down-parsing.htm

The final piece is to turn this sequence of tokens into our low level bytecode instructions for the virtual machine to execute. Buckle up, we are about to write us a compiler.

Our compiler will take a single pass over the tokens using Vaughan Pratt’s parsing technique, and output a chunk of bytecode – if we do it right it will be compatible with our existing virtual machine.

Remember the bytecode we defined above is really simple – by relying on our stack we can transform a nested expression into a sequence of our bytecode operations.

To make this more concrete let's go through by hand translating an expression into bytecode.

Our source expression:

(3 + 2) - (7 * 2)

If we were to make an abstract syntax tree we'd get something like this:

Now if we start at the first sub expression (3+2) we can clearly note from the first open bracket that we must see a close bracket, and that the expression inside that bracket must be valid on its own. Not only that but regardless of the inside we know that the whole expression still has to be valid. Let's focus on this first bracketed expression, let our attention recurse into it so to speak.

This gives us a much easier problem – we just want to get our virtual machine to compute 3 + 2. In this bytecode dialect we would load the two constants, and then add them with OP_ADD like so:

OP_CONSTANT (00) '3.000000'
OP_CONSTANT (01) '2.000000'
OP_ADD

The effect of our vm executing these three instructions is that sitting pretty at the top of the stack is the result of the addition. Winning.

Jumping back out from our bracketed expression, our next token is MINUS, at this point we have a fair idea that it must be used in an infix position. In fact whatever token followed the bracketed expression it must be a valid infix operator, if not the expression is over or had a syntax error.

Assuming the best from our user (naive), we handle MINUS the same way we handled the first PLUS. We've already got the first operand on the stack, now we compile the right operand and then write out the bytecode for OP_SUBTRACT.

The right operand is another simple three instructions:

OP_CONSTANT (02) '7.000000'
OP_CONSTANT (03) '2.000000'
OP_MULTIPLY

Then we finish our top level binary expression and write a OP_RETURN to return the value at the top of the stack as the execution's result. Our final hand compiled program is:

OP_CONSTANT (00) '3.000000'
OP_CONSTANT (01) '2.000000'
OP_ADD
OP_CONSTANT (02) '7.000000'
OP_CONSTANT (03) '2.000000'
OP_MULTIPLY
OP_SUBTRACT
OP_RETURN

Ok that wasn't so hard was it? Let's try make our code do that.

We define a parser object which will keep track of where we are, and whether things have all gone horribly wrong:

class Parser(object):
 def __init__(self):
 self.had_error = False
 self.panic_mode = False
 self.current = None
 self.previous = None

The compiler will also be a class, we'll need one of our Scanner instances to pull tokens from, and since the output is a bytecode Chunk let's go ahead and make one of those in our compiler initializer:

class Compiler(object):
 def __init__(self, source):
 self.parser = Parser()
 self.scanner = Scanner(source)
 self.chunk = Chunk()

Since we have this (empty) chunk of bytecode we will make a helper method to add individual bytes. Every instruction will pass from our compiler into an executable program through this simple .

 def emit_byte(self, byte):
 self.current_chunk().write_chunk(byte)

To quote from Bob Nystrom on the Pratt parsing technique:

the implementation is a deceptively-simple handful of deeply intertwined code

I don't actually think I can do justice to this section. Instead I suggest reading his treatment in Pratt Parsers: Expression Parsing Made Easy which explains the magic behind the parsing component. Our only major difference is instead of creating an AST we are going to directly emit bytecode for our VM.

Now that I've absolved myself from taking responsibility in explaining this somewhat tricky concept, I'll discuss some of the code from compiler.py, and walk through what happens for a particular rule.

I'll jump straight to the juicy bit the table of parse rules. We define a ParseRule for each token, and each rule comprises:

an optional handler for when the token is as a prefix (e.g. the minus in (-2)),
an optional handler for whet the token is used infix (e.g. the slash in 2/47)
a precedence value (a number that determines what is of higher precedence)

rules = [
 ParseRule(None, None, Precedence.NONE), # ERROR
 ParseRule(None, None, Precedence.NONE), # EOF
 ParseRule(Compiler.grouping, None, Precedence.CALL), # LEFT_PAREN
 ParseRule(None, None, Precedence.NONE), # RIGHT_PAREN
 ParseRule(Compiler.unary, Compiler.binary, Precedence.TERM), # MINUS
 ParseRule(None, Compiler.binary, Precedence.TERM), # PLUS
 ParseRule(None, Compiler.binary, Precedence.FACTOR), # SLASH
 ParseRule(None, Compiler.binary, Precedence.FACTOR), # STAR
 ParseRule(Compiler.number, None, Precedence.NONE), # NUMBER
]

These rules really are the magic of our compiler. When we get to a particular token such as MINUS we see if it is an infix operator and if so we've gone and got its first operand ready. At all times we rely on the relative precedence; consuming everything with higher precedence than the operator we are currently evaluating.

In the expression:

2 + 3 * 4

The * has higher precedence than the +, so 3 * 4 will be parsed together as the second operand to the first infix operator (the +) which follows the BEDMAS order of operations I was taught at high school.

To encode these precedence values we make another Python object moonlighting as an enum:

class Precedence(object):
 NONE = 0
 DEFAULT = 1
 TERM = 2 # + -
 FACTOR = 3 # * /
 UNARY = 4 # ! - +
 CALL = 5 # ()
 PRIMARY = 6

What happens in our compiler when turning -2.0 into bytecode? Assume we've just pulled the token MINUS from the scanner. Every expression has to start with some type of prefix – whether that is:

a bracket group (,
a number 2,
or a prefix unary operator -.

Knowing that, our compiler assumes there is a prefix handler in the rule table – in this case it points us at the unary handler.

 def parse_precedence(self, precedence):
 # parses any expression of a given precedence level or higher
 self.advance()
 prefix_rule = self._get_rule(self.parser.previous.type).prefix
 prefix_rule(self)

unary is called:

 def unary(self):
 op_type = self.parser.previous.type
 # Compile the operand
 self.parse_precedence(Precedence.UNARY)
 # Emit the operator instruction
 if op_type == TokenTypes.MINUS:
 self.emit_byte(OpCode.OP_NEGATE)

Here – before writing the OP_NEGATE opcode we recurse back into parse_precedence to ensure that whatever follows the MINUS token is compiled – provided it has higher precedence than unary – e.g. a bracketed group. Crucially at run time this recursive call will ensure that the result is left on top of our stack. Armed with this knowledge, the unary method just has to emit a single byte with the OP_NEGATE opcode.

Test compilation

Now we can test our compiler by outputting disassembled bytecode of our user entered expressions. Create a new entry_point targetcompiler:

from rpython.rlib import rfile
from compiler import Compiler
LINE_BUFFER_LENGTH = 1024
def entry_point(argv):
 stdin, stdout, stderr = rfile.create_stdio()
 try:
 while True:
 stdout.write("> ")
 source = stdin.readline(LINE_BUFFER_LENGTH)
 compiler = Compiler(source, debugging=True)
 compiler.compile()
 except:
 pass
 return 0

Translate it and test it out:

$ ./compiler1 
> (2/4 + 1/2)
== code ==
0000 OP_CONSTANT (00) '2.000000'
0002 OP_CONSTANT (01) '4.000000'
0004 OP_DIVIDE 
0005 OP_CONSTANT (02) '1.000000'
0007 OP_CONSTANT (00) '2.000000'
0009 OP_DIVIDE 
0010 OP_ADD 
0011 OP_RETURN

Now if you've made it this far you'll be eager to finally connect everything together by executing this bytecode with the virtual machine.

End to end

All the pieces slot together rather easily at this point, create a new file targetcalc.py and define our entry point:

from rpython.rlib import rfile
from compiler import Compiler
from vm import VM
LINE_BUFFER_LENGTH = 4096
def entry_point(argv):
 stdin, stdout, stderr = rfile.create_stdio()
 vm = VM()
 try:
 while True:
 stdout.write("> ")
 source = stdin.readline(LINE_BUFFER_LENGTH)
 if source:
 compiler = Compiler(source, debugging=False)
 compiler.compile()
 vm.interpret_chunk(compiler.chunk)
 except:
 pass
 return 0
def target(driver, *args):
 driver.exe_name = "calc"
 return entry_point, None

Let's try catch it out with a double negative:

$ ./calc 
> 2--3
== VM TRACE ==
 []
0000 OP_CONSTANT (00) '2.000000'
 [ 2.000000 ]
0002 OP_CONSTANT (01) '3.000000'
 [ 2.000000 ] [ 3.000000 ]
0004 OP_NEGATE 
 [ 2.000000 ] [ -3.000000 ]
0005 OP_SUBTRACT 
 [ 5.000000 ]
0006 OP_RETURN 
5.000000

Ok well let's evaluate the first 50 terms of the Nilakantha Series:

$ ./calc
> 3 + 4 * ((1/(2 * 3 * 4)) + (1/(4 * 5 * 6)) - (1/(6 * 7 * 8)) + (1/(8 * 9 * 10)) - (1/(10 * 11 * 12)) + (1/(12 * 13 * 14)) - (1/(14 * 15 * 16)) + (1/(16 * 17 * 18)) - (1/(18 * 19 * 20)) + (1/(20 * 21 * 22)) - (1/(22 * 23 * 24)) + (1/(24 * 25 * 26)) - (1/(26 * 27 * 28)) + (1/(28 * 29 * 30)) - (1/(30 * 31 * 32)) + (1/(32 * 33 * 34)) - (1/(34 * 35 * 36)) + (1/(36 * 37 * 38)) - (1/(38 * 39 * 40)) + (1/(40 * 41 * 42)) - (1/(42 * 43 * 44)) + (1/(44 * 45 * 46)) - (1/(46 * 47 * 48)) + (1/(48 * 49 * 50)) - (1/(50 * 51 * 52)) + (1/(52 * 53 * 54)) - (1/(54 * 55 * 56)) + (1/(56 * 57 * 58)) - (1/(58 * 59 * 60)) + (1/(60 * 61 * 62)) - (1/(62 * 63 * 64)) + (1/(64 * 65 * 66)) - (1/(66 * 67 * 68)) + (1/(68 * 69 * 70)) - (1/(70 * 71 * 72)) + (1/(72 * 73 * 74)) - (1/(74 * 75 * 76)) + (1/(76 * 77 * 78)) - (1/(78 * 79 * 80)) + (1/(80 * 81 * 82)) - (1/(82 * 83 * 84)) + (1/(84 * 85 * 86)) - (1/(86 * 87 * 88)) + (1/(88 * 89 * 90)) - (1/(90 * 91 * 92)) + (1/(92 * 93 * 94)) - (1/(94 * 95 * 96)) + (1/(96 * 97 * 98)) - (1/(98 * 99 * 100)) + (1/(100 * 101 * 102)))
== VM TRACE ==
 []
0000 OP_CONSTANT (00) '3.000000'
 [ 3.000000 ]
0002 OP_CONSTANT (01) '4.000000'
...SNIP...
0598 OP_CONSTANT (101) '102.000000'
 [ 3.000000 ] [ 4.000000 ] [ 0.047935 ] [ 1.000000 ] [ 10100.000000 ] [ 102.000000 ]
0600 OP_MULTIPLY 
 [ 3.000000 ] [ 4.000000 ] [ 0.047935 ] [ 1.000000 ] [ 1030200.000000 ]
0601 OP_DIVIDE 
 [ 3.000000 ] [ 4.000000 ] [ 0.047935 ] [ 0.000001 ]
0602 OP_ADD 
 [ 3.000000 ] [ 4.000000 ] [ 0.047936 ]
0603 OP_MULTIPLY 
 [ 3.000000 ] [ 0.191743 ]
0604 OP_ADD 
 [ 3.191743 ]
0605 OP_RETURN 
3.191743

We just executed 605 virtual machine instructions to compute pi to 1dp!

This brings us to the end of this tutorial. To recap we've walked through the whole compilation process: from the user providing an expression string on the REPL, scanning the source string into tokens, parsing the tokens while accounting for relative precedence via a Pratt parser, generating bytecode, and finally executing the bytecode on our own VM. RPython translated what we wrote into C and compiled it, meaning our resulting calc REPL is really fast.

"The world is a thing of utter inordinate complexity and richness and strangeness that is absolutely awesome."

― Douglas Adams

Many thanks to Bob Nystrom for writing the book that inspired this post, and thanks to Carl Friedrich and Matt Halverson for reviewing.

― Brian (@thorneynzb)