Using CPython extension modules with PyPy natively, or: PyPy can load .pyd files with CPyExt!

PyPy is now able to load and run CPython extension modules (i.e. .pyd and .so files) natively by using the new CPyExt subsystem. Unlike the solution presented in another blog post (where extension modules like numpy etc. were run on CPython and proxied through TCP), this solution does not require a running CPython anymore. We do not achieve full binary compatiblity yet (like Ironclad), but recompiling the extension is generally enough.

The only prerequisite is that the necessary functions of the C API of CPython are already implemented in PyPy. If you are a user or an author of a module and miss certain functions in PyPy, we invite you to implement them. Up until now, a lot of people (including a lot of new committers) have stepped up and implemented a few functions to get their favorite module running. See the end of this post for a list of names.

Regarding speed, we tried the following: even though there is a bit of overhead when running these modules, we could run the regular expression engine of CPython (_sre.so) and execute the spambayes benchmark of the Unladen Swallow benchmark suite (cf. speed.pypy.org) and experience a speedup: It became two times faster on pypy-c than with the built-in regular expression engine of PyPy. From Amdahl's Law it follows that the _sre.so must run several times faster than the built-in engine.

Currently pursued modules include PIL and others. Distutils support is nearly ready. If you would like to participate or want information on how to use this new feature, come and join our IRC channel #pypy on freenode.

Amaury Forgeot d'Arc and Alexander Schremmer

Further CPyExt Contributors:

• Alex Gaynor
• Benjamin Peterson
• Jean-Paul Calderone
• Maciej Fijalkowski
• Jan de Mooij
• Lucian Branescu Mihaila
• Andreas Stührk
• Zooko Wilcox-O Hearn

PyPy gets a new compiler

Today, I merged the parser-compiler branch, which I have been working on over the summer. It contained a total rewrite of both PyPy's Python parser and AST compiler. PyPy's old parser was (in)famous internally for being complicated and slow (with many algorithmic complexities greater than O(n)). The new parser is a simple as I could make it LL(1) parser like CPython (though it doesn't share the hacks of CPython's parser).

The new compiler is based on the Abstract Syntax Trees (AST) that CPython 2.5 introduced instead of PyPy's old AST based on the compiler package's. This means that Python code running on PyPy will be able to use the same _ast interface as CPython. PyPy's _ast implementation supports AST features that CPython 2.6 added, including compiling modified AST to bytecode and executing it. In this rewrite, some more obscure compiler features were added, too. For example, jumps in bytecode can now be greater than 65535 bytes! (That's like an if statement with 7000 lines of code in the body.)

While the PyPy translation toolchain still has many obscure details and hacks, this merge completes the process of making the actual Python interpreter very clean. Hopefully, this will make adding new features much easier and make PyPy less frustrating to maintain as well as providing application level code with an improved AST interface!

Roadmap for JIT

Hello.

First a disclaimer. This post is more about plans for future than current status. We usually try to write about things that we have done, because it's much much easier to promise things than to actually make it happen, but I think it's important enough to have some sort of roadmap.

In recent months we came to the point where the 5th generation of JIT prototype was working as nice or even a bit nicer than 1st one back in 2007. Someone might ask "so why did you spend all this time without going forward?". And indeed, we spend a lot of time moving sideways, but as posted, we also spent a lot of time doing some other things, which are important as well. The main advantage of current JIT incarnation is much much simpler than the first one. Even I can comprehend it, which is much of an improvement :-)

So, the prototype is working and gives very nice speedups in range of 20-30x over CPython. We're pretty confident this prototype will work and will produce fast python interpreter eventually. So we decided that now we'll work towards changing prototype into something stable and solid. This might sound easy, but in fact it's not. Having stable assembler backend and optimizations that keep semantics is not as easy as it might sound.

The current roadmap, as I see it, looks like as following:

• Provide a JIT that does not speedup things, but produce assembler without optimizations turned on, that is correct and able to run CPython's library tests on a nightly basis.
• Introduce simple optimizations, that should make above JIT a bit faster than CPython. With optimizations disabled JIT is producing incredibly dumb assembler, which is slower than correspoding C code, even with removal of interpretation overhead (which is not very surprising).
• Backport optimizations from JIT prototype, one by one, keeping an eye on how they perform and making sure they don't break anything.
• Create new optimizations, like speeding up attribute access.
• Profit.

This way, we can hopefully provide a working JIT, which gives fast python interpreter, which is a bit harder than just a nice prototype.

Tell us what you think about this plan.

Cheers,
fijal & others.