Let's Write an LLVM Specializer for Python

http://dev.stephendiehl.com/numpile/

by tenslisi • 12 years ago

287 43 12 years ago

dev.stephendiehl.com

walkamages• 12 years ago

An excellent article! I had wanted to get back into some python recently after seeing the changes in 3.4, I had also wanted to become more familiar with LLVM, and this does both.

gtirloni• 12 years ago

there is this discussion (flame war?) about python3 not bringing too many benefits. i haven't made my mind yet. could you elaborate what you saw in 3.4 that was nice?

gamesbrainiac• 12 years ago

I'd like to know too. Are there any LLVM specific enhancements that python 3.4 brings to the table?

exDM698• 12 years ago

Simply put: it's a better language. The whole "discussion" is whether it makes sense to migrate since many parts of the ecosystem (many important libraries and frameworks) have not made the transition. And many distros ship Python 2 by default, Python 3 is optional. Python 3 only is not feasible.

To me, the killer feature is better lazy evaluation (generators). In particular, important builtins like map, filter, zip, enumerate, etc are generators, instead of returning lists. This makes it feasible to write things like

    (process(line) for line in map(str.upper, open('giantfile.txt')) if line.lstrip()[0] != '#')

Some of the above can also be done with itertools package in Python 2, but not everything.

Python 3.4 changelog is here, it contains e.g. asynchronous io facilities (asyncio module): https://www.python.org/downloads/release/python-342/

edit: added enumerate() in the example above, for line in open(filename) returns a generator in Python 2.x too.

edit2: enumerate is lazy in python2, I replaced it with map(str.upper)

mkesper• 12 years ago

Saner handling of Unicode, for example.

baq• 12 years ago

much better exceptions and sane unicode are the biggest improvements.

ak217• 12 years ago

Without listing any of the modules or improvements that are in the standard library in 3 but backported as PyPI modules to 2 (of which there are many), here are the features that I actually use in Python 3: unicode handling that isn't insane, function annotations, async improvements, exception chaining, enums, single-dispatch generics, better SSL support, generator delegation, better int-bytes conversion support, unittest module improvements.

The key point is that 2.7 is a language frozen in time, while 3.4+ is continuing to develop and improve. And most of the hand-wringing was before the critical mass of third-party modules was ported to 3.x.

https://docs.python.org/3/whatsnew/3.4.html

https://docs.python.org/3/whatsnew/3.3.html

https://docs.python.org/3/whatsnew/3.2.html

https://docs.python.org/3/whatsnew/3.1.html

https://docs.python.org/3/whatsnew/3.0.html

ngoldbaum• 12 years ago

My favorite new feature is PEP-442 [0]. Basically, it's now safe to add a __del__ method to a class without worrying about memory leaks caused by reference cycles.

[0] https://www.python.org/dev/peps/pep-0442/

bkeroack• 12 years ago

Add to the list:

- function annotations (allows runtime type checking via third party modules)

- asyncio (not as easy to use as Go's goroutines but still vastly superior to the multiprocessing module)

andreasvc2• 12 years ago

I can't say I disagree with such sentiments, but over time I've ran into a few issues (features and performance improvements) which where only addressed in Python 3. This is reason enough to try working with Python 3.

illumen• 12 years ago

Very nice article! :)

Storing types via traces could be another step for gathering types. As well as using the more advanced static type checking code that is around for python.

Now I have something to work through on the weekend. Looking forward to part 2!

travisoliphant3• 12 years ago

The numba code-base implements quite a bit of this. We actually moved away from the AST approach and went back to the byte-code approach because the AST approach quickly becomes unwieldy as the number of Visitors that you apply grows. Compile times are also slower.

The author is definitely helping people learn about LLVM and how it can be used with Python --- which is great, because this is exactly what Numba is: http://numba.pydata.org. But, please don't start another "Numba". Just come help us improve the current one.

Erwin• 12 years ago

Interesting. I currently use Python's AST to convert some nested logical query expression (in a syntax unique to my application) into bytecode executed by a specialized VM (I originally tried using V8 and LuaJit for this but performance wise that was unsuccessful; the project replaced some old Boost::Python C++ code). This article should make it easy to get started attempting an LLVM replacement.

travisoliphant3• 12 years ago

Yes, LLVM is a great approach for doing code-gen from arbitrary specialized VMs. And the Python interface to it makes it easy to experiment. We no longer use llvmpy for Numba (we use a simpler interface llvmlite) and so llvmpy could use a maintainer.

jonstewart• 12 years ago

I really appreciate the length and detail in this blog post. It's comprehensive, not just showing off.

ch0wn• 12 years ago

Stephen Diehl continues to blow my mind on a regular basis. His latest work in progress "Wrote You a Haskell"[0] is also worth keeping an eye on. I've worked through the first couple of chapters and it's fantastic.

[0] http://dev.stephendiehl.com/fun/

travisoliphant3• 12 years ago

This is a great tutorial about first-generation Numba. The author learned a lot about LLVM and llvmpy while working with several of our devs. If you are interested in the "Further work" in his article, come join the Numba project.

tadlan• 12 years ago

What is second generation numba? And any plans to branch numba out of pure numeric application s?

wedesoft2• 12 years ago

I did something similar in Ruby but using GCC as a "JIT" compiler for image processing (software [1], thesis [2]). I can really recommend JIT compilation for doing array processing.

[1] http://www.wedesoft.de/hornetseye-api/ [2] http://www.wedesoft.de/downloads/thesis_wedekind.pdf

EDIT: In my approach I didn't go through the Ruby AST though. Rather I used the approach of injecting "GCCVariables" which emit C code instead of doing the actual computation.

chrisseaton• 12 years ago

You should submit your thesis to the Ruby Bibliography http://rubybib.org

wedesoft2• 12 years ago

Ok, will do. Cheers :)

cyberneticcook• 12 years ago

Could this be used to ahead-of-time compile Python code ? I'm more interested in getting to a native executable or library.

ericfrederich• 12 years ago

I love Python but when I use a statically typed language like C, Rust, Go, etc I really feel that it is missing from Python.

I'd love to see a new language exactly like Python but compiled and statically typed. Something similar to Cython, but rather than generating a bunch of C code it would target LLVM. Additionally it would be able to generate pure Python code simply by removing any typing syntax.

Let's Write an LLVM Specializer for Python

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