Mailman 3 July 2012 - Python-Dev

Print policy for deprecated modules
by anatoly techtonik July 22, 2012

July 22, 2012

What is a print policy for deprecated modules? "new" module is deprecated in 2.6, but 2.7.3 doesn't print any warnings. Is it a bug? python -Wd -c "import new" -- anatoly t.

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2to3 porting HOWTO: setup.py question
by anatoly techtonik July 22, 2012

July 22, 2012

http://docs.python.org/py3k/howto/pyporting.html#during-installation What's the point in making implicit Python 3 check here: try: # Python 3 from distutils.command.build_py import build_py_2to3 as build_py except ImportError: # Python 2 from distutils.command.build_py import build_py instead of explicit check like: import sys if sys.version_info[0] >= 3: from distutils.command.build_py import build_py_2to3 as build_py ? -- anatoly t.

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test_hashlib
by Kristján Valur Jónsson July 21, 2012

July 21, 2012

I was hit by this today. in test_hashlib.py there is this: def test_unknown_hash(self): self.assertRaises(ValueError, hashlib.new, 'spam spam spam spam spam') self.assertRaises(TypeError, hashlib.new, 1) but in hashlib.py, there is this code: except ImportError: pass # no extension module, this hash is unsupported. raise ValueError('unsupported hash type %s' % name) The code will raise ValueError when int(1) is passed in, but the unittests expect a TypeError. So, … [View More]

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four easy documentation patches
by Chris Jerdonek July 21, 2012

July 21, 2012

I was wondering if someone could review the following minor documentation patches: Document that filecmp.dircmp comparisons are "shallow" (7/5/2012): http://bugs.python.org/issue15250 Document dircmp.left and dircmp.right (7/6/2012): http://bugs.python.org/issue15269 Update "PyPI package display" documentation re: rst2html.py (7/5/2012): http://bugs.python.org/issue15231 [Dev Guide] Update documentation around Dev Guide process (7/10/2012): http://bugs.python.org/issue15311 Thanks, --Chris

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Re: [Python-Dev] cpython (3.2): ISsue #14988: restore Python 2's behavior of raising ImportError when unable to
by Georg Brandl July 21, 2012

July 21, 2012

This looks like it will give an "error return without exception set" if the "if (expat_capi)" directly before the change is true, and expat_capi is then set to NULL in the branch. Georg On 07/17/2012 01:25 PM, eli.bendersky wrote: > http://hg.python.org/cpython/rev/d896fd0a8ba7 > changeset: 78152:d896fd0a8ba7 > branch: 3.2 > parent: 78142:71537aba3a0a > user: Eli Bendersky <eliben(a)gmail.com> > date: Tue Jul 17 14:20:38 2012 +0300 > … [View More]

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Re: [Python-Dev] cpython: Optimize tostringlist by taking the stream class outside the function. It's now
by Georg Brandl July 21, 2012

July 21, 2012

Uh, optimizations are not exactly what I want to see during feature freeze. Georg On 07/17/2012 02:10 PM, eli.bendersky wrote: > http://hg.python.org/cpython/rev/51978f89e5ed > changeset: 78156:51978f89e5ed > user: Eli Bendersky <eliben(a)gmail.com> > date: Tue Jul 17 15:09:12 2012 +0300 > summary: > Optimize tostringlist by taking the stream class outside the function. It's now 2x faster on short calls. Related to #1767933 > > files: > … [View More]

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Re: [Python-Dev] cpython: Issue #15168: Move importlb.test to test.test_importlib.
by Antoine Pitrou July 21, 2012

July 21, 2012

On Fri, 20 Jul 2012 20:49:03 +0200 (CEST) brett.cannon <python-checkins(a)python.org> wrote: > diff --git a/Lib/importlib/test/__init__.py b/Lib/test/test_importlib/__init__.py > rename from Lib/importlib/test/__init__.py > rename to Lib/test/test_importlib/__init__.py > --- a/Lib/importlib/test/__init__.py > +++ b/Lib/test/test_importlib/__init__.py > @@ -1,5 +1,6 @@ > import os > import sys > +from .. import support I don't know if I'm only speaking for … [View More]

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A new JIT compiler for a faster CPython?
by Victor Stinner July 21, 2012

July 21, 2012

Hi, I would like to write yet another JIT compiler for CPython. Before writing anything, I would like your opinion because I don't know well other Python compilers. I also want to prepare a possible integration into CPython since the beginning of the project, or at least stay very close to the CPython project (and CPython developers!). I did not understand exactly why Unladen Swallow and psyco projects failed, so please tell me if you think that my project is going to fail too! == Why? == … [View More]CPython is still the reference implementation, new features are first added to this implementation (ex: PyPy is not supporting Python 3 yet, but there is a project to support Python 3). Some projects still rely on low level properties of CPython, especially its C API (ex: numpy; PyPy has a cpyext module to emulate the CPython C API). A JIT is the most promising solution to speed up the main evaluation loop: using a JIT, it is possible to compile a function for a specific type on the fly and so enable deeper optimizations. psyco is no more maintained. It had its own JIT which is complex to maintain. For example, it is hard to port it to a new hardware. LLVM is fast and the next version will be faster. LLVM has a community, a documentation, a lot of tools and is active. There are many Python compilers which are very fast, but most of them only support a subset of Python or require to modify the code (ex: specify the type of all parameters and variables). For example, you cannot run Django with Shredskin. IMO PyPy is complex and hard to maintain. PyPy has a design completly different than CPython and is much faster and has a better memory footprint. I don't expect to be as fast as PyPy, just faster than CPython. == General idea == I don't want to replace CPython. This is an important point. All others Python compilers try to write something completly new, which is an huge task and is a problem to stay compatible with CPython. I would like to reuse as much as possible code of CPython and don't try to fight against the GIL or reference counting, but try to cooperate instead. I would like to use a JIT to generate specialized functions for a combinaison of arguments types. Specialization enables more optimizations. I would like to use LLVM because LLVM is an active project, has many developers and users, is fast and the next version will be faster! LLVM already supports common optimizations like inlining. My idea is to emit the same code than ceval.c from the bytecode to be fully compatible with CPython, and then write a JIT to optimize functions for a specific type. == Roadmap == -- Milestone 1: Proof of concept -- * Use the bytecode produced by CPython parser and compiler * Only compile a single function * Emit the same code than ceval.c using LLVM, but without tracing, exceptions nor signal handling (they will be added later) * Support compiling and calling the following functions: "def func(a, b): return a+b" The pymothoa project can be used as a base to implement quickly such proof of concept. -- Milestone 2: Specialized function for the int type -- * Use type annotation to generate specialized functions for the int type * Use C int with a guard detecting integer overflow to fallback on Python int -- Milestone 3: JIT -- * Depending on the type seen at runtime, recompile the function to generate specialized functions * Use guard to fallback to a generic implementation if the type is not the expected type * Drop maybe the code using function annotations At this step, we can start to benchmark to check if the (JIT) compiler is faster than CPython. -- Later (unsorted ideas) -- * Support exceptions * Full support of Python - classes - list comprehension - etc. * Optimizations: - avoid reference counting when possible - avoid temporary objects when possible - release the GIL when possible - inlining: should be very interesting with list comprehension - unroll loops? - lazy creation of the frame? * Use registers instead of a stack in the "evaluation loop"? * Add code to allow tracing and profiling * Add code to handle signals (pending calls) * Write a compiler using the AST, with a fallback to the bytecode? (would it be faster? easier or more complex to maintain?) * Test LLVM optimizers * Compile a whole module or even a whole program * Reduce memory footprint * Type annotation to help the optimizer? (with guards?) * "const" annotation to help the optimizer? (with guards?) * Support any build option of Python: - support Python 2 (2.5, 2.6, 2.7) and 3 (3.1, 3.2, 3.3, 3.4) - support narrow and wide mode: flag at runtime? - support debug and release mode: flag at runtime? - support 32 and 64 bits mode on Windows? == Other Python VM and compilers == -- Fully Python compliant -- * `PyPy <http://pypy.org/>`_ * `Jython <http://www.jython.org/>`_ based on the JVM * `IronPython <http://ironpython.net/>`_ based on the .NET VM * `Unladen Swallow <http://code.google.com/p/unladen-swallow/>`_ fork of CPython 2.6 using LLVM - `Unladen Swallow Retrospective <http://qinsb.blogspot.com.au/2011/03/unladen-swallow-retrospective.html>`_ - `PEP 3146 <http://python.org/dev/peps/pep-3146/>`_ * `psyco <http://psyco.sourceforge.net/>`_ (fully Python compliant?), no more maintained -- Subset of Python to C++ -- * `Nuitka <http://www.nuitka.net/pages/overview.html>`_ * `Python2C <http://strout.net/info/coding/python/ai/python2c.py>`_ * `Shedskin <http://code.google.com/p/shedskin/>`_ * `pythran <https://github.com/serge-sans-paille/pythran>`_ (no class, set, dict, exception, file handling, ...) -- Subset of Python -- * `pymothoa <http://code.google.com/p/pymothoa/>`_: use LLVM; don't support classes nor exceptions. * `unpython <http://code.google.com/p/unpython/>`_: Python to C * `Perthon <http://perthon.sourceforge.net/>`_: Python to Perl * `Copperhead <http://copperhead.github.com/>`_: Python to GPU (Nvidia) -- Language very close to Python -- * `Cython <http://www.cython.org/>`_: "Cython is a programming language based on Python, with extra syntax allowing for optional static type declarations." Based on `Pyrex <http://www.cosc.canterbury.ac.nz/greg.ewing/python/Pyrex/>`_ == See also == * `Volunteer developed free-threaded cross platform virtual machines? <http://www.boredomandlaziness.org/2012/07/volunteer-supported-free-threaded…>`_ Victor Stinner [View Less]

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Summary of Python tracker Issues
by Python tracker July 20, 2012

July 20, 2012

ACTIVITY SUMMARY (2012-07-13 - 2012-07-20) Python tracker at http://bugs.python.org/ To view or respond to any of the issues listed below, click on the issue. Do NOT respond to this message. Issues counts and deltas: open 3528 ( +8) closed 23658 (+55) total 27186 (+63) Open issues with patches: 1501 Issues opened (39) ================== #9374: urlparse should parse query and fragment for arbitrary schemes http://bugs.python.org/issue9374 reopened by eric.araujo #9803: IDLE … [View More]

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Runtime forks through monkeypatching (was Re: Use function names instead of functions for os.supports_dir_fd?)
by Nick Coghlan July 19, 2012

July 19, 2012

On Fri, Jul 20, 2012 at 4:48 AM, Antoine Pitrou <solipsis(a)pitrou.net> wrote: > On Wed, 18 Jul 2012 02:26:14 +0200 > Victor Stinner <victor.stinner(a)gmail.com> wrote: > >> >> Monkey patching is a common practice in Python. test_os.py replaces >> >> os.exec*() functions temporary for example. >> > >> > Perhaps for testing, but I don't think monkey-patching is common in >> > production code. Perhaps you are thinking of Ruby … [View More]:) >> >> The gevent library does monkey-patch os.fork (and time.sleep and many >> other functions), but gevent is maybe not ready for production? :-) > > Extensive monkey-patching of core OS functions would certainly make me > weary of using such a third-party library, even if it claims to be > "serious". gevent is one of the well-behaved players in that game - it doesn't monkeypatch anything unless you explicitly tell it to [1]. You do need to have some idea what you're doing if using the monkey patching aspect (as not all third party modules will behave well in that case - just as not all third party modules behave well on other Python implementations), but you can also just use gevent as a normal library without monkey patching anything (however, adhering strictly to that rule leaves you in the same situation as Twisted, where modules that use the standard blocking APIs are completely useless to you. Thus, if your goal is "pure async" code without any need for monkeypatching, Twisted is a much better bet than gevent, just because it's been playing that game for longer and has more fully async aware components available). That's why I call features like the gevent.monkey module "runtime forks" - when they're implemented well, they effectively give you two Python implementations without the complexity of actually having parallel Python installations (compare the complexity of using Stackless, a traditional fork, vs gevent.monkey, a runtime fork - there's a reason the core greenlet component was lifted out of Stackless and made available as a CPython extension module). Applications can decide at startup which Python variant (standard CPython, or CPython+gevent) they want to use without affecting other Python applications installed on the system and without needing to duplicating all installed Python modules into a second interpreter. It's only when libraries monkeypatch standard interfaces *implicitly* as a side effect of import that you start to get seriously broken "action at a distance" behaviour. Or, as happened with distutils, you get a third party project (setuptools) effectively freezing implementation details of the stdlib version - when monkeypatching starts delving too deep into implementation details, it's time to switch to a traditional fork and start figuring out better ways to solve the problem (which process is now grinding away on the distutils2/packaging front). Monkeypatching is a powerful tool - used well, it lets you achieve impressive things that would otherwise be substantially more difficult to either create in the first place or maintain in the long run. As with many powerful tools though, used badly it can lead to a lot of pain. Static languages like Java decide the risk of action at a distance from monkeypatching isn't worth the power it gives you to work around issues in third party libraries without resorting to creating a traditional fork, so they strictly enforce access controls and have completely closed class definitions. Python permits monkeypatching at a language level (because when you need it, you really need it), but discourages it a social level (because there is usually a less magical technique, such as creating and using a wrapper or subclass, that will achieve the desired effect). Cheers, Nick. [1] http://www.gevent.org/intro.html#monkey-patching P.S. for a non-monkeypatching approach to reusing a synchronous library when creating an asynchronous equivalent, take a look at 10gen's attempt to create a *maintainable* Tornado-compatible async wrapper for PyMongo by using greenlets ([2][3]). It's a clever idea, but still a lot more work than just using PyMongo itself with gevent if you're only interested in the "async" part rather than the "Tornado-compatible" part. [2] http://emptysquare.net/blog/motor-internals-how-i-asynchronized-a-synchrono… [3] http://emptysquare.net/blog/motor-four-strategies-for-maintainability/ -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia [View Less]

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