Since the latest draft went four weeks w/o comment or complaint to address
the last round of issues, I am going to consider this PEP accepted (don't
think we need a BDFAP since this is procedural and not a language feature or
stdlib addition, but if people disagree then Guido can assign someone).
On Sun, Jul 17, 2011 at 17:16, Brett Cannon <br...@python.org> wrote:
> While at a mini-PyPy sprint w/ Alex Gaynor of PyPy and Phil Jenvey of
> Jython, I decided to finally put the time in to update this PEP yet again.
>
> The biggest changes is that the 100% branch coverage requirement has been
> replaced with "comprehensive" coverage from the tests. I think we are all
> enough grown-ups to not have to specify anything tighter than this. I also
> added a paragraph in the Details section about using the abstract C APIs
> (e.g., PyObject_GetItem) over type-specific ones (e.g., PyList_GetItem) in
> order to be more supportive of duck typing from the Python code. I figure
> the "be API compatible" assumes this, but mentioning it doesn't hurt (and
> should help make Raymond less angry =).
>
>
> PEP: 399
> Title: Pure Python/C Accelerator Module Compatibility Requirements
> Version: $Revision: 88219 $
> Last-Modified: $Date: 2011-01-27 13:47:00 -0800 (Thu, 27 Jan 2011) $
> Author: Brett Cannon <br...@python.org>
> Status: Draft
> Type: Informational
> Content-Type: text/x-rst
> Created: 04-Apr-2011
> Python-Version: 3.3
> Post-History: 04-Apr-2011, 12-Apr-2011, 17-Jul-2011
>
> Abstract
> ========
>
> The Python standard library under CPython contains various instances
> of modules implemented in both pure Python and C (either entirely or
> partially). This PEP requires that in these instances that the
> C code *must* pass the test suite used for the pure Python code
> so as to act as much as a drop-in replacement as reasonably possible
> (C- and VM-specific tests are exempt). It is also required that new
> C-based modules lacking a pure Python equivalent implementation get
> special permission to be added to the standard library.
>
>
> Rationale
> =========
>
> Python has grown beyond the CPython virtual machine (VM). IronPython_,
> Jython_, and PyPy_ are all currently viable alternatives to the
> CPython VM. The VM ecosystem that has sprung up around the Python
> programming language has led to Python being used in many different
> areas where CPython cannot be used, e.g., Jython allowing Python to be
> used in Java applications.
>
> A problem all of the VMs other than CPython face is handling modules
> from the standard library that are implemented (to some extent) in C.
> Since other VMs do not typically support the entire `C API of CPython`_
> they are unable to use the code used to create the module. Often times
> this leads these other VMs to either re-implement the modules in pure
> Python or in the programming language used to implement the VM itself
> (e.g., in C# for IronPython). This duplication of effort between
> CPython, PyPy, Jython, and IronPython is extremely unfortunate as
> implementing a module *at least* in pure Python would help mitigate
> this duplicate effort.
>
> The purpose of this PEP is to minimize this duplicate effort by
> mandating that all new modules added to Python's standard library
> *must* have a pure Python implementation _unless_ special dispensation
> is given. This makes sure that a module in the stdlib is available to
> all VMs and not just to CPython (pre-existing modules that do not meet
> this requirement are exempt, although there is nothing preventing
> someone from adding in a pure Python implementation retroactively).
>
> Re-implementing parts (or all) of a module in C (in the case
> of CPython) is still allowed for performance reasons, but any such
> accelerated code must pass the same test suite (sans VM- or C-specific
> tests) to verify semantics and prevent divergence. To accomplish this,
> the test suite for the module must have comprehensive coverage of the
> pure Python implementation before the acceleration code may be added.
>
>
> Details
> =======
>
> Starting in Python 3.3, any modules added to the standard library must
> have a pure Python implementation. This rule can only be ignored if
> the Python development team grants a special exemption for the module.
> Typically the exemption will be granted only when a module wraps a
> specific C-based library (e.g., sqlite3_). In granting an exemption it
> will be recognized that the module will be considered exclusive to
> CPython and not part of Python's standard library that other VMs are
> expected to support. Usage of ``ctypes`` to provide an
> API for a C library will continue to be frowned upon as ``ctypes``
> lacks compiler guarantees that C code typically relies upon to prevent
> certain errors from occurring (e.g., API changes).
>
> Even though a pure Python implementation is mandated by this PEP, it
> does not preclude the use of a companion acceleration module. If an
> acceleration module is provided it is to be named the same as the
> module it is accelerating with an underscore attached as a prefix,
> e.g., ``_warnings`` for ``warnings``. The common pattern to access
> the accelerated code from the pure Python implementation is to import
> it with an ``import *``, e.g., ``from _warnings import *``. This is
> typically done at the end of the module to allow it to overwrite
> specific Python objects with their accelerated equivalents. This kind
> of import can also be done before the end of the module when needed,
> e.g., an accelerated base class is provided but is then subclassed by
> Python code. This PEP does not mandate that pre-existing modules in
> the stdlib that lack a pure Python equivalent gain such a module. But
> if people do volunteer to provide and maintain a pure Python
> equivalent (e.g., the PyPy team volunteering their pure Python
> implementation of the ``csv`` module and maintaining it) then such
> code will be accepted. In those instances the C version is considered
> the reference implementation in terms of expected semantics.
>
> Any new accelerated code must act as a drop-in replacement as close
> to the pure Python implementation as reasonable. Technical details of
> the VM providing the accelerated code are allowed to differ as
> necessary, e.g., a class being a ``type`` when implemented in C. To
> verify that the Python and equivalent C code operate as similarly as
> possible, both code bases must be tested using the same tests which
> apply to the pure Python code (tests specific to the C code or any VM
> do not follow under this requirement). The test suite is expected to
> be extensive in order to verify expected semantics.
>
> Acting as a drop-in replacement also dictates that no public API be
> provided in accelerated code that does not exist in the pure Python
> code. Without this requirement people could accidentally come to rely
> on a detail in the accelerated code which is not made available to
> other VMs that use the pure Python implementation. To help verify
> that the contract of semantic equivalence is being met, a module must
> be tested both with and without its accelerated code as thoroughly as
> possible.
>
> As an example, to write tests which exercise both the pure Python and
> C accelerated versions of a module, a basic idiom can be followed::
>
> import collections.abc
> from test.support import import_fresh_module, run_unittest
> import unittest
>
> c_heapq = import_fresh_module('heapq', fresh=['_heapq'])
> py_heapq = import_fresh_module('heapq', blocked=['_heapq'])
>
>
> class ExampleTest(unittest.TestCase):
>
> def test_heappop_exc_for_non_MutableSequence(self):
> # Raise TypeError when heap is not a
> # collections.abc.MutableSequence.
> class Spam:
> """Test class lacking many ABC-required methods
> (e.g., pop())."""
> def __len__(self):
> return 0
>
> heap = Spam()
> self.assertFalse(isinstance(heap,
> collections.abc.MutableSequence))
> with self.assertRaises(TypeError):
> self.heapq.heappop(heap)
>
>
> class AcceleratedExampleTest(ExampleTest):
>
> """Test using the accelerated code."""
>
> heapq = c_heapq
>
>
> class PyExampleTest(ExampleTest):
>
> """Test with just the pure Python code."""
>
> heapq = py_heapq
>
>
> def test_main():
> run_unittest(AcceleratedExampleTest, PyExampleTest)
>
>
> if __name__ == '__main__':
> test_main()
>
>
> If this test were to provide extensive coverage for
> ``heapq.heappop()`` in the pure Python implementation then the
> accelerated C code would be allowed to be added to CPython's standard
> library. If it did not, then the test suite would need to be updated
> until proper coverage was provided before the accelerated C code
> could be added.
>
> To also help with compatibility, C code should use abstract APIs on
> objects to prevent accidental dependence on specific types. For
> instance, if a function accepts a sequence then the C code should
> default to using `PyObject_GetItem()` instead of something like
> `PyList_GetItem()`. C code is allowed to have a fast path if the
> proper `PyList_Check()` is used, but otherwise APIs should work with
> any object that duck types to the proper interface instead of a
> specific type.
>
>
> Copyright
> =========
>
> This document has been placed in the public domain.
>
>
> .. _IronPython: http://ironpython.net/
> .. _Jython: http://www.jython.org/
> .. _PyPy: http://pypy.org/
> .. _C API of CPython: http://docs.python.org/py3k/c-api/index.html
> .. _sqlite3: http://docs.python.org/py3k/library/sqlite3.html
>
>
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