236 lines
9.6 KiB
Plaintext
236 lines
9.6 KiB
Plaintext
PEP: 399
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Title: Pure Python/C Accelerator Module Compatibility Requirements
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Version: $Revision: 88219 $
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Last-Modified: $Date: 2011-01-27 13:47:00 -0800 (Thu, 27 Jan 2011) $
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Author: Brett Cannon <brett@python.org>
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Status: Draft
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Type: Informational
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Content-Type: text/x-rst
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Created: 04-Apr-2011
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Python-Version: 3.3
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Post-History: 04-Apr-2011, 12-Apr-2011
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Abstract
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========
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The Python standard library under CPython contains various instances
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of modules implemented in both pure Python and C (either entirely or
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partially). This PEP requires that in these instances that the
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C code *must* pass the test suite used for the pure Python code
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so as to act as much as a drop-in replacement as possible
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(C- and VM-specific tests are exempt). It is also required that new
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C-based modules lacking a pure Python equivalent implementation get
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special permissions to be added to the standard library.
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Rationale
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=========
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Python has grown beyond the CPython virtual machine (VM). IronPython_,
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Jython_, and PyPy_ all currently being viable alternatives to the
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CPython VM. This VM ecosystem that has sprung up around the Python
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programming language has led to Python being used in many different
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areas where CPython cannot be used, e.g., Jython allowing Python to be
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used in Java applications.
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A problem all of the VMs other than CPython face is handling modules
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from the standard library that are implemented (to some extent) in C.
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Since they do not typically support the entire `C API of Python`_ they
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are unable to use the code used to create the module. Often times this
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leads these other VMs to either re-implement the modules in pure
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Python or in the programming language used to implement the VM
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(e.g., in C# for IronPython). This duplication of effort between
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CPython, PyPy, Jython, and IronPython is extremely unfortunate as
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implementing a module *at least* in pure Python would help mitigate
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this duplicate effort.
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The purpose of this PEP is to minimize this duplicate effort by
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mandating that all new modules added to Python's standard library
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*must* have a pure Python implementation _unless_ special dispensation
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is given. This makes sure that a module in the stdlib is available to
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all VMs and not just to CPython (pre-existing modules that do not meet
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this requirement are exempt, although there is nothing preventing
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someone from adding in a pure Python implementation retroactively).
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Re-implementing parts (or all) of a module in C (in the case
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of CPython) is still allowed for performance reasons, but any such
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accelerated code must pass the same test suite (sans VM- or C-specific
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tests) to verify semantics and prevent divergence. To accomplish this,
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the test suite for the module must have 100% branch coverage of the
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pure Python implementation before the acceleration code may be added.
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This is to prevent users from accidentally relying
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on semantics that are specific to the C code and are not reflected in
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the pure Python implementation that other VMs rely upon. For example,
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in CPython 3.2.0, ``heapq.heappop()`` does an explicit type
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check in its accelerated C code while the Python code uses duck
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typing::
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from test.support import import_fresh_module
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c_heapq = import_fresh_module('heapq', fresh=['_heapq'])
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py_heapq = import_fresh_module('heapq', blocked=['_heapq'])
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class Spam:
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"""Tester class which defines no other magic methods but
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__len__()."""
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def __len__(self):
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return 0
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try:
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c_heapq.heappop(Spam())
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except TypeError:
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# Explicit type check failure: "heap argument must be a list"
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pass
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try:
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py_heapq.heappop(Spam())
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except AttributeError:
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# Duck typing failure: "'Foo' object has no attribute 'pop'"
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pass
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This kind of divergence is a problem for users as they unwittingly
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write code that is CPython-specific. This is also an issue for other
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VM teams as they have to deal with bug reports from users thinking
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that they incorrectly implemented the module when in fact it was
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caused by an untested case.
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Details
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=======
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Starting in Python 3.3, any modules added to the standard library must
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have a pure Python implementation. This rule can only be ignored if
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the Python development team grants a special exemption for the module.
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Typically the exemption will be granted only when a module wraps a
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specific C-based library (e.g., sqlite3_). In granting an exemption it
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will be recognized that the module will be considered exclusive to
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CPython and not part of Python's standard library that other VMs are
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expected to support. Usage of ``ctypes`` to provide an
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API for a C library will continue to be frowned upon as ``ctypes``
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lacks compiler guarantees that C code typically relies upon to prevent
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certain errors from occurring (e.g., API changes).
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Even though a pure Python implementation is mandated by this PEP, it
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does not preclude the use of a companion acceleration module. If an
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acceleration module is provided it is to be named the same as the
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module it is accelerating with an underscore attached as a prefix,
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e.g., ``_warnings`` for ``warnings``. The common pattern to access
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the accelerated code from the pure Python implementation is to import
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it with an ``import *``, e.g., ``from _warnings import *``. This is
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typically done at the end of the module to allow it to overwrite
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specific Python objects with their accelerated equivalents. This kind
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of import can also be done before the end of the module when needed,
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e.g., an accelerated base class is provided but is then subclassed by
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Python code. This PEP does not mandate that pre-existing modules in
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the stdlib that lack a pure Python equivalent gain such a module. But
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if people do volunteer to provide and maintain a pure Python
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equivalent (e.g., the PyPy team volunteering their pure Python
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implementation of the ``csv`` module and maintaining it) then such
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code will be accepted.
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This requirement does not apply to modules already existing as only C
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code in the standard library. It is acceptable to retroactively add a
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pure Python implementation of a module implemented entirely in C, but
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in those instances the C version is considered the reference
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implementation in terms of expected semantics.
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Any new accelerated code must act as a drop-in replacement as close
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to the pure Python implementation as reasonable. Technical details of
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the VM providing the accelerated code are allowed to differ as
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necessary, e.g., a class being a ``type`` when implemented in C. To
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verify that the Python and equivalent C code operate as similarly as
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possible, both code bases must be tested using the same tests which
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apply to the pure Python code (tests specific to the C code or any VM
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do not follow under this requirement). To make sure that the test
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suite is thorough enough to cover all relevant semantics, the tests
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must have 100% branch coverage for the Python code being replaced by
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C code. This will make sure that the new acceleration code will
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operate as much like a drop-in replacement for the Python code is as
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possible. Testing should still be done for issues that come up when
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working with C code even if it is not explicitly required to meet the
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coverage requirement, e.g., Tests should be aware that C code typically
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has special paths for things such as built-in types, subclasses of
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built-in types, etc.
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Acting as a drop-in replacement also dictates that no public API be
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provided in accelerated code that does not exist in the pure Python
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code. Without this requirement people could accidentally come to rely
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on a detail in the accelerated code which is not made available to
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other VMs that use the pure Python implementation. To help verify
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that the contract of semantic equivalence is being met, a module must
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be tested both with and without its accelerated code as thoroughly as
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possible.
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As an example, to write tests which exercise both the pure Python and
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C accelerated versions of a module, a basic idiom can be followed::
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import collections.abc
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from test.support import import_fresh_module, run_unittest
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import unittest
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c_heapq = import_fresh_module('heapq', fresh=['_heapq'])
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py_heapq = import_fresh_module('heapq', blocked=['_heapq'])
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class ExampleTest(unittest.TestCase):
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def test_heappop_exc_for_non_MutableSequence(self):
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# Raise TypeError when heap is not a
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# collections.abc.MutableSequence.
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class Spam:
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"""Test class lacking many ABC-required methods
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(e.g., pop())."""
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def __len__(self):
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return 0
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heap = Spam()
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self.assertFalse(isinstance(heap,
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collections.abc.MutableSequence))
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with self.assertRaises(TypeError):
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self.heapq.heappop(heap)
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class AcceleratedExampleTest(ExampleTest):
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"""Test using the accelerated code."""
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heapq = c_heapq
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class PyExampleTest(ExampleTest):
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"""Test with just the pure Python code."""
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heapq = py_heapq
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def test_main():
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run_unittest(AcceleratedExampleTest, PyExampleTest)
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if __name__ == '__main__':
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test_main()
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If this test were to provide 100% branch coverage for
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``heapq.heappop()`` in the pure Python implementation then the
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accelerated C code would be allowed to be added to CPython's standard
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library. If it did not, then the test suite would need to be updated
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until 100% branch coverage was provided before the accelerated C code
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could be added.
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Copyright
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=========
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This document has been placed in the public domain.
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.. _IronPython: http://ironpython.net/
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.. _Jython: http://www.jython.org/
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.. _PyPy: http://pypy.org/
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.. _C API of Python: http://docs.python.org/py3k/c-api/index.html
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.. _sqlite3: http://docs.python.org/py3k/library/sqlite3.html
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