Mailman 3 January 2016 - Python-Dev

Typehinting repo moved to python/typing
by Guido van Rossum Jan. 23, 2016

Jan. 23, 2016

This is just a note that with Benjamin's help we've moved the ambv/typehinting repo on GitHub into the python org, so its URL is now https://github.com/python/typing . This repo was used most intensely for discussions during PEP 484's drafting period. It also contains the code for typing.py, repackaged for earlier releases on PyPI. The issue tracker is still open for proposals to change PEP 484, which is not unheard of given its provisional status. If you find a pointer to the original … [View More]

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python3 k1om dissociation permanence
by ƦOB COASTN Jan. 22, 2016

Jan. 22, 2016

Hello, Enabling the build system for Intel MIC k1om is non-trivial using Python-3.4.4 Using Python2 for the k1om is very popular, but I require Python3 support on k1om. The first requirement to complete this build involved the download and extraction of pre-built MPSS RPM's. Then built required host python bins using GCC. Lastly, build MIC bins using ICC. The exacts are appended to the end of this message. I would like to discuss a few change requirements that trial and error has revealed. … [View More]1.) libffi requires the University OF Cantabria patch because the k1om is not binary compatible with x86_64. [attached] These libffi changes could be implemented using the __MIC__ or __KNC__ macros. *see https://software.intel.com/en-us/node/514528 2.) ./configure script halts during failure to locate readelf for the host. I simply commented out these lines in the ./configure file: #if test "$cross_compiling" = yes; then #case "$READELF" in #readelf|:) #as_fn_error $? "readelf for the host is required for cross builds" "$LINENO" 5 #;; #esac #fi Ideally, ./configure would support ICC and k1om. Am I missing something in the configure/make commands below? Is it possible to bypass the readelf requirement when cross-compiling for k1om? Additionally, are any of the command line parameters below unnecessary? PKG_CONFIG_LIBDIR PKG_CONFIG_PATH PYTHON_FOR_BUILD _PYTHON_HOST_PLATFORM HOSTPGEN HOSTARCH BUILDARCH Thanks, Rob #copy/unpack the k1om bins tarball cd /home/ wget mpss-3.4.6-k1om.tar tar xvf mpss-3.4.6-k1om.tar cd /home/mpss-3.4.6/k1om/ for rpm in *.rpm; do rpm2cpio $rpm | cpio -idm; done #vars PythonVersion=Python-3.4.4 k1om_rpm=/home/mpss-3.4.6/k1om/ INSTALLPREFIX=/home/Python/release/$PythonVersion-mic SRC=/home/Python/$PythonVersion echo "Compiling host Python" cd $SRC && make distclean cd $SRC && ./configure cd $SRC && make python Parser/pgen rm -f $SRC/hostpython mv $SRC/python $SRC/hostpython rm -f $SRC/Parser/hostpgen mv $SRC/Parser/pgen $SRC/Parser/hostpgen cd $SRC && make distclean echo "Configuring Python for MIC..." cd $SRC && CONFIG_SITE=config.site \ ./configure \ CC="icc -mmic" \ CFLAGS="-I$k1om_rpm/include -I$k1om_rpm/usr/include -wd10006" \ CXX="icpc -mmic" \ CPPFLAGS="-I$k1om_rpm/include -I$k1om_rpm/usr/include -wd10006" \ PKG_CONFIG_LIBDIR="$k1om_rpm/usr/lib64/pkgconfig" \ PKG_CONFIG_PATH="$k1om_rpm/usr/lib64/pkgconfig" \ --host=x86_64-k1om-linux \ --build=x86_64-linux-gnu \ --with-cxx-main="icpc -mmic" \ --disable-ipv6 echo "done" echo "Compiling Python for MIC..." cd $SRC && make \ PYTHON_FOR_BUILD=./hostpython \ _PYTHON_HOST_PLATFORM=x86_64-k1om-linux \ HOSTPGEN=./Parser/hostpgen \ HOSTARCH=x86_64-k1om-linux \ BUILDARCH=x86_64-linux-gnu \ EXTRA_CFLAGS="-fp-model precise -shared -fPIC" \ LDFLAGS="-L$k1om_rpm/lib64 -L$k1om_rpm/usr/lib64" echo "done" echo "Installing Python for MIC" mkdir -p $INSTALLPREFIX cd $SRC && make install \ PYTHON_FOR_BUILD=./hostpython \ _PYTHON_HOST_PLATFORM=x86_64-k1om-linux \ prefix=$INSTALLPREFIX echo "done" [View Less]

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Summary of Python tracker Issues
by Python tracker Jan. 22, 2016

Jan. 22, 2016

ACTIVITY SUMMARY (2016-01-15 - 2016-01-22) 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 5354 (-33) closed 32585 (+89) total 37939 (+56) Open issues with patches: 2348 Issues opened (31) ================== #25791: Raise an ImportWarning when __spec__.parent/__package__ isn't http://bugs.python.org/issue25791 reopened by brett.cannon #26128: Let … [View More]

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Devguide: Add a table summarizing status of Python branches
by Victor Stinner Jan. 22, 2016

Jan. 22, 2016

Hi, I proposed a patch for the devguide to give the current status of all Python branches: active, bugfix, security only, end-of-line, with their end-of-life when applicable (past date or scheduled date) http://bugs.python.org/issue26165 What do you think? Does it look correct? We will have to update this table each time that the status of a branch change. Hopefully, it's not a common event, so it will not require a lot of work for release managers :-) Victor

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Update PEP 7 to require curly braces in C
by Brett Cannon Jan. 22, 2016

Jan. 22, 2016

While doing a review of http://bugs.python.org/review/26129/ I asked to have curly braces put around all `if` statement bodies. Serhiy pointed out that PEP 7 says curly braces are optional: https://www.python.org/dev/peps/pep-0007/#id5. I would like to change that. My argument is to require them to prevent bugs like the one Apple made with OpenSSL about two years ago: https://www.imperialviolet.org/2014/02/22/applebug.html. Skipping the curly braces is purely an aesthetic thing while leaving … [View More]

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PEP 509: Add a private version to dict
by Victor Stinner Jan. 21, 2016

Jan. 21, 2016

Hi, After a first round on python-ideas, here is the second version of my PEP. The main changes since the first version are that the dictionary version is no more exposed at the Python level and the field type now also has a size of 64-bit on 32-bit platforms. The PEP is part of a serie of 3 PEP adding an API to implement a static Python optimizer specializing functions with guards. The second PEP is currently discussed on python-ideas and I'm still working on the third PEP. Thanks to Red … [View More]Hat for giving me time to experiment on this. HTML version: https://www.python.org/dev/peps/pep-0509/ PEP: 509 Title: Add a private version to dict Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stinner(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 4-January-2016 Python-Version: 3.6 Abstract ======== Add a new private version to builtin ``dict`` type, incremented at each change, to implement fast guards on namespaces. Rationale ========= In Python, the builtin ``dict`` type is used by many instructions. For example, the ``LOAD_GLOBAL`` instruction searchs for a variable in the global namespace, or in the builtins namespace (two dict lookups). Python uses ``dict`` for the builtins namespace, globals namespace, type namespaces, instance namespaces, etc. The local namespace (namespace of a function) is usually optimized to an array, but it can be a dict too. Python is hard to optimize because almost everything is mutable: builtin functions, function code, global variables, local variables, ... can be modified at runtime. Implementing optimizations respecting the Python semantics requires to detect when "something changes": we will call these checks "guards". The speedup of optimizations depends on the speed of guard checks. This PEP proposes to add a version to dictionaries to implement fast guards on namespaces. Dictionary lookups can be skipped if the version does not change which is the common case for most namespaces. The performance of a guard does not depend on the number of watched dictionary entries, complexity of O(1), if the dictionary version does not change. Example of optimization: copy the value of a global variable to function constants. This optimization requires a guard on the global variable to check if it was modified. If the variable is modified, the variable must be loaded at runtime when the function is called, instead of using the constant. See the `PEP 510 -- Specialized functions with guards <https://www.python.org/dev/peps/pep-0510/>`_ for the concrete usage of guards to specialize functions and for the rationale on Python static optimizers. Guard example ============= Pseudo-code of an fast guard to check if a dictionary entry was modified (created, updated or deleted) using an hypothetical ``dict_get_version(dict)`` function:: UNSET = object() class GuardDictKey: def __init__(self, dict, key): self.dict = dict self.key = key self.value = dict.get(key, UNSET) self.version = dict_get_version(dict) def check(self): """Return True if the dictionary entry did not changed.""" # read the version field of the dict structure version = dict_get_version(self.dict) if version == self.version: # Fast-path: dictionary lookup avoided return True # lookup in the dictionary value = self.dict.get(self.key, UNSET) if value is self.value: # another key was modified: # cache the new dictionary version self.version = version return True # the key was modified return False Usage of the dict version ========================= Specialized functions using guards ---------------------------------- The `PEP 510 -- Specialized functions with guards <https://www.python.org/dev/peps/pep-0510/>`_ proposes an API to support specialized functions with guards. It allows to implement static optimizers for Python without breaking the Python semantics. Example of a static Python optimizer: the astoptimizer of the `FAT Python <http://faster-cpython.readthedocs.org/fat_python.html>`_ project implements many optimizations which require guards on namespaces. Examples: * Call pure builtins: to replace ``len("abc")`` with ``3``, guards on ``builtins.__dict__['len']`` and ``globals()['len']`` are required * Loop unrolling: to unroll the loop ``for i in range(...): ...``, guards on ``builtins.__dict__['range']`` and ``globals()['range']`` are required Pyjion ------ According of Brett Cannon, one of the two main developers of Pyjion, Pyjion can also benefit from dictionary version to implement optimizations. Pyjion is a JIT compiler for Python based upon CoreCLR (Microsoft .NET Core runtime). Unladen Swallow --------------- Even if dictionary version was not explicitly mentionned, optimization globals and builtins lookup was part of the Unladen Swallow plan: "Implement one of the several proposed schemes for speeding lookups of globals and builtins." Source: `Unladen Swallow ProjectPlan <https://code.google.com/p/unladen-swallow/wiki/ProjectPlan>`_. Unladen Swallow is a fork of CPython 2.6.1 adding a JIT compiler implemented with LLVM. The project stopped in 2011: `Unladen Swallow Retrospective <http://qinsb.blogspot.com.au/2011/03/unladen-swallow-retrospective.html>`_. Changes ======= Add a ``ma_version`` field to the ``PyDictObject`` structure with the C type ``PY_INT64_T``, 64-bit unsigned integer. New empty dictionaries are initilized to version ``0``. The version is incremented at each change: * ``clear()`` if the dict was non-empty * ``pop(key)`` if the key exists * ``popitem()`` if the dict is non-empty * ``setdefault(key, value)`` if the `key` does not exist * ``__detitem__(key)`` if the key exists * ``__setitem__(key, value)`` if the `key` doesn't exist or if the value is different * ``update(...)`` if new values are different than existing values (the version can be incremented multiple times) Example using an hypothetical ``dict_get_version(dict)`` function:: >>> d = {} >>> dict_get_version(d) 0 >>> d['key'] = 'value' >>> dict_get_version(d) 1 >>> d['key'] = 'new value' >>> dict_get_version(d) 2 >>> del d['key'] >>> dict_get_version(d) 3 If a dictionary is created with items, the version is also incremented at each dictionary insertion. Example:: >>> d=dict(x=7, y=33) >>> dict_get_version(d) 2 The version is not incremented if an existing key is set to the same value. For efficiency, values are compared by their identity: ``new_value is old_value``, not by their content: ``new_value == old_value``. Example:: >>> d={} >>> value = object() >>> d['key'] = value >>> dict_get_version(d) 2 >>> d['key'] = value >>> dict_get_version(d) 2 .. note:: CPython uses some singleton like integers in the range [-5; 257], empty tuple, empty strings, Unicode strings of a single character in the range [U+0000; U+00FF], etc. When a key is set twice to the same singleton, the version is not modified. Implementation ============== The `issue #26058: PEP 509: Add ma_version to PyDictObject <https://bugs.python.org/issue26058>`_ contains a patch implementing this PEP. On pybench and timeit microbenchmarks, the patch does not seem to add any overhead on dictionary operations. When the version does not change, ``PyDict_GetItem()`` takes 14.8 ns for a dictioanry lookup, whereas a guard check only takes 3.8 ns. Moreover, a guard can watch for multiple keys. For example, for an optimization using 10 global variables in a function, 10 dictionary lookups costs 148 ns, whereas the guard still only costs 3.8 ns when the version does not change (39x as fast). Integer overflow ================ The implementation uses the C unsigned integer type ``PY_INT64_T`` to store the version, a 64 bits unsigned integer. The C code uses ``version++``. On integer overflow, the version is wrapped to ``0`` (and then continue to be incremented) according to the C standard. After an integer overflow, a guard can succeed whereas the watched dictionary key was modified. The bug occurs if the dictionary is modified at least ``2 ** 64`` times between two checks of the guard and if the new version (theorical value with no integer overflow) is equal to the old version modulo ``2 ** 64``. If a dictionary is modified each nanosecond, an overflow takes longer than 584 years. Using a 32-bit version, the overflow occurs only after 4 seconds. That's why a 64-bit unsigned type is also used on 32-bit systems. A dictionary lookup at the C level takes 14.8 ns. A risk of a bug every 584 years is acceptable. Alternatives ============ Expose the version at Python level as a read-only __version__ property ---------------------------------------------------------------------- The first version of the PEP proposed to expose the dictionary version as a read-only ``__version__`` property at Python level, and also to add the property to ``collections.UserDict`` (since this type must mimick the ``dict`` API). There are multiple issues: * To be consistent and avoid bad surprises, the version must be added to all mapping types. Implementing a new mapping type would require extra work for no benefit, since the version is only required on the ``dict`` type in practice. * All Python implementations must implement this new property, it gives more work to other implementations, whereas they may not use the dictionary version at all. * The ``__version__`` can be wrapped on integer overflow. It is error prone: using ``dict.__version__ <= guard_version`` is wrong, ``dict.__version__ == guard_version`` must be used instead to reduce the risk of bug on integer overflow (even if the integer overflow is unlikely in practice). * Exposing the dictionary version at Python level can lead the false assumption on performances. Checking ``dict.__version__`` at the Python level is not faster than a dictionary lookup. A dictionary lookup has a cost of 48.7 ns and checking a guard has a cost of 47.5 ns, the difference is only 1.2 ns (3%):: $ ./python -m timeit -s 'd = {str(i):i for i in range(100)}' 'd["33"] == 33' 10000000 loops, best of 3: 0.0487 usec per loop $ ./python -m timeit -s 'd = {str(i):i for i in range(100)}' 'd.__version__ == 100' 10000000 loops, best of 3: 0.0475 usec per loop Bikeshedding on the property name: * ``__cache_token__``: name proposed by Nick Coghlan, name coming from `abc.get_cache_token() <https://docs.python.org/3/library/abc.html#abc.get_cache_token>`_. * ``__version__`` * ``__timestamp__`` Add a version to each dict entry -------------------------------- A single version per dictionary requires to keep a strong reference to the value which can keep the value alive longer than expected. If we add also a version per dictionary entry, the guard can only store the entry version to avoid the strong reference to the value (only strong references to the dictionary and to the key are needed). Changes: add a ``me_version`` field to the ``PyDictKeyEntry`` structure, the field has the C type ``PY_INT64_T``. When a key is created or modified, the entry version is set to the dictionary version which is incremented at any change (create, modify, delete). Pseudo-code of an fast guard to check if a dictionary key was modified using hypothetical ``dict_get_version(dict)`` ``dict_get_entry_version(dict)`` functions:: UNSET = object() class GuardDictKey: def __init__(self, dict, key): self.dict = dict self.key = key self.dict_version = dict_get_version(dict) self.entry_version = dict_get_entry_version(dict, key) def check(self): """Return True if the dictionary entry did not changed.""" # read the version field of the dict structure dict_version = dict_get_version(self.dict) if dict_version == self.version: # Fast-path: dictionary lookup avoided return True # lookup in the dictionary entry_version = get_dict_key_version(dict, key) if entry_version == self.entry_version: # another key was modified: # cache the new dictionary version self.dict_version = dict_version return True # the key was modified return False The main drawback of this option is the impact on the memory footprint. It increases the size of each dictionary entry, so the overhead depends on the number of buckets (dictionary entries, used or unused yet). For example, it increases the size of each dictionary entry by 8 bytes on 64-bit system. In Python, the memory footprint matters and the trend is to reduce it. Examples: * `PEP 393 -- Flexible String Representation <https://www.python.org/dev/peps/pep-0393/>`_ * `PEP 412 -- Key-Sharing Dictionary <https://www.python.org/dev/peps/pep-0412/>`_ Add a new dict subtype ---------------------- Add a new ``verdict`` type, subtype of ``dict``. When guards are needed, use the ``verdict`` for namespaces (module namespace, type namespace, instance namespace, etc.) instead of ``dict``. Leave the ``dict`` type unchanged to not add any overhead (memory footprint) when guards are not needed. Technical issue: a lot of C code in the wild, including CPython core, expecting the exact ``dict`` type. Issues: * ``exec()`` requires a ``dict`` for globals and locals. A lot of code use ``globals={}``. It is not possible to cast the ``dict`` to a ``dict`` subtype because the caller expects the ``globals`` parameter to be modified (``dict`` is mutable). * Functions call directly ``PyDict_xxx()`` functions, instead of calling ``PyObject_xxx()`` if the object is a ``dict`` subtype * ``PyDict_CheckExact()`` check fails on ``dict`` subtype, whereas some functions require the exact ``dict`` type. * ``Python/ceval.c`` does not completly supports dict subtypes for namespaces The ``exec()`` issue is a blocker issue. Other issues: * The garbage collector has a special code to "untrack" ``dict`` instances. If a ``dict`` subtype is used for namespaces, the garbage collector can be unable to break some reference cycles. * Some functions have a fast-path for ``dict`` which would not be taken for ``dict`` subtypes, and so it would make Python a little bit slower. Prior Art ========= Method cache and type version tag --------------------------------- In 2007, Armin Rigo wrote a patch to to implement a cache of methods. It was merged into Python 2.6. The patch adds a "type attribute cache version tag" (``tp_version_tag``) and a "valid version tag" flag to types (the ``PyTypeObject`` structure). The type version tag is not available at the Python level. The version tag has the C type ``unsigned int``. The cache is a global hash table of 4096 entries, shared by all types. The cache is global to "make it fast, have a deterministic and low memory footprint, and be easy to invalidate". Each cache entry has a version tag. A global version tag is used to create the next version tag, it also has the C type ``unsigned int``. By default, a type has its "valid version tag" flag cleared to indicate that the version tag is invalid. When the first method of the type is cached, the version tag and the "valid version tag" flag are set. When a type is modified, the "valid version tag" flag of the type and its subclasses is cleared. Later, when a cache entry of these types is used, the entry is removed because its version tag is outdated. On integer overflow, the whole cache is cleared and the global version tag is reset to ``0``. See `Method cache (issue #1685986) <https://bugs.python.org/issue1685986>`_ and `Armin's method cache optimization updated for Python 2.6 (issue #1700288) <https://bugs.python.org/issue1700288>`_. Globals / builtins cache ------------------------ In 2010, Antoine Pitrou proposed a `Globals / builtins cache (issue #10401) <http://bugs.python.org/issue10401>`_ which adds a private ``ma_version`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``Py_ssize_t``. The patch adds a "global and builtin cache" to functions and frames, and changes ``LOAD_GLOBAL`` and ``STORE_GLOBAL`` instructions to use the cache. The change on the ``PyDictObject`` structure is very similar to this PEP. Cached globals+builtins lookup ------------------------------ In 2006, Andrea Griffini proposed a patch implementing a `Cached globals+builtins lookup optimization <https://bugs.python.org/issue1616125>`_. The patch adds a private ``timestamp`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``size_t``. Thread on python-dev: `About dictionary lookup caching <https://mail.python.org/pipermail/python-dev/2006-December/070348.html>`_. Guard against changing dict during iteration -------------------------------------------- In 2013, Serhiy Storchaka proposed `Guard against changing dict during iteration (issue #19332) <https://bugs.python.org/issue19332>`_ which adds a ``ma_count`` field to the ``PyDictObject`` structure (``dict`` type), the field has the C type ``size_t``. This field is incremented when the dictionary is modified, and so is very similar to the proposed dictionary version. Sadly, the dictionary version proposed in this PEP doesn't help to detect dictionary mutation. The dictionary version changes when values are replaced, whereas modifying dictionary values while iterating on dictionary keys is legit in Python. PySizer ------- `PySizer <http://pysizer.8325.org/>`_: a memory profiler for Python, Google Summer of Code 2005 project by Nick Smallbone. This project has a patch for CPython 2.4 which adds ``key_time`` and ``value_time`` fields to dictionary entries. It uses a global process-wide counter for dictionaries, incremented each time that a dictionary is modified. The times are used to decide when child objects first appeared in their parent objects. Discussion ========== Thread on the python-ideas mailing list: `RFC: PEP: Add dict.__version__ <https://mail.python.org/pipermail/python-ideas/2016-January/037702.html>`_. Copyright ========= This document has been placed in the public domain. [View Less]

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Reference cycle on the module dict (globals())
by Victor Stinner Jan. 19, 2016

Jan. 19, 2016

Hi, While working on my FAT Python optimizer project, I found an annoying bug in my code. When at least one guard is created with a reference to the global namespace (globals(), the module dictionary), objects of the module are no more removed at exit. Example: --- import sys class MessageAtExit: def __del__(self): print('__del__ called') # display a message at exit, when message_at_exit is removed message_at_exit = MessageAtExit() # create a reference cycle: # module -> … [View More]

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_PyThreadState_Current
by Maciej Fijalkowski Jan. 19, 2016

Jan. 19, 2016

Hi change in between 3.5.0 and 3.5.1 (hiding _PyThreadState_Current and pyatomic.h) broke vmprof. The problem is that as a profile, vmprof can really encounter _PyThreadState_Current being null, while crashing an interpreter is a bit not ideal in this case. Any chance, a) _PyThreadState_Current can be restored in visibility? b) can I get a better API to get it in case it can be NULL, but also in 3.5 (since it works in 3.5.0 and breaks in 3.5.1) Cheers, fijal

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PEP 510: Specialize functions with guards
by Victor Stinner Jan. 19, 2016

Jan. 19, 2016

Hi, I posted a first version of this PEP on python-ideas and I got interesting feedback. The main changes in this second version is that the whole API is now private: no more exposed in Python at all, only in the Python C API (C level). The PEP is part of a serie of 3 PEP (509, 510, 511) adding an API to implement a static Python optimizer specializing functions with guards. I will post a first version of the PEP 511 to python-ideas soon. HTML version: https://www.python.org/dev/peps/pep-… [View More]0510/#changes Victor PEP: 510 Title: Specialize functions with guards Version: $Revision$ Last-Modified: $Date$ Author: Victor Stinner <victor.stinner(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 4-January-2016 Python-Version: 3.6 Abstract ======== Add functions to the Python C API to specialize pure Python functions: add specialized codes with guards. It allows to implement static optimizers respecting the Python semantics. Rationale ========= Python semantics ---------------- Python is hard to optimize because almost everything is mutable: builtin functions, function code, global variables, local variables, ... can be modified at runtime. Implement optimizations respecting the Python semantics requires to detect when "something changes", we will call these checks "guards". This PEP proposes to add a public API to the Python C API to add specialized codes with guards to a function. When the function is called, a specialized code is used if nothing changed, otherwise use the original bytecode. Even if guards help to respect most parts of the Python semantics, it's hard to optimize Python without making subtle changes on the exact behaviour. CPython has a long history and many applications rely on implementation details. A compromise must be found between "everything is mutable" and performance. Writing an optimizer is out of the scope of this PEP. Why not a JIT compiler? ----------------------- There are multiple JIT compilers for Python actively developed: * `PyPy <http://pypy.org/>`_ * `Pyston <https://github.com/dropbox/pyston>`_ * `Numba <http://numba.pydata.org/>`_ * `Pyjion <https://github.com/microsoft/pyjion>`_ Numba is specific to numerical computation. Pyston and Pyjion are still young. PyPy is the most complete Python interpreter, it is much faster than CPython and has a very good compatibility with CPython (it respects the Python semantics). There are still issues with Python JIT compilers which avoid them to be widely used instead of CPython. Many popular libraries like numpy, PyGTK, PyQt, PySide and wxPython are implemented in C or C++ and use the Python C API. To have a small memory footprint and better performances, Python JIT compilers do not use reference counting to use a faster garbage collector, do not use C structures of CPython objects and manage memory allocations differently. PyPy has a ``cpyext`` module which emulates the Python C API but it has worse performances than CPython and does not support the full Python C API. New features are first developped in CPython. In january 2016, the latest CPython stable version is 3.5, whereas PyPy only supports Python 2.7 and 3.2, and Pyston only supports Python 2.7. Even if PyPy has a very good compatibility with Python, some modules are still not compatible with PyPy: see `PyPy Compatibility Wiki <https://bitbucket.org/pypy/compatibility/wiki/Home>`_. The incomplete support of the the Python C API is part of this problem. There are also subtle differences between PyPy and CPython like reference counting: object destructors are always called in PyPy, but can be called "later" than in CPython. Using context managers helps to control when resources are released. Even if PyPy is much faster than CPython in a wide range of benchmarks, some users still report worse performances than CPython on some specific use cases or unstable performances. When Python is used as a scripting program for programs running less than 1 minute, JIT compilers can be slower because their startup time is higher and the JIT compiler takes time to optimize the code. For example, most Mercurial commands take a few seconds. Numba now supports ahead of time compilation, but it requires decorator to specify arguments types and it only supports numerical types. CPython 3.5 has almost no optimization: the peephole optimizer only implements basic optimizations. A static compiler is a compromise between CPython 3.5 and PyPy. .. note:: There was also the Unladen Swallow project, but it was abandoned in 2011. Examples ======== Following examples are not written to show powerful optimizations promising important speedup, but to be short and easy to understand, just to explain the principle. Hypothetical myoptimizer module ------------------------------- Examples in this PEP uses an hypothetical ``myoptimizer`` module which provides the following functions and types: * ``specialize(func, code, guards)``: add the specialized code `code` with guards `guards` to the function `func` * ``get_specialized(func)``: get the list of specialized codes as a list of ``(code, guards)`` tuples where `code` is a callable or code object and `guards` is a list of a guards * ``GuardBuiltins(name)``: guard watching for ``builtins.__dict__[name]`` and ``globals()[name]``. The guard fails if ``builtins.__dict__[name]`` is replaced, or if ``globals()[name]`` is set. Using bytecode -------------- Add specialized bytecode where the call to the pure builtin function ``chr(65)`` is replaced with its result ``"A"``:: import myoptimizer def func(): return chr(65) def fast_func(): return "A" myoptimizer.specialize(func, fast_func.__code__, [myoptimizer.GuardBuiltins("chr")]) del fast_func Example showing the behaviour of the guard:: print("func(): %s" % func()) print("#specialized: %s" % len(myoptimizer.get_specialized(func))) print() import builtins builtins.chr = lambda obj: "mock" print("func(): %s" % func()) print("#specialized: %s" % len(myoptimizer.get_specialized(func))) Output:: func(): A #specialized: 1 func(): mock #specialized: 0 The first call uses the specialized bytecode which returns the string ``"A"``. The second call removes the specialized code because the builtin ``chr()`` function was replaced, and executes the original bytecode calling ``chr(65)``. On a microbenchmark, calling the specialized bytecode takes 88 ns, whereas the original function takes 145 ns (+57 ns): 1.6 times as fast. Using builtin function ---------------------- Add the C builtin ``chr()`` function as the specialized code instead of a bytecode calling ``chr(obj)``:: import myoptimizer def func(arg): return chr(arg) myoptimizer.specialize(func, chr, [myoptimizer.GuardBuiltins("chr")]) Example showing the behaviour of the guard:: print("func(65): %s" % func(65)) print("#specialized: %s" % len(myoptimizer.get_specialized(func))) print() import builtins builtins.chr = lambda obj: "mock" print("func(65): %s" % func(65)) print("#specialized: %s" % len(myoptimizer.get_specialized(func))) Output:: func(): A #specialized: 1 func(): mock #specialized: 0 The first call calls the C builtin ``chr()`` function (without creating a Python frame). The second call removes the specialized code because the builtin ``chr()`` function was replaced, and executes the original bytecode. On a microbenchmark, calling the C builtin takes 95 ns, whereas the original bytecode takes 155 ns (+60 ns): 1.6 times as fast. Calling directly ``chr(65)`` takes 76 ns. Choose the specialized code =========================== Pseudo-code to choose the specialized code to call a pure Python function:: def call_func(func, args, kwargs): specialized = myoptimizer.get_specialized(func) nspecialized = len(specialized) index = 0 while index < nspecialized: specialized_code, guards = specialized[index] for guard in guards: check = guard(args, kwargs) if check: break if not check: # all guards succeeded: # use the specialized code return specialized_code elif check == 1: # a guard failed temporarely: # try the next specialized code index += 1 else: assert check == 2 # a guard will always fail: # remove the specialized code del specialized[index] # if a guard of each specialized code failed, or if the function # has no specialized code, use original bytecode code = func.__code__ Changes ======= Changes to the Python C API: * Add a ``PyFuncGuardObject`` object and a ``PyFuncGuard_Type`` type * Add a ``PySpecializedCode`` structure * Add the following fields to the ``PyFunctionObject`` structure:: Py_ssize_t nb_specialized; PySpecializedCode *specialized; * Add function methods: * ``PyFunction_Specialize()`` * ``PyFunction_GetSpecializedCodes()`` * ``PyFunction_GetSpecializedCode()`` None of these function and types are exposed at the Python level. All these additions are explicitly excluded of the stable ABI. When a function code is replaced (``func.__code__ = new_code``), all specialized codes and guards are removed. When a function is serialized ``pickle``, specialized codes and guards are ignored (not serialized). Specialized codes and guards are not stored in ``.pyc`` files but created and registered at runtime, when a module is loaded. Function guard -------------- Add a function guard object:: typedef struct { PyObject ob_base; int (*init) (PyObject *guard, PyObject *func); int (*check) (PyObject *guard, PyObject **stack, int na, int nk); } PyFuncGuardObject; The ``init()`` function initializes a guard: * Return ``0`` on success * Return ``1`` if the guard will always fail: ``PyFunction_Specialize()`` must ignore the specialized code * Raise an exception and return ``-1`` on error The ``check()`` function checks a guard: * Return ``0`` on success * Return ``1`` if the guard failed temporarely * Return ``2`` if the guard will always fail: the specialized code must be removed * Raise an exception and return ``-1`` on error *stack* is an array of arguments: indexed arguments followed by (*key*, *value*) pairs of keyword arguments. *na* is the number of indexed arguments. *nk* is the number of keyword arguments: the number of (*key*, *value*) pairs. `stack` contains ``na + nk * 2`` objects. Specialized code ---------------- Add a specialized code structure:: typedef struct { PyObject *code; /* callable or code object */ Py_ssize_t nb_guard; PyObject **guards; /* PyFuncGuardObject objects */ } PySpecializedCode; Function methods ---------------- Add a function method to specialize the function, add a specialized code with guards:: int PyFunction_Specialize(PyObject *func, PyObject *code, PyObject *guards) Result: * Return ``0`` on success * Return ``1`` if the specialization has been ignored * Raise an exception and return ``-1`` on error Add a function method to get the list of specialized codes:: PyObject* PyFunction_GetSpecializedCodes(PyObject *func) Return a list of (*code*, *guards*) tuples where *code* is a callable or code object and *guards* is a list of ``PyFuncGuard`` objects. Raise an exception and return ``NULL`` on error. Add a function method to get the specialized code:: PyObject* PyFunction_GetSpecializedCode(PyObject *func, PyObject **stack, int na, int nk) See ``check()`` function of guards for *stack*, *na* and *nk* arguments. Return a callable or a code object on success. Raise an exception and return ``NULL`` on error. Benchmark --------- Microbenchmark on ``python3.6 -m timeit -s 'def f(): pass' 'f()'`` (best of 3 runs): * Original Python: 79 ns * Patched Python: 79 ns According to this microbenchmark, the changes has no overhead on calling a Python function without specialization. Other implementations of Python =============================== This PEP only contains changes to the Python C API, the Python API is unchanged. Other implementations of Python are free to not implement new additions, or implement added functions as no-op: * ``PyFunction_Specialize()``: always return ``1`` (the specialization has been ignored) * ``PyFunction_GetSpecializedCodes()``: always return an empty list * ``PyFunction_GetSpecializedCode()``: return the function code object, as the existing ``PyFunction_GET_CODE()`` macro Discussion ========== Thread on the python-ideas mailing list: `RFC: PEP: Specialized functions with guards <https://mail.python.org/pipermail/python-ideas/2016-January/037703.html>`_. Copyright ========= This document has been placed in the public domain. [View Less]

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Should inspect.getargspec take any callable?
by Aviv Cohn Jan. 17, 2016

Jan. 17, 2016

Hello :)This is my first mail here, I had an idea that I'd like to propose. The `getargspec` function in the `inspect` module enforces the input parameter to be either a method or a function. def getargspec(func): """Get the names and default values of a function's arguments. A tuple of four things is returned: (args, varargs, varkw, defaults). 'args' is a list of the argument names … [View More]

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