Python-Dev April 2016

python-dev@python.org

103 participants
80 discussions

congrats on 3.5! Alas, windows 7 users are having problems installing it

by Laura Creighton

webmaster has already heard from 4 people who cannot install it. I sent them to the bug tracker or to python-list but they seem not to have gone either place. Is there some guide I should be sending them to, 'how to debug installation problems'? Laura

1 year, 5 months

RFC: PEP 509: Add a private version to dict

by Victor Stinner

Hi, I updated my PEP 509 to make the dictionary version globally unique. With *two* use cases of this PEP (Yury's method call patch and my FAT Python project), I think that the PEP is now ready to be accepted. Globally unique identifier is a requirement for Yury's patch optimizing method calls ( https://bugs.python.org/issue26110 ). It allows to check for free if the dictionary was replaced. I also renamed the ma_version field to ma_version_tag. HTML version: https://www.python.org/dev/peps/pep-0509/ Victor 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 the builtin ``dict`` type, incremented at each dictionary creation and at each dictionary 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. Since the version is globally unique, the version is also enough to check if the namespace dictionary was not replaced with a new dictionary. 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 and the dictionary was not replaced.""" # read the version 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 ========================= Speedup method calls 1.2x ------------------------- Yury Selivanov wrote a `patch to optimize method calls <https://bugs.python.org/issue26110>`_. The patch depends on the `implement per-opcode cache in ceval <https://bugs.python.org/issue26219>`_ patch which requires dictionary versions to invalidate the cache if the globals dictionary or the builtins dictionary has been modified. The cache also requires that the dictionary version is globally unique. It is possible to define a function in a namespace and call it in a different namespace: using ``exec()`` with the *globals* parameter for example. In this case, the globals dictionary was changed and the cache must be invalidated. 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 `fatoptimizer <http://fatoptimizer.readthedocs.org/>`_ 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 mentioned, optimizing 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_tag`` field to the ``PyDictObject`` structure with the C type ``PY_INT64_T``, 64-bit unsigned integer. Add also a global dictionary version. Each time a dictionary is created, the global version is incremented and the dictionary version is initialized to the global version. The global version is also incremented and copied to the dictionary version at each dictionary 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 not ``dict[key]`` * ``update(...)`` if new values are different than existing values: values are compared by identity, not by their content; the version can be incremented multiple times The ``PyDictObject`` structure is not part of the stable ABI. The field is called ``ma_version_tag`` rather than ``ma_version`` to suggest to compare it using ``version_tag == old_version_tag`` rather than ``version <= old_version`` which makes the integer overflow much likely. Example using an hypothetical ``dict_get_version(dict)`` function:: >>> d = {} >>> dict_get_version(d) 100 >>> d['key'] = 'value' >>> dict_get_version(d) 101 >>> d['key'] = 'new value' >>> dict_get_version(d) 102 >>> del d['key'] >>> dict_get_version(d) 103 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) 40 >>> d['key'] = value >>> dict_get_version(d) 40 .. 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 and Performance ============================== The `issue #26058: PEP 509: Add ma_version_tag 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 dictionary 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). The `fat module <http://fatoptimizer.readthedocs.org/en/latest/fat.html>`_ implements such guards: ``fat.GuardDict`` is based on the dictionary version. Integer overflow ================ The implementation uses the C type ``PY_UINT64_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 only occurs at a guard check if there are exaclty ``2 ** 64`` dictionary creations or modifications since the previous guard check. If a dictionary is modified every nanosecond, ``2 ** 64`` modifications takes longer than 584 years. Using a 32-bit version, it only takes 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. * 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 * 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). Mandatory 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 and the dictionary was not replaced.""" # read the version 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 completely 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 mailing lists: * python-dev: `PEP 509: Add a private version to dict <https://mail.python.org/pipermail/python-dev/2016-January/142685.html>`_ (january 2016) * python-ideas: `RFC: PEP: Add dict.__version__ <https://mail.python.org/pipermail/python-ideas/2016-January/037702.html>`_ (january 2016) Copyright ========= This document has been placed in the public domain.

4 years, 5 months

Speeding up CPython 5-10%

by Yury Selivanov

Hi, tl;dr The summary is that I have a patch that improves CPython performance up to 5-10% on macro benchmarks. Benchmarks results on Macbook Pro/Mac OS X, desktop CPU/Linux, server CPU/Linux are available at [1]. There are no slowdowns that I could reproduce consistently. There are twodifferent optimizations that yield this speedup: LOAD_METHOD/CALL_METHOD opcodes and per-opcode cache in ceval loop. LOAD_METHOD & CALL_METHOD ------------------------- We had a lot of conversations with Victor about his PEP 509, and he sent me a link to his amazing compilation of notes about CPython performance [2]. One optimization that he pointed out to me was LOAD/CALL_METHOD opcodes, an idea first originated in PyPy. There is a patch that implements this optimization, it's tracked here: [3]. There are some low level details that I explained in the issue, but I'll go over the high level design in this email as well. Every time you access a method attribute on an object, a BoundMethod object is created. It is a fairly expensive operation, despite a freelist of BoundMethods (so that memory allocation is generally avoided). The idea is to detect what looks like a method call in the compiler, and emit a pair of specialized bytecodes for that. So instead of LOAD_GLOBAL/LOAD_ATTR/CALL_FUNCTION we will have LOAD_GLOBAL/LOAD_METHOD/CALL_METHOD. LOAD_METHOD looks at the object on top of the stack, and checks if the name resolves to a method or to a regular attribute. If it's a method, then we push the unbound method object and the object to the stack. If it's an attribute, we push the resolved attribute and NULL. When CALL_METHOD looks at the stack it knows how to call the unbound method properly (pushing the object as a first arg), or how to call a regular callable. This idea does make CPython faster around 2-4%. And it surely doesn't make it slower. I think it's a safe bet to at least implement this optimization in CPython 3.6. So far, the patch only optimizes positional-only method calls. It's possible to optimize all kind of calls, but this will necessitate 3 more opcodes (explained in the issue). We'll need to do some careful benchmarking to see if it's really needed. Per-opcode cache in ceval ------------------------- While reading PEP 509, I was thinking about how we can use dict->ma_version in ceval to speed up globals lookups. One of the key assumptions (and this is what makes JITs possible) is that real-life programs don't modify globals and rebind builtins (often), and that most code paths operate on objects of the same type. In CPython, all pure Python functions have code objects. When you call a function, ceval executes its code object in a frame. Frames contain contextual information, including pointers to the globals and builtins dict. The key observation here is that almost all code objects always have same pointers to the globals (the module they were defined in) and to the builtins. And it's not a good programming practice to mutate globals or rebind builtins. Let's look at this function: def spam(): print(ham) Here are its opcodes: 2 0 LOAD_GLOBAL 0 (print) 3 LOAD_GLOBAL 1 (ham) 6 CALL_FUNCTION 1 (1 positional, 0 keyword pair) 9 POP_TOP 10 LOAD_CONST 0 (None) 13 RETURN_VALUE The opcodes we want to optimize are LAOD_GLOBAL, 0 and 3. Let's look at the first one, that loads the 'print' function from builtins. The opcode knows the following bits of information: - its offset (0), - its argument (0 -> 'print'), - its type (LOAD_GLOBAL). And these bits of information will *never* change. So if this opcode could resolve the 'print' name (from globals or builtins, likely the latter) and save the pointer to it somewhere, along with globals->ma_version and builtins->ma_version, it could, on its second call, just load this cached info back, check that the globals and builtins dict haven't changed and push the cached ref to the stack. That would save it from doing two dict lookups. We can also optimize LOAD_METHOD. There are high chances, that 'obj' in 'obj.method()' will be of the same type every time we execute the code object. So if we'd have an opcodes cache, LOAD_METHOD could then cache a pointer to the resolved unbound method, a pointer to obj.__class__, and tp_version_tag of obj.__class__. Then it would only need to check if the cached object type is the same (and that it wasn't modified) and that obj.__dict__ doesn't override 'method'. Long story short, this caching really speeds up method calls on types implemented in C. list.append becomes very fast, because list doesn't have a __dict__, so the check is very cheap (with cache). A straightforward way to implement such a cache is simple, but consumes a lot of memory, that would be just wasted, since we only need such a cache for LOAD_GLOBAL and LOAD_METHOD opcodes. So we have to be creative about the cache design. Here's what I came up with: 1. We add a few fields to the code object. 2. ceval will count how many times each code object is executed. 3. When the code object is executed over ~900 times, we mark it as "hot". We also create an 'unsigned char' array "MAPPING", with length set to match the length of the code object. So we have a 1-to-1 mapping between opcodes and MAPPING array. 4. Next ~100 calls, while the code object is "hot", LOAD_GLOBAL and LOAD_METHOD do "MAPPING[opcode_offset()]++". 5. After 1024 calls to the code object, ceval loop will iterate through the MAPPING, counting all opcodes that were executed more than 50 times. 6. We then create an array of cache structs "CACHE" (here's a link to the updated code.h file: [6]). We update MAPPING to be a mapping between opcode position and position in the CACHE. The code object is now "optimized". 7. When the code object is "optimized", LOAD_METHOD and LOAD_GLOBAL use the CACHE array for fast path. 8. When there is a cache miss, i.e. the builtins/global/obj.__dict__ were mutated, the opcode marks its entry in 'CACHE' as deoptimized, and it will never try to use the cache again. Here's a link to the issue tracker with the first version of the patch: [5]. I'm working on the patch in a github repo here: [4]. Summary ------- There are many things about this algorithm that we can improve/tweak. Perhaps we should profile code objects longer, or account for time they were executed. Maybe we shouldn't deoptimize opcodes on their first cache miss. Maybe we can come up with better data structures. We also need to profile the memory and see how much more this cache will require. One thing I'm certain about, is that we can get a 5-10% speedup of CPython with relatively low memory impact. And I think it's worth exploring that! If you're interested in these kind of optimizations, please help with code reviews, ideas, profiling and benchmarks. The latter is especially important, I'd never imagine how hard it is to come up with a good macro benchmark. I also want to thank my company MagicStack (magic.io) for sponsoring this work. Thanks, Yury [1] https://gist.github.com/1st1/aed69d63a2ff4de4c7be [2] http://faster-cpython.readthedocs.org/index.html [3] http://bugs.python.org/issue26110 [4] https://github.com/1st1/cpython/tree/opcache2 [5] http://bugs.python.org/issue26219 [6] https://github.com/python/cpython/compare/master...1st1:opcache2?expand=1#d…

4 years, 9 months

Wordcode: new regular bytecode using 16-bit units

by Victor Stinner

Hi, In the middle of recent discussions about Python performance, it was discussed to change the Python bytecode. Serhiy proposed to reuse MicroPython short bytecode to reduce the disk space and reduce the memory footprint. Demur Rumed proposes a different change to use a regular bytecode using 16-bit units: an instruction has always one 8-bit argument, it's zero if the instruction doesn't have an argument: http://bugs.python.org/issue26647 According to benchmarks, it looks faster: http://bugs.python.org/issue26647#msg263339 IMHO it's a nice enhancement: it makes the code simpler. The most interesting change is made in Python/ceval.c: - if (HAS_ARG(opcode)) - oparg = NEXTARG(); + oparg = NEXTARG(); This code is the very hot loop evaluating Python bytecode. I expect that removing a conditional branch here can reduce the CPU branch misprediction. I reviewed first versions of the change, and IMHO it's almost ready to be merged. But I would prefer to have a review from a least a second core reviewer. Can someone please review the change? -- The side effect of wordcode is that arguments in 0..255 now uses 2 bytes per instruction instead of 3, so it also reduce the size of bytecode for the most common case. Larger argument, 16-bit argument (0..65,535), now uses 4 bytes instead of 3. Arguments are supported up to 32-bit: 24-bit uses 3 units (6 bytes), 32-bit uses 4 units (8 bytes). MAKE_FUNCTION uses 16-bit argument for keyword defaults and 24-bit argument for annotations. Other common instruction known to use large argument are jumps for bytecode longer than 256 bytes. -- Right now, ceval.c still fetchs opcode and then oparg with two 8-bit instructions. Later, we can discuss if it would be possible to ensure that the bytecode is always aligned to 16-bit in memory to fetch the two bytes using a uint16_t* pointer. Maybe we can overallocate 1 byte in codeobject.c and align manually the memory block if needed. Or ceval.c should maybe copy the code if it's not aligned? Raymond Hettinger proposes something like that, but it looks like there are concerns about non-aligned memory accesses: http://bugs.python.org/issue25823 The cost of non-aligned memory accesses depends on the CPU architecture, but it can raise a SIGBUS on some arch (MIPS and SPARC?). Victor

4 years, 9 months

Wordcode v2

by Demur Rumed

Saw recent discussion: https://mail.python.org/pipermail/python-dev/2016-February/143013.html I remember trying WPython; it was fast. Unfortunately it feels it came at the wrong time when development was invested in getting py3k out the door. It also had a lot of other ideas like *_INT instructions which allowed having oparg to be a constant int rather than needing to LOAD_CONST one. Anyways I'll stop reminiscing abarnert has started an experiment with wordcode: https://github.com/abarnert/cpython/blob/c095a32f2a68ac708466b9c64906cc4d0f… I've personally benchmarked this fork with positive results. This experiment seeks to be conservative-- it doesn't seek to introduce new opcodes or combine BINARY_OP's all into a single op where the currently unused-in-wordcode arg then states the kind of binary op (à la COMPARE_OP). I've submitted a pull request which is working on fixing tests & updating peephole.c Bringing this up on the list to figure out if there's interest in a basic wordcode change. It feels like there's no downsides: faster code, smaller bytecode, simpler interpretation of bytecode (The Nth instruction starts at the 2Nth byte if you count EXTENDED_ARG as an instruction). The only downside is the transitional cost What'd be necessary for this to be pulled upstream?

4 years, 9 months

PEP 515: Underscores in Numeric Literals (revision 3)

by Georg Brandl

Hi all, after talking to Guido and Serhiy we present the next revision of this PEP. It is a compromise that we are all happy with, and a relatively restricted rule that makes additions to PEP 8 basically unnecessary. I think the discussion has shown that supporting underscores in the from-string constructors is valuable, therefore this is now added to the specification section. The remaining open question is about the reverse direction: do we want a string formatting modifier that adds underscores as thousands separators? cheers, Georg ----------------------------------------------------------------- PEP: 515 Title: Underscores in Numeric Literals Version: $Revision$ Last-Modified: $Date$ Author: Georg Brandl, Serhiy Storchaka Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 10-Feb-2016 Python-Version: 3.6 Post-History: 10-Feb-2016, 11-Feb-2016 Abstract and Rationale ====================== This PEP proposes to extend Python's syntax and number-from-string constructors so that underscores can be used as visual separators for digit grouping purposes in integral, floating-point and complex number literals. This is a common feature of other modern languages, and can aid readability of long literals, or literals whose value should clearly separate into parts, such as bytes or words in hexadecimal notation. Examples:: # grouping decimal numbers by thousands amount = 10_000_000.0 # grouping hexadecimal addresses by words addr = 0xDEAD_BEEF # grouping bits into nibbles in a binary literal flags = 0b_0011_1111_0100_1110 # same, for string conversions flags = int('0b_1111_0000', 2) Specification ============= The current proposal is to allow one underscore between digits, and after base specifiers in numeric literals. The underscores have no semantic meaning, and literals are parsed as if the underscores were absent. Literal Grammar --------------- The production list for integer literals would therefore look like this:: integer: decinteger | bininteger | octinteger | hexinteger decinteger: nonzerodigit (["_"] digit)* | "0" (["_"] "0")* bininteger: "0" ("b" | "B") (["_"] bindigit)+ octinteger: "0" ("o" | "O") (["_"] octdigit)+ hexinteger: "0" ("x" | "X") (["_"] hexdigit)+ nonzerodigit: "1"..."9" digit: "0"..."9" bindigit: "0" | "1" octdigit: "0"..."7" hexdigit: digit | "a"..."f" | "A"..."F" For floating-point and complex literals:: floatnumber: pointfloat | exponentfloat pointfloat: [digitpart] fraction | digitpart "." exponentfloat: (digitpart | pointfloat) exponent digitpart: digit (["_"] digit)* fraction: "." digitpart exponent: ("e" | "E") ["+" | "-"] digitpart imagnumber: (floatnumber | digitpart) ("j" | "J") Constructors ------------ Following the same rules for placement, underscores will be allowed in the following constructors: - ``int()`` (with any base) - ``float()`` - ``complex()`` - ``Decimal()`` Prior Art ========= Those languages that do allow underscore grouping implement a large variety of rules for allowed placement of underscores. In cases where the language spec contradicts the actual behavior, the actual behavior is listed. ("single" or "multiple" refer to allowing runs of consecutive underscores.) * Ada: single, only between digits [8]_ * C# (open proposal for 7.0): multiple, only between digits [6]_ * C++14: single, between digits (different separator chosen) [1]_ * D: multiple, anywhere, including trailing [2]_ * Java: multiple, only between digits [7]_ * Julia: single, only between digits (but not in float exponent parts) [9]_ * Perl 5: multiple, basically anywhere, although docs say it's restricted to one underscore between digits [3]_ * Ruby: single, only between digits (although docs say "anywhere") [10]_ * Rust: multiple, anywhere, except for between exponent "e" and digits [4]_ * Swift: multiple, between digits and trailing (although textual description says only "between digits") [5]_ Alternative Syntax ================== Underscore Placement Rules -------------------------- Instead of the relatively strict rule specified above, the use of underscores could be limited. As we seen from other languages, common rules include: * Only one consecutive underscore allowed, and only between digits. * Multiple consecutive underscores allowed, but only between digits. * Multiple consecutive underscores allowed, in most positions except for the start of the literal, or special positions like after a decimal point. The syntax in this PEP has ultimately been selected because it covers the common use cases, and does not allow for syntax that would have to be discouraged in style guides anyway. A less common rule would be to allow underscores only every N digits (where N could be 3 for decimal literals, or 4 for hexadecimal ones). This is unnecessarily restrictive, especially considering the separator placement is different in different cultures. Different Separators -------------------- A proposed alternate syntax was to use whitespace for grouping. Although strings are a precedent for combining adjoining literals, the behavior can lead to unexpected effects which are not possible with underscores. Also, no other language is known to use this rule, except for languages that generally disregard any whitespace. C++14 introduces apostrophes for grouping (because underscores introduce ambiguity with user-defined literals), which is not considered because of the use in Python's string literals. [1]_ Open Proposals ============== It has been proposed [11]_ to extend the number-to-string formatting language to allow ``_`` as a thousans separator, where currently only ``,`` is supported. This could be used to easily generate code with more readable literals. Implementation ============== A preliminary patch that implements the specification given above has been posted to the issue tracker. [12]_ References ========== .. [1] http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2013/n3499.html .. [2] http://dlang.org/spec/lex.html#integerliteral .. [3] http://perldoc.perl.org/perldata.html#Scalar-value-constructors .. [4] http://doc.rust-lang.org/reference.html#number-literals .. [5] https://developer.apple.com/library/ios/documentation/Swift/Conceptual/Swif… .. [6] https://github.com/dotnet/roslyn/issues/216 .. [7] https://docs.oracle.com/javase/7/docs/technotes/guides/language/underscores… .. [8] http://archive.adaic.com/standards/83lrm/html/lrm-02-04.html#2.4 .. [9] http://docs.julialang.org/en/release-0.4/manual/integers-and-floating-point… .. [10] http://ruby-doc.org/core-2.3.0/doc/syntax/literals_rdoc.html#label-Numbers .. [11] https://mail.python.org/pipermail/python-dev/2016-February/143283.html .. [12] http://bugs.python.org/issue26331 Copyright ========= This document has been placed in the public domain.

4 years, 9 months

Anyone want to lead the sprints at PyCon US 2016?

by Brett Cannon

The call has started to go out for sprint groups to list themselves online. Anyone want to specifically lead the core sprint this year? If no one specifically does then I will sign us up and do my usual thing of pointing people at the devguide and encourage people to ask questions but not do a lot of hand-holding (I'm expecting to be busy either working on GitHub migration stuff or doing other things that I have been neglecting due to my GitHub migration work). ---------- Forwarded message --------- From: Ewa Jodlowska <ewa(a)python.org> Date: Mon, 4 Apr 2016 at 07:14 Subject: [PSF-Community] Sprinting at PyCon US 2016 To: <psf-community(a)python.org> Are you coming to PyCon US? Have you thought about sprinting? The coding Sprints are the hidden gem of PyCon, up to 4 days (June 2-5) of coding with many Python projects and their maintainers. And if you're coming to PyCon, taking part in the Sprints is easy! You don’t need to change your registration* to join the Sprints. There’s no additional registration fee, and you even get lunch. You do need to cover the additional lodging and other meals, but that’s it. If you’ve booked a room through the PyCon registration system, you'll need to contact the registration team at pycon2016(a)cteusa.com as soon as possible to request the extra nights. The sprinting itself (along with lunch every day) is free, so your only expenses are your room and other meals. If you're interested in what projects will be sprinting, just keep an eye on the sprints page on the PyCon web site at https://us.pycon.org/2016/community/sprints/ Be sure to check back, as groups are being added all the time. If you haven't sprinted before, or if you just need to brush up on sprinting tools and techniques, there will again be an 'Intro to Sprinting' session the evening of June 1, lead by Shauna Gordon-McKeon and other members of Python community. To grab a free ticket for this session, just visit https://www.eventbrite.com/e/introduction-to-open-source-the-pycon-sprints-… . *Please note that conference registration is sold out, but you do not need a conference registration to come to the Sprints. _______________________________________________ PSF-Community mailing list PSF-Community(a)python.org https://mail.python.org/mailman/listinfo/psf-community

4 years, 9 months

Re: [Python-Dev] Convert int() to size_t in Python/C

by Marcos Dione

On Fri, Apr 29, 2016 at 12:18:46PM -0400, Random832 wrote: > On Fri, Apr 29, 2016, at 10:45, Marcos Dione wrote: > > One possible solution hat was suggested to me in the #python IRC > > channel was to use that, then test if the resulting value is negative, > > and adjust accordingly, but I wonder if there is a cleaner, more general > > solution (for instance, what if the type was something else, like loff_t, > > although for that one in particular there *is* a convertion > > function/macro). > > In principle, you could just use PyLong_AsUnsignedLong (or LongLong), > and raise OverflowError manually if the value happens to be out of > size_t's range. (99% sure that on every linux platform unsigned long is > the same size as size_t. > > But it's not like it'd be the first function in OS to call a system call > that takes a size_t. Read just uses Py_ssize_t. Write uses the buffer > protocol, which uses Py_ssize_t. How concerned are you really about the > lost range here? What does the system call return (its return type is > ssize_t) if it writes more than SSIZE_MAX bytes? (This shouldn't be hard > to test, just try copying a >2GB file on a 32-bit system) It's a very good point, but I don't have any 32 bits systems around with a kernel-4.5. I'll try to figure it out and/or ask in the kernel ML. > I'm more curious about what your calling convention is going to be for > off_in and off_out. I can't think of any other interfaces that have > optional output parameters. Python functions generally deal with output > parameters in the underlying C function (there are a few examples in > math) by returning a tuple. These are not output parameters, even if they're pointers. they'r using the NULL pointer to signal that the current offsets should not be touched, to differentiate from a offset of 0. Something that in Python we would use None.

4 years, 10 months

Problemas con modulos

by ruizriverafelipejavier＠yahoo.com.mx

Hola, Estoy intentando conectarme a twitter para recibir tweets, sin embargo algunos códigos que he bajado de internet, me indican que debo de instalar tweepy y matplotlib, lo hago y sigo recibiendo el mensaje de que no están instalados. tweepy no reporta problemas, lo invoco en la línea de comandos, todo bien, Igual con matplotlib requiere de varias dependencias (dateutils, numpy, tornado, etc, ya las instales) antes de su instalación, pero ya en el editor de Python, al ejecutar el código, me aparece el siguiente mensaje: ImportError: No module named 'matplotlib' alguna idea? Felipe

4 years, 10 months

Convert int() to size_t in Python/C

by Marcos Dione

First of all, I'm not subbscribed to the list (too much traffic for me), so please CC: me in any answers if possible. I'm trying to add a new syscall to the os module: https://bugs.python.org/issue26826 One of the few missing parts is to cenvert a parameter, which would be a Python int object using PyArg_ParseTupleAndKeywords() to a size_t variable. For something similar, the 'n' format exists, but that one converts to Py_ssize_t (which is ssize_t, really), but that one is signed. One possible solution hat was suggested to me in the #python IRC channel was to use that, then test if the resulting value is negative, and adjust accordingly, but I wonder if there is a cleaner, more general solution (for instance, what if the type was something else, like loff_t, although for that one in particular there *is* a convertion function/macro). -- (Not so) Random fortune: Premature optimization is the root of all evil. -- Donald Knuth

4 years, 10 months

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Python-Dev April 2016