Mailman 3 RFC: PEP: Add dict.__version__ - Python-ideas

8 Jan 2016

      Hi,

Here is a first PEP, part of a serie of 3 PEP to add an API to
implement a static Python optimizer specializing functions with
guards.

HTML version:
https://faster-cpython.readthedocs.org/pep_dict_version.html#pep-dict-versio...

PEP: xxx
Title: Add dict.__version__
Version: $Revision$
Last-Modified: $Date$
Author: Victor Stinner <victor.stinner@gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 4-January-2016
Python-Version: 3.6

Abstract
========

Add a new read-only ``__version__`` property to ``dict`` and
``collections.UserDict`` types, incremented at each change.

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
semantic 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 efficient
guards on namespaces.

Example of optimization: replace loading a global variable with a
constant.  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, instead of using the constant.

Guard example
=============

Pseudo-code of an efficient guard to check if a dictionary key was
modified (created, updated or deleted)::

    UNSET = object()

    class Guard:
        def __init__(self, dict, key):
            self.dict = dict
            self.key = key
            self.value = dict.get(key, UNSET)
            self.version = dict.__version__

        def check(self):
            """Return True if the dictionary value did not changed."""
            version = self.dict.__version__
            if version == self.version:
                # Fast-path: avoid the dictionary lookup
                return True

            value = self.dict.get(self.key, UNSET)
            if value == self.value:
                # another key was modified:
                # cache the new dictionary version
                self.version = version
                return True

            return False

Changes
=======

Add a read-only ``__version__`` property to builtin ``dict`` type and to
the ``collections.UserDict`` type. 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::

    >>> d = {}
    >>> d.__version__
    0
    >>> d['key'] = 'value'
    >>> d.__version__
    1
    >>> d['key'] = 'new value'
    >>> d.__version__
    2
    >>> del d['key']
    >>> d.__version__
    3

If a dictionary is created with items, the version is also incremented
at each dictionary insertion. Example::

    >>> d=dict(x=7, y=33)
    >>> d.__version__
    2

The version is not incremented is an existing key is modified to the
same value, but only the identifier of the value is tested, not the
content of the value. Example::

    >>> d={}
    >>> value = object()
    >>> d['key'] = value
    >>> d.__version__
    2
    >>> d['key'] = value
    >>> d.__version__
    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.

The PEP is designed to implement guards on namespaces, only the ``dict``
type can be used for namespaces in practice.  ``collections.UserDict``
is modified because it must mimicks ``dict``. ``collections.Mapping`` is
unchanged.

Integer overflow
================

The implementation uses the C unsigned integer type ``size_t`` to store
the version.  On 32-bit systems, the maximum version is ``2**32-1``
(more than ``4.2 * 10 ** 9``, 4 billions). On 64-bit systems, the maximum
version is ``2**64-1`` (more than ``1.8 * 10**19``).

The C code uses ``version++``. The behaviour on integer overflow of the
version is undefined. The minimum guarantee is that the version always
changes when the dictionary is modified.

The check ``dict.__version__ == old_version`` can be true after an
integer overflow, so a guard can return false even if the value changed,
which is wrong. The bug occurs if the dict is modified at least ``2**64``
times (on 64-bit system) between two checks of the guard.

Using a more complex type (ex: ``PyLongObject``) to avoid the overflow
would slow down operations on the ``dict`` type. Even if there is a
theorical risk of missing a value change, the risk is considered too low
compared to the slow down of using a more complex type.

Alternatives
============

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 rely on the entry
version and so avoid the strong reference to the value (only strong
references to a dictionary and key are needed).

Changes: add a ``getversion(key)`` method to dictionary which returns
``None`` if the key doesn't exist. When a key is created or modified,
the entry version is set to the dictionary version which is incremented
at each change (create, modify, delete).

Pseudo-code of an efficient guard to check if a dict key was modified
using ``getversion()``::

    UNSET = object()

    class Guard:
        def __init__(self, dict, key):
            self.dict = dict
            self.key = key
            self.dict_version = dict.__version__
            self.entry_version = dict.getversion(key)

        def check(self):
            """Return True if the dictionary value did not changed."""
            dict_version = self.dict.__version__
            if dict_version == self.version:
                # Fast-path: avoid the dictionary lookup
                return True

            # lookup in the dictionary, but get the entry version,
            #not the value
            entry_version = self.dict.getversion(self.key)
            if entry_version == self.entry_version:
                # another key was modified:
                # cache the new dictionary version
                self.dict_version = dict_version
                return True

            return False

This 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 if we use ``size_t``.

In Python, the memory footprint matters and the trend is more 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,
expect 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 may 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.

Usage of dict.__version__
=========================

astoptimizer of FAT Python
--------------------------

The astoptimizer of the FAT Python 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

The `FAT Python
<http://faster-cpython.readthedocs.org/fat_python.html>`_ project is a
static optimizer for Python 3.6.

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>`_.

Prior Art
=========

Cached globals+builtins lookup
------------------------------

In 2006, Andrea Griffini proposes a patch implementing a `Cached
globals+builtins lookup optimization <https://bugs.python.org/issue1616125>`_.
The patch adds a private ``timestamp`` field to dict.

See the thread on python-dev: `About dictionary lookup caching
<https://mail.python.org/pipermail/python-dev/2006-December/070348.html>`_.

Globals / builtins cache
------------------------

In 2010, Antoine Pitrou proposed a `Globals / builtins cache
<http://bugs.python.org/issue10401>`_ which adds a private
``ma_version`` field to the ``dict`` type. The patch adds a "global and
builtin cache" to functions and frames, and changes ``LOAD_GLOBAL`` and
``STORE_GLOBAL`` instructions to use the cache.

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.

Copyright
=========

This document has been placed in the public domain.

--
Victor

RFC: PEP: Add dict.__version__

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RFC: PEP: Add dict.version