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reSTify PEP 266 (#265)

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@ -5,450 +5,440 @@ Last-Modified: $Date$
Author: skip@pobox.com (Skip Montanaro)
Status: Withdrawn
Type: Standards Track
Content-Type: text/x-rst
Created: 13-Aug-2001
Python-Version: 2.3
Post-History:
Abstract
========
The bindings for most global variables and attributes of other
modules typically never change during the execution of a Python
program, but because of Python's dynamic nature, code which
accesses such global objects must run through a full lookup each
time the object is needed. This PEP proposes a mechanism that
allows code that accesses most global objects to treat them as
local objects and places the burden of updating references on the
code that changes the name bindings of such objects.
The bindings for most global variables and attributes of other modules
typically never change during the execution of a Python program, but because
of Python's dynamic nature, code which accesses such global objects must run
through a full lookup each time the object is needed. This PEP proposes a
mechanism that allows code that accesses most global objects to treat them as
local objects and places the burden of updating references on the code that
changes the name bindings of such objects.
Introduction
============
Consider the workhorse function sre_compile._compile. It is the
internal compilation function for the sre module. It consists
almost entirely of a loop over the elements of the pattern being
compiled, comparing opcodes with known constant values and
appending tokens to an output list. Most of the comparisons are
with constants imported from the sre_constants module. This means
there are lots of LOAD_GLOBAL bytecodes in the compiled output of
this module. Just by reading the code it's apparent that the
author intended LITERAL, NOT_LITERAL, OPCODES and many other
symbols to be constants. Still, each time they are involved in an
expression, they must be looked up anew.
Consider the workhorse function ``sre_compile._compile``. It is the internal
compilation function for the ``sre`` module. It consists almost entirely of a
loop over the elements of the pattern being compiled, comparing opcodes with
known constant values and appending tokens to an output list. Most of the
comparisons are with constants imported from the ``sre_constants`` module.
This means there are lots of ``LOAD_GLOBAL`` bytecodes in the compiled output
of this module. Just by reading the code it's apparent that the author
intended ``LITERAL``, ``NOT_LITERAL``, ``OPCODES`` and many other symbols to
be constants. Still, each time they are involved in an expression, they must
be looked up anew.
Most global accesses are actually to objects that are "almost
constants". This includes global variables in the current module
as well as the attributes of other imported modules. Since they
rarely change, it seems reasonable to place the burden of updating
references to such objects on the code that changes the name
bindings. If sre_constants.LITERAL is changed to refer to another
object, perhaps it would be worthwhile for the code that modifies
the sre_constants module dict to correct any active references to
that object. By doing so, in many cases global variables and the
attributes of many objects could be cached as local variables. If
the bindings between the names given to the objects and the
objects themselves changes rarely, the cost of keeping track of
such objects should be low and the potential payoff fairly large.
Most global accesses are actually to objects that are "almost constants".
This includes global variables in the current module as well as the attributes
of other imported modules. Since they rarely change, it seems reasonable to
place the burden of updating references to such objects on the code that
changes the name bindings. If ``sre_constants.LITERAL`` is changed to refer
to another object, perhaps it would be worthwhile for the code that modifies
the ``sre_constants`` module dict to correct any active references to that
object. By doing so, in many cases global variables and the attributes of
many objects could be cached as local variables. If the bindings between the
names given to the objects and the objects themselves changes rarely, the cost
of keeping track of such objects should be low and the potential payoff fairly
large.
In an attempt to gauge the effect of this proposal, I modified the Pystone
benchmark program included in the Python distribution to cache global
functions. Its main function, ``Proc0``, makes calls to ten different
functions inside its ``for`` loop. In addition, ``Func2`` calls ``Func1``
repeatedly inside a loop. If local copies of these 11 global idenfiers are
made before the functions' loops are entered, performance on this particular
benchmark improves by about two percent (from 5561 pystones to 5685 on my
laptop). It gives some indication that performance would be improved by
caching most global variable access. Note also that the pystone benchmark
makes essentially no accesses of global module attributes, an anticipated area
of improvement for this PEP.
In an attempt to gauge the effect of this proposal, I modified the
Pystone benchmark program included in the Python distribution to
cache global functions. Its main function, Proc0, makes calls to
ten different functions inside its for loop. In addition, Func2
calls Func1 repeatedly inside a loop. If local copies of these 11
global idenfiers are made before the functions' loops are entered,
performance on this particular benchmark improves by about two per
cent (from 5561 pystones to 5685 on my laptop). It gives some
indication that performance would be improved by caching most
global variable access. Note also that the pystone benchmark
makes essentially no accesses of global module attributes, an
anticipated area of improvement for this PEP.
Proposed Change
===============
I propose that the Python virtual machine be modified to include
TRACK_OBJECT and UNTRACK_OBJECT opcodes. TRACK_OBJECT would
associate a global name or attribute of a global name with a slot
in the local variable array and perform an initial lookup of the
associated object to fill in the slot with a valid value. The
association it creates would be noted by the code responsible for
changing the name-to-object binding to cause the associated local
variable to be updated. The UNTRACK_OBJECT opcode would delete
any association between the name and the local variable slot.
I propose that the Python virtual machine be modified to include
``TRACK_OBJECT`` and ``UNTRACK_OBJECT`` opcodes. ``TRACK_OBJECT`` would
associate a global name or attribute of a global name with a slot in the local
variable array and perform an initial lookup of the associated object to fill
in the slot with a valid value. The association it creates would be noted by
the code responsible for changing the name-to-object binding to cause the
associated local variable to be updated. The ``UNTRACK_OBJECT`` opcode would
delete any association between the name and the local variable slot.
Threads
=======
Operation of this code in threaded programs will be no different
than in unthreaded programs. If you need to lock an object to
access it, you would have had to do that before TRACK_OBJECT would
have been executed and retain that lock until after you stop using
it.
Operation of this code in threaded programs will be no different than in
unthreaded programs. If you need to lock an object to access it, you would
have had to do that before ``TRACK_OBJECT`` would have been executed and
retain that lock until after you stop using it.
FIXME: I suspect I need more here.
FIXME: I suspect I need more here.
Rationale
=========
Global variables and attributes rarely change. For example, once
a function imports the math module, the binding between the name
"math" and the module it refers to aren't likely to change.
Similarly, if the function that uses the math module refers to its
"sin" attribute, it's unlikely to change. Still, every time the
module wants to call the math.sin function, it must first execute
a pair of instructions:
Global variables and attributes rarely change. For example, once a function
imports the math module, the binding between the name *math* and the
module it refers to aren't likely to change. Similarly, if the function that
uses the ``math`` module refers to its *sin* attribute, it's unlikely to
change. Still, every time the module wants to call the ``math.sin`` function,
it must first execute a pair of instructions::
LOAD_GLOBAL math
LOAD_ATTR sin
LOAD_GLOBAL math
LOAD_ATTR sin
If the client module always assumed that math.sin was a local
constant and it was the responsibility of "external forces"
outside the function to keep the reference correct, we might have
code like this:
If the client module always assumed that ``math.sin`` was a local constant and
it was the responsibility of "external forces" outside the function to keep
the reference correct, we might have code like this::
TRACK_OBJECT math.sin
...
LOAD_FAST math.sin
...
UNTRACK_OBJECT math.sin
TRACK_OBJECT math.sin
...
LOAD_FAST math.sin
...
UNTRACK_OBJECT math.sin
If the LOAD_FAST was in a loop the payoff in reduced global loads
and attribute lookups could be significant.
If the ``LOAD_FAST`` was in a loop the payoff in reduced global loads and
attribute lookups could be significant.
This technique could, in theory, be applied to any global variable
access or attribute lookup. Consider this code:
This technique could, in theory, be applied to any global variable access or
attribute lookup. Consider this code::
l = []
for i in range(10):
l.append(math.sin(i))
return l
l = []
for i in range(10):
l.append(math.sin(i))
return l
Even though l is a local variable, you still pay the cost of
loading l.append ten times in the loop. The compiler (or an
optimizer) could recognize that both math.sin and l.append are
being called in the loop and decide to generate the tracked local
code, avoiding it for the builtin range() function because it's
only called once during loop setup. Performance issues related to
accessing local variables make tracking l.append less attractive
than tracking globals such as math.sin.
Even though *l* is a local variable, you still pay the cost of loading
``l.append`` ten times in the loop. The compiler (or an optimizer) could
recognize that both ``math.sin`` and ``l.append`` are being called in the loop
and decide to generate the tracked local code, avoiding it for the builtin
``range()`` function because it's only called once during loop setup.
Performance issues related to accessing local variables make tracking
``l.append`` less attractive than tracking globals such as ``math.sin``.
According to a post to python-dev by Marc-Andre Lemburg [1],
LOAD_GLOBAL opcodes account for over 7% of all instructions
executed by the Python virtual machine. This can be a very
expensive instruction, at least relative to a LOAD_FAST
instruction, which is a simple array index and requires no extra
function calls by the virtual machine. I believe many LOAD_GLOBAL
instructions and LOAD_GLOBAL/LOAD_ATTR pairs could be converted to
LOAD_FAST instructions.
According to a post to python-dev by Marc-Andre Lemburg [1]_, ``LOAD_GLOBAL``
opcodes account for over 7% of all instructions executed by the Python virtual
machine. This can be a very expensive instruction, at least relative to a
``LOAD_FAST`` instruction, which is a simple array index and requires no extra
function calls by the virtual machine. I believe many ``LOAD_GLOBAL``
instructions and ``LOAD_GLOBAL/LOAD_ATTR`` pairs could be converted to
``LOAD_FAST`` instructions.
Code that uses global variables heavily often resorts to various
tricks to avoid global variable and attribute lookup. The
aforementioned sre_compile._compile function caches the append
method of the growing output list. Many people commonly abuse
functions' default argument feature to cache global variable
lookups. Both of these schemes are hackish and rarely address all
the available opportunities for optimization. (For example,
sre_compile._compile does not cache the two globals that it uses
most frequently: the builtin len function and the global OPCODES
array that it imports from sre_constants.py.
Code that uses global variables heavily often resorts to various tricks to
avoid global variable and attribute lookup. The aforementioned
``sre_compile._compile`` function caches the ``append`` method of the growing
output list. Many people commonly abuse functions' default argument feature
to cache global variable lookups. Both of these schemes are hackish and
rarely address all the available opportunities for optimization. (For
example, ``sre_compile._compile`` does not cache the two globals that it uses
most frequently: the builtin ``len`` function and the global ``OPCODES`` array
that it imports from ``sre_constants.py``.
Questions
=========
Q. What about threads? What if math.sin changes while in cache?
What about threads? What if ``math.sin`` changes while in cache?
-----------------------------------------------------------------
A. I believe the global interpreter lock will protect values from
being corrupted. In any case, the situation would be no worse
than it is today. If one thread modified math.sin after another
thread had already executed "LOAD_GLOBAL math", but before it
executed "LOAD_ATTR sin", the client thread would see the old
value of math.sin.
I believe the global interpreter lock will protect values from being
corrupted. In any case, the situation would be no worse than it is today.
If one thread modified ``math.sin`` after another thread had already executed
``LOAD_GLOBAL math``, but before it executed ``LOAD_ATTR sin``, the client
thread would see the old value of ``math.sin``.
The idea is this. I use a multi-attribute load below as an
example, not because it would happen very often, but because by
demonstrating the recursive nature with an extra call hopefully
it will become clearer what I have in mind. Suppose a function
defined in module foo wants to access spam.eggs.ham and that
spam is a module imported at the module level in foo:
The idea is this. I use a multi-attribute load below as an example, not
because it would happen very often, but because by demonstrating the recursive
nature with an extra call hopefully it will become clearer what I have in
mind. Suppose a function defined in module ``foo`` wants to access
``spam.eggs.ham`` and that ``spam`` is a module imported at the module level
in ``foo``::
import spam
...
def somefunc():
...
x = spam.eggs.ham
import spam
...
def somefunc():
...
x = spam.eggs.ham
Upon entry to somefunc, a TRACK_GLOBAL instruction will be
executed:
Upon entry to ``somefunc``, a ``TRACK_GLOBAL`` instruction will be executed::
TRACK_GLOBAL spam.eggs.ham n
TRACK_GLOBAL spam.eggs.ham n
"spam.eggs.ham" is a string literal stored in the function's
constants array. "n" is a fastlocals index. "&fastlocals[n]"
is a reference to slot "n" in the executing frame's fastlocals
array, the location in which the spam.eggs.ham reference will
be stored. Here's what I envision happening:
*spam.eggs.ham* is a string literal stored in the function's constants
array. *n* is a fastlocals index. ``&fastlocals[n]`` is a reference to
slot *n* in the executing frame's ``fastlocals`` array, the location in
which the *spam.eggs.ham* reference will be stored. Here's what I envision
happening:
1. The TRACK_GLOBAL instruction locates the object referred to
by the name "spam" and finds it in its module scope. It
then executes a C function like
1. The ``TRACK_GLOBAL`` instruction locates the object referred to by the name
*spam* and finds it in its module scope. It then executes a C function
like::
_PyObject_TrackName(m, "spam.eggs.ham", &fastlocals[n])
_PyObject_TrackName(m, "spam.eggs.ham", &fastlocals[n])
where "m" is the module object with an attribute "spam".
where ``m`` is the module object with an attribute ``spam``.
2. The module object strips the leading "spam." stores the
necessary information ("eggs.ham" and &fastlocals[n]) in
case its binding for the name "eggs" changes. It then
locates the object referred to by the key "eggs" in its
dict and recursively calls
2. The module object strips the leading *spam.* and stores the necessary
information (*eggs.ham* and ``&fastlocals[n]``) in case its binding for the
name *eggs* changes. It then locates the object referred to by the key
*eggs* in its dict and recursively calls::
_PyObject_TrackName(eggs, "eggs.ham", &fastlocals[n])
_PyObject_TrackName(eggs, "eggs.ham", &fastlocals[n])
3. The eggs object strips the leading "eggs.", stores the
("ham", &fastlocals[n]) info, locates the object in its
namespace called "ham" and calls _PyObject_TrackName once
again:
3. The ``eggs`` object strips the leading *eggs.*, stores the
(*ham*, &fastlocals[n]) info, locates the object in its namespace called
``ham`` and calls ``_PyObject_TrackName`` once again::
_PyObject_TrackName(ham, "ham", &fastlocals[n])
_PyObject_TrackName(ham, "ham", &fastlocals[n])
4. The "ham" object strips the leading string (no "." this
time, but that's a minor point), sees that the result is
empty, then uses its own value (self, probably) to update
the location it was handed:
4. The ``ham`` object strips the leading string (no "." this time, but that's
a minor point), sees that the result is empty, then uses its own value
(``self``, probably) to update the location it was handed::
Py_XDECREF(&fastlocals[n]);
&fastlocals[n] = self;
Py_INCREF(&fastlocals[n]);
Py_XDECREF(&fastlocals[n]);
&fastlocals[n] = self;
Py_INCREF(&fastlocals[n]);
At this point, each object involved in resolving
"spam.eggs.ham" knows which entry in its namespace needs to be
tracked and what location to update if that name changes.
Furthermore, if the one name it is tracking in its local
storage changes, it can call _PyObject_TrackName using the new
object once the change has been made. At the bottom end of
the food chain, the last object will always strip a name, see
the empty string and know that its value should be stuffed
into the location it's been passed.
At this point, each object involved in resolving ``spam.eggs.ham``
knows which entry in its namespace needs to be tracked and what location
to update if that name changes. Furthermore, if the one name it is
tracking in its local storage changes, it can call ``_PyObject_TrackName``
using the new object once the change has been made. At the bottom end of
the food chain, the last object will always strip a name, see the empty
string and know that its value should be stuffed into the location it's
been passed.
When the object referred to by the dotted expression
"spam.eggs.ham" is going to go out of scope, an
"UNTRACK_GLOBAL spam.eggs.ham n" instruction is executed. It
has the effect of deleting all the tracking information that
TRACK_GLOBAL established.
When the object referred to by the dotted expression ``spam.eggs.ham``
is going to go out of scope, an ``UNTRACK_GLOBAL spam.eggs.ham n``
instruction is executed. It has the effect of deleting all the tracking
information that ``TRACK_GLOBAL`` established.
The tracking operation may seem expensive, but recall that the
objects being tracked are assumed to be "almost constant", so
the setup cost will be traded off against hopefully multiple
local instead of global loads. For globals with attributes
the tracking setup cost grows but is offset by avoiding the
extra LOAD_ATTR cost. The TRACK_GLOBAL instruction needs to
perform a PyDict_GetItemString for the first name in the chain
to determine where the top-level object resides. Each object
in the chain has to store a string and an address somewhere,
probably in a dict that uses storage locations as keys
(e.g. the &fastlocals[n]) and strings as values. (This dict
could possibly be a central dict of dicts whose keys are
object addresses instead of a per-object dict.) It shouldn't
be the other way around because multiple active frames may
want to track "spam.eggs.ham", but only one frame will want to
associate that name with one of its fast locals slots.
The tracking operation may seem expensive, but recall that the objects
being tracked are assumed to be "almost constant", so the setup cost will
be traded off against hopefully multiple local instead of global loads.
For globals with attributes the tracking setup cost grows but is offset by
avoiding the extra ``LOAD_ATTR`` cost. The ``TRACK_GLOBAL`` instruction
needs to perform a ``PyDict_GetItemString`` for the first name in the chain
to determine where the top-level object resides. Each object in the chain
has to store a string and an address somewhere, probably in a dict that
uses storage locations as keys (e.g. the ``&fastlocals[n]``) and strings as
values. (This dict could possibly be a central dict of dicts whose keys
are object addresses instead of a per-object dict.) It shouldn't be the
other way around because multiple active frames may want to track
``spam.eggs.ham``, but only one frame will want to associate that name with
one of its fast locals slots.
Unresolved Issues
=================
Threading -
Threading
---------
What about this (dumb) code?
What about this (dumb) code?::
l = []
lock = threading.Lock()
...
def fill_l():
for i in range(1000):
lock.acquire()
l.append(math.sin(i))
lock.release()
...
def consume_l():
while 1:
lock.acquire()
if l:
elt = l.pop()
lock.release()
fiddle(elt)
l = []
lock = threading.Lock()
...
def fill_l()::
for i in range(1000)::
lock.acquire()
l.append(math.sin(i))
lock.release()
...
def consume_l()::
while 1::
lock.acquire()
if l::
elt = l.pop()
lock.release()
fiddle(elt)
It's not clear from a static analysis of the code what the lock is
protecting. (You can't tell at compile-time that threads are even
involved can you?) Would or should it affect attempts to track
"l.append" or "math.sin" in the fill_l function?
It's not clear from a static analysis of the code what the lock is protecting.
(You can't tell at compile-time that threads are even involved can you?)
Would or should it affect attempts to track ``l.append`` or ``math.sin`` in
the ``fill_l`` function?
If we annotate the code with mythical track_object and untrack_object
builtins (I'm not proposing this, just illustrating where stuff would
go!), we get
If we annotate the code with mythical ``track_object`` and ``untrack_object``
builtins (I'm not proposing this, just illustrating where stuff would go!), we
get::
l = []
lock = threading.Lock()
...
def fill_l():
track_object("l.append", append)
track_object("math.sin", sin)
for i in range(1000):
lock.acquire()
append(sin(i))
lock.release()
untrack_object("math.sin", sin)
untrack_object("l.append", append)
...
def consume_l():
while 1:
lock.acquire()
if l:
elt = l.pop()
lock.release()
fiddle(elt)
l = []
lock = threading.Lock()
...
def fill_l()::
track_object("l.append", append)
track_object("math.sin", sin)
for i in range(1000)::
lock.acquire()
append(sin(i))
lock.release()
untrack_object("math.sin", sin)
untrack_object("l.append", append)
...
def consume_l()::
while 1::
lock.acquire()
if l::
elt = l.pop()
lock.release()
fiddle(elt)
Is that correct both with and without threads (or at least equally
incorrect with and without threads)?
Is that correct both with and without threads (or at least equally incorrect
with and without threads)?
Nested Scopes -
Nested Scopes
-------------
The presence of nested scopes will affect where TRACK_GLOBAL finds
a global variable, but shouldn't affect anything after that. (I
think.)
The presence of nested scopes will affect where ``TRACK_GLOBAL`` finds a
global variable, but shouldn't affect anything after that. (I think.)
Missing Attributes -
Missing Attributes
------------------
Suppose I am tracking the object referred to by "spam.eggs.ham"
and "spam.eggs" is rebound to an object that does not have a "ham"
attribute. It's clear this will be an AttributeError if the
programmer attempts to resolve "spam.eggs.ham" in the current
Python virtual machine, but suppose the programmer has anticipated
this case:
Suppose I am tracking the object referred to by ``spam.eggs.ham`` and
``spam.eggs`` is rebound to an object that does not have a ``ham`` attribute.
It's clear this will be an ``AttributeError`` if the programmer attempts to
resolve ``spam.eggs.ham`` in the current Python virtual machine, but suppose
the programmer has anticipated this case::
if hasattr(spam.eggs, "ham"):
print spam.eggs.ham
elif hasattr(spam.eggs, "bacon"):
print spam.eggs.bacon
else:
print "what? no meat?"
if hasattr(spam.eggs, "ham"):
print spam.eggs.ham
elif hasattr(spam.eggs, "bacon"):
print spam.eggs.bacon
else:
print "what? no meat?"
You can't raise an AttributeError when the tracking information is
recalculated. If it does not raise AttributeError and instead
lets the tracking stand, it may be setting the programmer up for a
very subtle error.
You can't raise an ``AttributeError`` when the tracking information is
recalculated. If it does not raise ``AttributeError`` and instead lets the
tracking stand, it may be setting the programmer up for a very subtle error.
One solution to this problem would be to track the shortest
possible root of each dotted expression the function refers to
directly. In the above example, "spam.eggs" would be tracked, but
"spam.eggs.ham" and "spam.eggs.bacon" would not.
One solution to this problem would be to track the shortest possible root of
each dotted expression the function refers to directly. In the above example,
``spam.eggs`` would be tracked, but ``spam.eggs.ham`` and ``spam.eggs.bacon``
would not.
Who does the dirty work? -
Who does the dirty work?
------------------------
In the Questions section I postulated the existence of a
_PyObject_TrackName function. While the API is fairly easy to
specify, the implementation behind-the-scenes is not so obvious.
A central dictionary could be used to track the name/location
mappings, but it appears that all setattr functions might need to
be modified to accommodate this new functionality.
In the Questions section I postulated the existence of a
``_PyObject_TrackName`` function. While the API is fairly easy to specify,
the implementation behind-the-scenes is not so obvious. A central dictionary
could be used to track the name/location mappings, but it appears that all
``setattr`` functions might need to be modified to accommodate this new
functionality.
If all types used the PyObject_GenericSetAttr function to set
attributes that would localize the update code somewhat. They
don't however (which is not too surprising), so it seems that all
getattrfunc and getattrofunc functions will have to be updated.
In addition, this would place an absolute requirement on C
extension module authors to call some function when an attribute
changes value (PyObject_TrackUpdate?).
If all types used the ``PyObject_GenericSetAttr`` function to set attributes
that would localize the update code somewhat. They don't however (which is
not too surprising), so it seems that all ``getattrfunc`` and ``getattrofunc``
functions will have to be updated. In addition, this would place an absolute
requirement on C extension module authors to call some function when an
attribute changes value (``PyObject_TrackUpdate``?).
Finally, it's quite possible that some attributes will be set by side effect
and not by any direct call to a ``setattr`` method of some sort. Consider a
device interface module that has an interrupt routine that copies the contents
of a device register into a slot in the object's ``struct`` whenever it
changes. In these situations, more extensive modifications would have to be
made by the module author. To identify such situations at compile time would
be impossible. I think an extra slot could be added to ``PyTypeObjects`` to
indicate if an object's code is safe for global tracking. It would have a
default value of 0 (``Py_TRACKING_NOT_SAFE``). If an extension module author
has implemented the necessary tracking support, that field could be
initialized to 1 (``Py_TRACKING_SAFE``). ``_PyObject_TrackName`` could check
that field and issue a warning if it is asked to track an object that the
author has not explicitly said was safe for tracking.
Finally, it's quite possible that some attributes will be set by
side effect and not by any direct call to a setattr method of some
sort. Consider a device interface module that has an interrupt
routine that copies the contents of a device register into a slot
in the object's struct whenever it changes. In these situations,
more extensive modifications would have to be made by the module
author. To identify such situations at compile time would be
impossible. I think an extra slot could be added to PyTypeObjects
to indicate if an object's code is safe for global tracking. It
would have a default value of 0 (Py_TRACKING_NOT_SAFE). If an
extension module author has implemented the necessary tracking
support, that field could be initialized to 1 (Py_TRACKING_SAFE).
_PyObject_TrackName could check that field and issue a warning if
it is asked to track an object that the author has not explicitly
said was safe for tracking.
Discussion
==========
Jeremy Hylton has an alternate proposal on the table [2]. His
proposal seeks to create a hybrid dictionary/list object for use
in global name lookups that would make global variable access look
more like local variable access. While there is no C code
available to examine, the Python implementation given in his
proposal still appears to require dictionary key lookup. It
doesn't appear that his proposal could speed local variable
attribute lookup, which might be worthwhile in some situations if
potential performance burdens could be addressed.
Jeremy Hylton has an alternate proposal on the table [2]_. His proposal seeks
to create a hybrid dictionary/list object for use in global name lookups that
would make global variable access look more like local variable access. While
there is no C code available to examine, the Python implementation given in
his proposal still appears to require dictionary key lookup. It doesn't
appear that his proposal could speed local variable attribute lookup, which
might be worthwhile in some situations if potential performance burdens could
be addressed.
Backwards Compatibility
=======================
I don't believe there will be any serious issues of backward
compatibility. Obviously, Python bytecode that contains
TRACK_OBJECT opcodes could not be executed by earlier versions of
the interpreter, but breakage at the bytecode level is often
assumed between versions.
I don't believe there will be any serious issues of backward compatibility.
Obviously, Python bytecode that contains ``TRACK_OBJECT`` opcodes could not be
executed by earlier versions of the interpreter, but breakage at the bytecode
level is often assumed between versions.
Implementation
==============
TBD. This is where I need help. I believe there should be either
a central name/location registry or the code that modifies object
attributes should be modified, but I'm not sure the best way to go
about this. If you look at the code that implements the
STORE_GLOBAL and STORE_ATTR opcodes, it seems likely that some
changes will be required to PyDict_SetItem and PyObject_SetAttr or
their String variants. Ideally, there'd be a fairly central place
to localize these changes. If you begin considering tracking
attributes of local variables you get into issues of modifying
STORE_FAST as well, which could be a problem, since the name
bindings for local variables are changed much more frequently. (I
think an optimizer could avoid inserting the tracking code for the
attributes for any local variables where the variable's name
binding changes.)
TBD. This is where I need help. I believe there should be either a central
name/location registry or the code that modifies object attributes should be
modified, but I'm not sure the best way to go about this. If you look at the
code that implements the ``STORE_GLOBAL`` and ``STORE_ATTR`` opcodes, it seems
likely that some changes will be required to ``PyDict_SetItem`` and
``PyObject_SetAttr`` or their String variants. Ideally, there'd be a fairly
central place to localize these changes. If you begin considering tracking
attributes of local variables you get into issues of modifying ``STORE_FAST``
as well, which could be a problem, since the name bindings for local variables
are changed much more frequently. (I think an optimizer could avoid inserting
the tracking code for the attributes for any local variables where the
variable's name binding changes.)
Performance
===========
I believe (though I have no code to prove it at this point), that
implementing TRACK_OBJECT will generally not be much more
expensive than a single LOAD_GLOBAL instruction or a
LOAD_GLOBAL/LOAD_ATTR pair. An optimizer should be able to avoid
converting LOAD_GLOBAL and LOAD_GLOBAL/LOAD_ATTR to the new scheme
unless the object access occurred within a loop. Further down the
line, a register-oriented replacement for the current Python
virtual machine [3] could conceivably eliminate most of the
LOAD_FAST instructions as well.
I believe (though I have no code to prove it at this point), that implementing
``TRACK_OBJECT`` will generally not be much more expensive than a single
``LOAD_GLOBAL`` instruction or a ``LOAD_GLOBAL``/``LOAD_ATTR`` pair. An
optimizer should be able to avoid converting ``LOAD_GLOBAL`` and
``LOAD_GLOBAL``/``LOAD_ATTR`` to the new scheme unless the object access
occurred within a loop. Further down the line, a register-oriented
replacement for the current Python virtual machine [3]_ could conceivably
eliminate most of the ``LOAD_FAST`` instructions as well.
The number of tracked objects should be relatively small. All
active frames of all active threads could conceivably be tracking
objects, but this seems small compared to the number of functions
defined in a given application.
The number of tracked objects should be relatively small. All active frames
of all active threads could conceivably be tracking objects, but this seems
small compared to the number of functions defined in a given application.
References
==========
[1] http://mail.python.org/pipermail/python-dev/2000-July/007609.html
.. [1] http://mail.python.org/pipermail/python-dev/2000-July/007609.html
[2] http://www.zope.org/Members/jeremy/CurrentAndFutureProjects/FastGlobalsPEP
.. [2] http://www.zope.org/Members/jeremy/CurrentAndFutureProjects/FastGlobalsPEP
[3] http://www.musi-cal.com/~skip/python/rattlesnake20010813.tar.gz
.. [3] http://www.musi-cal.com/~skip/python/rattlesnake20010813.tar.gz
Copyright
=========
This document has been placed in the public domain.
This document has been placed in the public domain.
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