Python Enhancement Proposals

PEP 280 – Optimizing access to globals

PEP
280
Title
Optimizing access to globals
Author
guido at python.org (Guido van Rossum)
Status
Deferred
Type
Standards Track
Created
10-Feb-2002
Python-Version
2.3
Post-History

Contents

Deferral

While this PEP is a nice idea, no-one has yet emerged to do the work of hashing out the differences between this PEP, PEP 266 and PEP 267. Hence, it is being deferred.

Abstract

This PEP describes yet another approach to optimizing access to module globals, providing an alternative to PEP 266 (Optimizing Global Variable/Attribute Access by Skip Montanaro) and PEP 267 (Optimized Access to Module Namespaces by Jeremy Hylton).

The expectation is that eventually one approach will be picked and implemented; possibly multiple approaches will be prototyped first.

Description

(Note: Jason Orendorff writes: “””I implemented this once, long ago, for Python 1.5-ish, I believe. I got it to the point where it was only 15% slower than ordinary Python, then abandoned it. ;) In my implementation, “cells” were real first-class objects, and “celldict” was a copy-and-hack version of dictionary. I forget how the rest worked.””” Reference: https://mail.python.org/pipermail/python-dev/2002-February/019876.html)

Let a cell be a really simple Python object, containing a pointer to a Python object and a pointer to a cell. Both pointers may be NULL. A Python implementation could be:

class cell(object):

    def __init__(self):
        self.objptr = NULL
        self.cellptr = NULL

The cellptr attribute is used for chaining cells together for searching built-ins; this will be explained later.

Let a celldict be a mapping from strings (the names of a module’s globals) to objects (the values of those globals), implemented using a dict of cells. A Python implementation could be:

class celldict(object):

    def __init__(self):
        self.__dict = {} # dict of cells

    def getcell(self, key):
        c = self.__dict.get(key)
        if c is None:
            c = cell()
            self.__dict[key] = c
        return c

    def cellkeys(self):
        return self.__dict.keys()

    def __getitem__(self, key):
        c = self.__dict.get(key)
        if c is None:
            raise KeyError, key
        value = c.objptr
        if value is NULL:
            raise KeyError, key
        else:
            return value

    def __setitem__(self, key, value):
        c = self.__dict.get(key)
        if c is None:
            c = cell()
            self.__dict[key] = c
        c.objptr = value

    def __delitem__(self, key):
        c = self.__dict.get(key)
        if c is None or c.objptr is NULL:
            raise KeyError, key
        c.objptr = NULL

    def keys(self):
        return [k for k, c in self.__dict.iteritems()
                if c.objptr is not NULL]

    def items(self):
        return [k, c.objptr for k, c in self.__dict.iteritems()
                if c.objptr is not NULL]

    def values(self):
        preturn [c.objptr for c in self.__dict.itervalues()
                if c.objptr is not NULL]

    def clear(self):
        for c in self.__dict.values():
            c.objptr = NULL

    # Etc.

It is possible that a cell exists corresponding to a given key, but the cell’s objptr is NULL; let’s call such a cell empty. When the celldict is used as a mapping, it is as if empty cells don’t exist. However, once added, a cell is never deleted from a celldict, and it is possible to get at empty cells using the getcell() method.

The celldict implementation never uses the cellptr attribute of cells.

We change the module implementation to use a celldict for its __dict__. The module’s getattr, setattr and delattr operations now map to getitem, setitem and delitem on the celldict. The type of <module>.__dict__ and globals() is probably the only backwards incompatibility.

When a module is initialized, its __builtins__ is initialized from the __builtin__ module’s __dict__, which is itself a celldict. For each cell in __builtins__, the new module’s __dict__ adds a cell with a NULL objptr, whose cellptr points to the corresponding cell of __builtins__. Python pseudo-code (ignoring rexec):

import __builtin__

class module(object):

    def __init__(self):
        self.__dict__ = d = celldict()
        d['__builtins__'] = bd = __builtin__.__dict__
        for k in bd.cellkeys():
            c = self.__dict__.getcell(k)
            c.cellptr = bd.getcell(k)

    def __getattr__(self, k):
        try:
            return self.__dict__[k]
        except KeyError:
            raise IndexError, k

    def __setattr__(self, k, v):
        self.__dict__[k] = v

    def __delattr__(self, k):
        del self.__dict__[k]

The compiler generates LOAD_GLOBAL_CELL <i> (and STORE_GLOBAL_CELL <i> etc.) opcodes for references to globals, where <i> is a small index with meaning only within one code object like the const index in LOAD_CONST. The code object has a new tuple, co_globals, giving the names of the globals referenced by the code indexed by <i>. No new analysis is required to be able to do this.

When a function object is created from a code object and a celldict, the function object creates an array of cell pointers by asking the celldict for cells corresponding to the names in the code object’s co_globals. If the celldict doesn’t already have a cell for a particular name, it creates and an empty one. This array of cell pointers is stored on the function object as func_cells. When a function object is created from a regular dict instead of a celldict, func_cells is a NULL pointer.

When the VM executes a LOAD_GLOBAL_CELL <i> instruction, it gets cell number <i> from func_cells. It then looks in the cell’s PyObject pointer, and if not NULL, that’s the global value. If it is NULL, it follows the cell’s cell pointer to the next cell, if it is not NULL, and looks in the PyObject pointer in that cell. If that’s also NULL, or if there is no second cell, NameError is raised. (It could follow the chain of cell pointers until a NULL cell pointer is found; but I have no use for this.) Similar for STORE_GLOBAL_CELL <i>, except it doesn’t follow the cell pointer chain – it always stores in the first cell.

There are fallbacks in the VM for the case where the function’s globals aren’t a celldict, and hence func_cells is NULL. In that case, the code object’s co_globals is indexed with <i> to find the name of the corresponding global and this name is used to index the function’s globals dict.

Additional Ideas

  • Never make func_cell a NULL pointer; instead, make up an array of empty cells, so that LOAD_GLOBAL_CELL can index func_cells without a NULL check.
  • Make c.cellptr equal to c when a cell is created, so that LOAD_GLOBAL_CELL can always dereference c.cellptr without a NULL check.

    With these two additional ideas added, here’s Python pseudo-code for LOAD_GLOBAL_CELL:

    def LOAD_GLOBAL_CELL(self, i):
    # self is the frame
    c = self.func_cells[i]
    obj = c.objptr
    if obj is not NULL:
        return obj # Existing global
    return c.cellptr.objptr # Built-in or NULL
  • Be more aggressive: put the actual values of builtins into module dicts, not just pointers to cells containing the actual values.

    There are two points to this: (1) Simplify and speed access, which is the most common operation. (2) Support faithful emulation of extreme existing corner cases.

    WRT #2, the set of builtins in the scheme above is captured at the time a module dict is first created. Mutations to the set of builtin names following that don’t get reflected in the module dicts. Example: consider files main.py and cheater.py:

    [main.py]
import cheater
def f():
    cheater.cheat()
    return pachinko()
print f()

[cheater.py]
def cheat():
    import __builtin__
    __builtin__.pachinko = lambda: 666

    If main.py is run under Python 2.2 (or before), 666 is printed. But under the proposal, __builtin__.pachinko doesn’t exist at the time main’s __dict__ is initialized. When the function object for f is created, main.__dict__ grows a pachinko cell mapping to two NULLs. When cheat() is called, __builtin__.__dict__ grows a pachinko cell too, but main.__dict__ doesn’t know– and will never know –about that. When f’s return stmt references pachinko, in will still find the double-NULLs in main.__dict__’s pachinko cell, and so raise NameError.

    A similar (in cause) break in compatibility can occur if a module global foo is del’ed, but a builtin foo was created prior to that but after the module dict was first created. Then the builtin foo becomes visible in the module under 2.2 and before, but remains invisible under the proposal.

    Mutating builtins is extremely rare (most programs never mutate the builtins, and it’s hard to imagine a plausible use for frequent mutation of the builtins – I’ve never seen or heard of one), so it doesn’t matter how expensive mutating the builtins becomes. OTOH, referencing globals and builtins is very common. Combining those observations suggests a more aggressive caching of builtins in module globals, speeding access at the expense of making mutations of the builtins (potentially much) more expensive to keep the caches in synch.

    Much of the scheme above remains the same, and most of the rest is just a little different. A cell changes to:

    class cell(object):
    def __init__(self, obj=NULL, builtin=0):
        self.objptr = obj
        self.builtinflag = builtin

    and a celldict maps strings to this version of cells. builtinflag is true when and only when objptr contains a value obtained from the builtins; in other words, it’s true when and only when a cell is acting as a cached value. When builtinflag is false, objptr is the value of a module global (possibly NULL). celldict changes to:

    class celldict(object):

    def __init__(self, builtindict=()):
        self.basedict = builtindict
        self.__dict = d = {}
        for k, v in builtindict.items():
            d[k] = cell(v, 1)

    def __getitem__(self, key):
        c = self.__dict.get(key)
        if c is None or c.objptr is NULL or c.builtinflag:
            raise KeyError, key
        return c.objptr

    def __setitem__(self, key, value):
        c = self.__dict.get(key)
        if c is None:
            c = cell()
            self.__dict[key] = c
        c.objptr = value
        c.builtinflag = 0

    def __delitem__(self, key):
        c = self.__dict.get(key)
        if c is None or c.objptr is NULL or c.builtinflag:
            raise KeyError, key
        c.objptr = NULL
        # We may have unmasked a builtin.  Note that because
        # we're checking the builtin dict for that *now*, this
        # still works if the builtin first came into existence
        # after we were constructed.  Note too that del on
        # namespace dicts is rare, so the expense of this check
        # shouldn't matter.
        if key in self.basedict:
            c.objptr = self.basedict[key]
            assert c.objptr is not NULL # else "in" lied
            c.builtinflag = 1
        else:
            # There is no builtin with the same name.
            assert not c.builtinflag

    def keys(self):
        return [k for k, c in self.__dict.iteritems()
                if c.objptr is not NULL and not c.builtinflag]

    def items(self):
        return [k, c.objptr for k, c in self.__dict.iteritems()
                if c.objptr is not NULL and not c.builtinflag]

    def values(self):
        preturn [c.objptr for c in self.__dict.itervalues()
                if c.objptr is not NULL and not c.builtinflag]

    def clear(self):
        for c in self.__dict.values():
            if not c.builtinflag:
                c.objptr = NULL

    # Etc.

    The speed benefit comes from simplifying LOAD_GLOBAL_CELL, which I expect is executed more frequently than all other namespace operations combined:

    def LOAD_GLOBAL_CELL(self, i):
    # self is the frame
    c = self.func_cells[i]
    return c.objptr   # may be NULL (also true before)

    That is, accessing builtins and accessing module globals are equally fast. For module globals, a NULL-pointer test+branch is saved. For builtins, an additional pointer chase is also saved.

    The other part needed to make this fly is expensive, propagating mutations of builtins into the module dicts that were initialized from the builtins. This is much like, in 2.2, propagating changes in new-style base classes to their descendants: the builtins need to maintain a list of weakrefs to the modules (or module dicts) initialized from the builtin’s dict. Given a mutation to the builtin dict (adding a new key, changing the value associated with an existing key, or deleting a key), traverse the list of module dicts and make corresponding mutations to them. This is straightforward; for example, if a key is deleted from builtins, execute reflect_bltin_del in each module:

    def reflect_bltin_del(self, key):
    c = self.__dict.get(key)
    assert c is not None # else we were already out of synch
    if c.builtinflag:
        # Put us back in synch.
        c.objptr = NULL
        c.builtinflag = 0
    # Else we're shadowing the builtin, so don't care that
    # the builtin went away.

    Note that c.builtinflag protects from us erroneously deleting a module global of the same name. Adding a new (key, value) builtin pair is similar:

    def reflect_bltin_new(self, key, value):
    c = self.__dict.get(key)
    if c is None:
        # Never heard of it before:  cache the builtin value.
        self.__dict[key] = cell(value, 1)
    elif c.objptr is NULL:
        # This used to exist in the module or the builtins,
        # but doesn't anymore; rehabilitate it.
        assert not c.builtinflag
        c.objptr = value
        c.builtinflag = 1
    else:
        # We're shadowing it already.
        assert not c.builtinflag

    Changing the value of an existing builtin:

    def reflect_bltin_change(self, key, newvalue):
    c = self.__dict.get(key)
    assert c is not None # else we were already out of synch
    if c.builtinflag:
        # Put us back in synch.
        c.objptr = newvalue
    # Else we're shadowing the builtin, so don't care that
    # the builtin changed.

FAQs

  • Q: Will it still be possible to:

    a) install new builtins in the __builtin__ namespace and have them available in all already loaded modules right away ?

    b) override builtins (e.g. open()) with my own copies (e.g. to increase security) in a way that makes these new copies override the previous ones in all modules ?

    A: Yes, this is the whole point of this design. In the original approach, when LOAD_GLOBAL_CELL finds a NULL in the second cell, it should go back to see if the __builtins__ dict has been modified (the pseudo code doesn’t have this yet). Tim’s “more aggressive” alternative also takes care of this.

  • Q: How does the new scheme get along with the restricted execution model?

    A: It is intended to support that fully.

  • Q: What happens when a global is deleted?

    A: The module’s celldict would have a cell with a NULL objptr for that key. This is true in both variations, but the “aggressive” variation goes on to see whether this unmasks a builtin of the same name, and if so copies its value (just a pointer-copy of the ultimate PyObject*) into the cell’s objptr and sets the cell’s builtinflag to true.

  • Q: What would the C code for LOAD_GLOBAL_CELL look like?

    A: The first version, with the first two bullets under “Additional ideas” incorporated, could look like this:

    case LOAD_GLOBAL_CELL:
    cell = func_cells[oparg];
    x = cell->objptr;
    if (x == NULL) {
        x = cell->cellptr->objptr;
        if (x == NULL) {
            ... error recovery ...
            break;
        }
    }
    Py_INCREF(x);
    PUSH(x);
    continue;

    We could even write it like this (idea courtesy of Ka-Ping Yee):

    case LOAD_GLOBAL_CELL:
    cell = func_cells[oparg];
    x = cell->cellptr->objptr;
    if (x != NULL) {
        Py_INCREF(x);
        PUSH(x);
        continue;
    }
    ... error recovery ...
    break;

    In modern CPU architectures, this reduces the number of branches taken for built-ins, which might be a really good thing, while any decent memory cache should realize that cell->cellptr is the same as cell for regular globals and hence this should be very fast in that case too.

    For the aggressive variant:

    case LOAD_GLOBAL_CELL:
    cell = func_cells[oparg];
    x = cell->objptr;
    if (x != NULL) {
        Py_INCREF(x);
        PUSH(x);
        continue;
    }
    ... error recovery ...
    break;
  • Q: What happens in the module’s top-level code where there is presumably no func_cells array?

    A: We could do some code analysis and create a func_cells array, or we could use LOAD_NAME which should use PyMapping_GetItem on the globals dict.

Graphics

Ka-Ping Yee supplied a drawing of the state of things after “import spam”, where spam.py contains:

import eggs

i = -2
max = 3

def foo(n):
    y = abs(i) + max
    return eggs.ham(y + n)

The drawing is at http://web.lfw.org/repo/cells.gif; a larger version is at http://lfw.org/repo/cells-big.gif; the source is at http://lfw.org/repo/cells.ai.

Comparison

XXX Here, a comparison of the three approaches could be added.

Source: https://github.com/python/peps/blob/master/pep-0280.txt

Last modified: 2019-07-03 18:20:45 GMT