pep-0280.txt

PEP: 280
Title: Optimizing access to globals
Version: $Revision$
Last-Modified: $Date$
Author: guido@python.org (Guido van Rossum)
Status: Deferred
Type: Standards Track
Content-Type: text/x-rst
Created: 10-Feb-2002
Python-Version: 2.3
Post-History:


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.


Copyright
=========

This document has been placed in the public domain.


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