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.
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.
(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:
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:
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):
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.
With these two additional ideas added, here’s Python pseudo-code for LOAD_GLOBAL_CELL:
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:
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:
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:
The speed benefit comes from simplifying LOAD_GLOBAL_CELL, which I expect is executed more frequently than all other namespace operations combined:
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:
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:
Changing the value of an existing builtin:
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.
A: It is intended to support that fully.
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.
A: The first version, with the first two bullets under “Additional ideas” incorporated, could look like this:
We could even write it like this (idea courtesy of Ka-Ping Yee):
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:
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.
Ka-Ping Yee supplied a drawing of the state of things after “import spam”, where spam.py contains:
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.
XXX Here, a comparison of the three approaches could be added.
This document has been placed in the public domain.
Source: https://github.com/python/peps/blob/main/peps/pep-0280.rst
Last modified: 2025-02-01 08:55:40 UTC
