Python Enhancement Proposals

PEP 329 – Treating Builtins as Constants in the Standard Library

Treating Builtins as Constants in the Standard Library
Raymond Hettinger <python at>
Standards Track



The proposal is to add a function for treating builtin references as constants and to apply that function throughout the standard library.


The PEP is self rejected by the author. Though the ASPN recipe was well received, there was less willingness to consider this for inclusion in the core distribution.

The Jython implementation does not use byte codes, so its performance would suffer if the current _len=len optimizations were removed.

Also, altering byte codes is one of the least clean ways to improve performance and enable cleaner coding. A more robust solution would likely involve compiler pragma directives or metavariables indicating what can be optimized (similar to const/volatile declarations).


The library contains code such as _len=len which is intended to create fast local references instead of slower global lookups. Though necessary for performance, these constructs clutter the code and are usually incomplete (missing many opportunities).

If the proposal is adopted, those constructs could be eliminated from the code base and at the same time improve upon their results in terms of performance.

There are currently over a hundred instances of while 1 in the library. They were not replaced with the more readable while True because of performance reasons (the compiler cannot eliminate the test because True is not known to always be a constant). Conversion of True to a constant will clarify the code while retaining performance.

Many other basic Python operations run much slower because of global lookups. In try/except statements, the trapped exceptions are dynamically looked up before testing whether they match. Similarly, simple identity tests such as while x is not None require the None variable to be re-looked up on every pass. Builtin lookups are especially egregious because the enclosing global scope must be checked first. These lookup chains devour cache space that is best used elsewhere.

In short, if the proposal is adopted, the code will become cleaner and performance will improve across the board.


Add a module called which contains two functions, bind_constants() and bind_all(). The first function performs constant binding and the second recursively applies it to every function and class in a target module.

For most modules in the standard library, add a pair of lines near the end of the script:

import codetweaks, sys

In addition to binding builtins, there are some modules (like sre_compile) where it also makes sense to bind module variables as well as builtins into constants.

Questions and Answers

  1. Will this make everyone divert their attention to optimization issues?

    Because it is done automatically, it reduces the need to think about optimizations.

  2. In a nutshell, how does it work?

    Every function has attributes with its bytecodes (the language of the Python virtual machine) and a table of constants. The bind function scans the bytecodes for a LOAD_GLOBAL instruction and checks to see whether the value is already known. If so, it adds that value to the constants table and replaces the opcode with LOAD_CONSTANT.

  3. When does it work?

    When a module is imported for the first time, python compiles the bytecode and runs the binding optimization. Subsequent imports just re-use the previous work. Each session repeats this process (the results are not saved in pyc files).

  4. How do you know this works?

    I implemented it, applied it to every module in library, and the test suite ran without exception.

  5. What if the module defines a variable shadowing a builtin?

    This does happen. For instance, True can be redefined at the module level as True = (1==1). The sample implementation below detects the shadowing and leaves the global lookup unchanged.

  6. Are you the first person to recognize that most global lookups are for values that never change?

    No, this has long been known. Skip Montanaro provides an eloquent explanation in PEP 266.

  7. What if I want to replace the builtins module and supply my own implementations?

    Either do this before importing a module, or just reload the module, or disable (it will have a disable flag).

  8. How susceptible is this module to changes in Python’s byte coding?

    It imports to protect against renumbering. Also, it uses LOAD_CONST and LOAD_GLOBAL which are fundamental and have been around forever. That notwithstanding, the coding scheme could change and this implementation would have to change along with modules like dis which also rely on the current coding scheme.

  9. What is the effect on startup time?

    I could not measure a difference. None of the startup modules are bound except for Also, the binding function is very fast, making just a single pass over the code string in search of the LOAD_GLOBAL opcode.

Sample Implementation

Here is a sample implementation for

from types import ClassType, FunctionType
from opcode import opmap, HAVE_ARGUMENT, EXTENDED_ARG

def bind_constants(f, builtin_only=False, stoplist=[], verbose=False):
    """ Return a new function with optimized global references.

    Replaces global references with their currently defined values.
    If not defined, the dynamic (runtime) global lookup is left undisturbed.
    If builtin_only is True, then only builtins are optimized.
    Variable names in the stoplist are also left undisturbed.
    If verbose is True, prints each substitution as is occurs.

    import __builtin__
    env = vars(__builtin__).copy()
    stoplist = dict.fromkeys(stoplist)
    if builtin_only:

    co = f.func_code
    newcode = map(ord, co.co_code)
    newconsts = list(co.co_consts)
    codelen = len(newcode)

    i = 0
    while i < codelen:
        opcode = newcode[i]
        if opcode in ABORT_CODES:
            return f    # for simplicity, only optimize common cases
        if opcode == LOAD_GLOBAL:
            oparg = newcode[i+1] + (newcode[i+2] << 8)
            name = co.co_names[oparg]
            if name in env and name not in stoplist:
                value = env[name]
                    pos = newconsts.index(value)
                except ValueError:
                    pos = len(newconsts)
                newcode[i] = LOAD_CONST
                newcode[i+1] = pos & 0xFF
                newcode[i+2] = pos >> 8
                if verbose:
                    print name, '-->', value
        i += 1
        if opcode >= HAVE_ARGUMENT:
            i += 2

    codestr = ''.join(map(chr, newcode))
    codeobj = type(co)(co.co_argcount, co.co_nlocals, co.co_stacksize,
                    co.co_flags, codestr, tuple(newconsts), co.co_names,
                    co.co_varnames, co.co_filename, co.co_name,
                    co.co_firstlineno, co.co_lnotab, co.co_freevars,
    return type(f)(codeobj, f.func_globals, f.func_name, f.func_defaults,

def bind_all(mc, builtin_only=False, stoplist=[], verbose=False):
    """Recursively apply bind_constants() to functions in a module or class.

    Use as the last line of the module (after everything is defined, but
    before test code).

    In modules that need modifiable globals, set builtin_only to True.

    for k, v in vars(mc).items():
        if type(v) is FunctionType:
            newv = bind_constants(v, builtin_only, stoplist, verbose)
            setattr(mc, k, newv)
        elif type(v) in (type, ClassType):
            bind_all(v, builtin_only, stoplist, verbose)

def f(): pass
except AttributeError:                  # detect non-CPython environments
    bind_all = lambda *args, **kwds: 0
del f

import sys
bind_all(sys.modules[__name__])         # Optimizer, optimize thyself!

Note the automatic detection of a non-CPython environment that does not have bytecodes [3]. In that situation, the bind functions would simply return the original function unchanged. This assures that the two line additions to library modules do not impact other implementations.

The final code should add a flag to make it easy to disable binding.


ASPN Recipe for a non-private implementation
Differences between CPython and Jython


Last modified: 2022-01-21 11:03:51 GMT