PEP 488 – Elimination of PYO files

PEP: 488
Title: Elimination of PYO files
Author: Brett Cannon <brett at python.org>
Status: Final
Type: Standards Track
Created: 20-Feb-2015
Python-Version: 3.5
Post-History: 2015-03-06 2015-03-13 2015-03-20

Abstract
Rationale
Proposal
Implementation
- importlib
- Rest of the standard library
Compatibility Considerations
Rejected Ideas
References
Copyright

Abstract

This PEP proposes eliminating the concept of PYO files from Python. To continue the support of the separation of bytecode files based on their optimization level, this PEP proposes extending the PYC file name to include the optimization level in the bytecode repository directory when there are optimizations applied.

Rationale

As of today, bytecode files come in two flavours: PYC and PYO. A PYC file is the bytecode file generated and read from when no optimization level is specified at interpreter startup (i.e., -O is not specified). A PYO file represents the bytecode file that is read/written when any optimization level is specified (i.e., when -O or -OO is specified). This means that while PYC files clearly delineate the optimization level used when they were generated – namely no optimizations beyond the peepholer – the same is not true for PYO files. To put this in terms of optimization levels and the file extension:

0: .pyc
1 (-O): .pyo
2 (-OO): .pyo

The reuse of the .pyo file extension for both level 1 and 2 optimizations means that there is no clear way to tell what optimization level was used to generate the bytecode file. In terms of reading PYO files, this can lead to an interpreter using a mixture of optimization levels with its code if the user was not careful to make sure all PYO files were generated using the same optimization level (typically done by blindly deleting all PYO files and then using the compileall module to compile all-new PYO files 1). This issue is only compounded when people optimize Python code beyond what the interpreter natively supports, e.g., using the astoptimizer project 2.

In terms of writing PYO files, the need to delete all PYO files every time one either changes the optimization level they want to use or are unsure of what optimization was used the last time PYO files were generated leads to unnecessary file churn. The change proposed by this PEP also allows for all optimization levels to be pre-compiled for bytecode files ahead of time, something that is currently impossible thanks to the reuse of the .pyo file extension for multiple optimization levels.

As for distributing bytecode-only modules, having to distribute both .pyc and .pyo files is unnecessary for the common use-case of code obfuscation and smaller file deployments. This means that bytecode-only modules will only load from their non-optimized .pyc file name.

Proposal

To eliminate the ambiguity that PYO files present, this PEP proposes eliminating the concept of PYO files and their accompanying .pyo file extension. To allow for the optimization level to be unambiguous as well as to avoid having to regenerate optimized bytecode files needlessly in the __pycache__ directory, the optimization level used to generate the bytecode file will be incorporated into the bytecode file name. When no optimization level is specified, the pre-PEP .pyc file name will be used (i.e., no optimization level will be specified in the file name). For example, a source file named foo.py in CPython 3.5 could have the following bytecode files based on the interpreter’s optimization level (none, -O, and -OO):

0: foo.cpython-35.pyc (i.e., no change)
1: foo.cpython-35.opt-1.pyc
2: foo.cpython-35.opt-2.pyc

Currently bytecode file names are created by importlib.util.cache_from_source(), approximately using the following expression defined by PEP 3147 3, 4, 5:

'{name}.{cache_tag}.pyc'.format(name=module_name,
                                cache_tag=sys.implementation.cache_tag)

This PEP proposes to change the expression when an optimization level is specified to:

'{name}.{cache_tag}.opt-{optimization}.pyc'.format(
        name=module_name,
        cache_tag=sys.implementation.cache_tag,
        optimization=str(sys.flags.optimize))

The “opt-” prefix was chosen so as to provide a visual separator from the cache tag. The placement of the optimization level after the cache tag was chosen to preserve lexicographic sort order of bytecode file names based on module name and cache tag which will not vary for a single interpreter. The “opt-” prefix was chosen over “o” so as to be somewhat self-documenting. The “opt-” prefix was chosen over “O” so as to not have any confusion in case “0” was the leading prefix of the optimization level.

A period was chosen over a hyphen as a separator so as to distinguish clearly that the optimization level is not part of the interpreter version as specified by the cache tag. It also lends to the use of the period in the file name to delineate semantically different concepts.

For example, if -OO had been passed to the interpreter then instead of importlib.cpython-35.pyo the file name would be importlib.cpython-35.opt-2.pyc.

Leaving out the new opt- tag when no optimization level is applied should increase backwards-compatibility. This is also more understanding of Python implementations which have no use for optimization levels (e.g., PyPy[10]_).

It should be noted that this change in no way affects the performance of import. Since the import system looks for a single bytecode file based on the optimization level of the interpreter already and generates a new bytecode file if it doesn’t exist, the introduction of potentially more bytecode files in the __pycache__ directory has no effect in terms of stat calls. The interpreter will continue to look for only a single bytecode file based on the optimization level and thus no increase in stat calls will occur.

The only potentially negative result of this PEP is the probable increase in the number of .pyc files and thus increase in storage use. But for platforms where this is an issue, sys.dont_write_bytecode exists to turn off bytecode generation so that it can be controlled offline.

Implementation

An implementation of this PEP is available 11.

importlib

As importlib.util.cache_from_source() is the API that exposes bytecode file paths as well as being directly used by importlib, it requires the most critical change. As of Python 3.4, the function’s signature is:

importlib.util.cache_from_source(path, debug_override=None)

This PEP proposes changing the signature in Python 3.5 to:

importlib.util.cache_from_source(path, debug_override=None, *, optimization=None)

The introduced optimization keyword-only parameter will control what optimization level is specified in the file name. If the argument is None then the current optimization level of the interpreter will be assumed (including no optimization). Any argument given for optimization will be passed to str() and must have str.isalnum() be true, else ValueError will be raised (this prevents invalid characters being used in the file name). If the empty string is passed in for optimization then the addition of the optimization will be suppressed, reverting to the file name format which predates this PEP.

It is expected that beyond Python’s own two optimization levels, third-party code will use a hash of optimization names to specify the optimization level, e.g. hashlib.sha256(','.join(['no dead code', 'const folding'])).hexdigest(). While this might lead to long file names, it is assumed that most users never look at the contents of the __pycache__ directory and so this won’t be an issue.

The debug_override parameter will be deprecated. A False value will be equivalent to optimization=1 while a True value will represent optimization='' (a None argument will continue to mean the same as for optimization). A deprecation warning will be raised when debug_override is given a value other than None, but there are no plans for the complete removal of the parameter at this time (but removal will be no later than Python 4).

The various module attributes for importlib.machinery which relate to bytecode file suffixes will be updated 7. The DEBUG_BYTECODE_SUFFIXES and OPTIMIZED_BYTECODE_SUFFIXES will both be documented as deprecated and set to the same value as BYTECODE_SUFFIXES (removal of DEBUG_BYTECODE_SUFFIXES and OPTIMIZED_BYTECODE_SUFFIXES is not currently planned, but will be not later than Python 4).

All various finders and loaders will also be updated as necessary, but updating the previous mentioned parts of importlib should be all that is required.

Rest of the standard library

The various functions exposed by the py_compile and compileall functions will be updated as necessary to make sure they follow the new bytecode file name semantics 6, 1. The CLI for the compileall module will not be directly affected (the -b flag will be implicit as it will no longer generate .pyo files when -O is specified).

Compatibility Considerations

Any code directly manipulating bytecode files from Python 3.2 on will need to consider the impact of this change on their code (prior to Python 3.2 – including all of Python 2 – there was no __pycache__ which already necessitates bifurcating bytecode file handling support). If code was setting the debug_override argument to importlib.util.cache_from_source() then care will be needed if they want the path to a bytecode file with an optimization level of 2. Otherwise only code not using importlib.util.cache_from_source() will need updating.

As for people who distribute bytecode-only modules (i.e., use a bytecode file instead of a source file), they will have to choose which optimization level they want their bytecode files to be since distributing a .pyo file with a .pyc file will no longer be of any use. Since people typically only distribute bytecode files for code obfuscation purposes or smaller distribution size then only having to distribute a single .pyc should actually be beneficial to these use-cases. And since the magic number for bytecode files changed in Python 3.5 to support PEP 465 there is no need to support pre-existing .pyo files 8.

Rejected Ideas

Completely dropping optimization levels from CPython

Some have suggested that instead of accommodating the various optimization levels in CPython, we should instead drop them entirely. The argument is that significant performance gains would occur from runtime optimizations through something like a JIT and not through pre-execution bytecode optimizations.

This idea is rejected for this PEP as that ignores the fact that there are people who do find the pre-existing optimization levels for CPython useful. It also assumes that no other Python interpreter would find what this PEP proposes useful.

Alternative formatting of the optimization level in the file name

Using the “opt-” prefix and placing the optimization level between the cache tag and file extension is not critical. All options which have been considered are:

importlib.cpython-35.opt-1.pyc
importlib.cpython-35.opt1.pyc
importlib.cpython-35.o1.pyc
importlib.cpython-35.O1.pyc
importlib.cpython-35.1.pyc
importlib.cpython-35-O1.pyc
importlib.O1.cpython-35.pyc
importlib.o1.cpython-35.pyc
importlib.1.cpython-35.pyc

These were initially rejected either because they would change the sort order of bytecode files, possible ambiguity with the cache tag, or were not self-documenting enough. An informal poll was taken and people clearly preferred the formatting proposed by the PEP 9. Since this topic is non-technical and of personal choice, the issue is considered solved.

Embedding the optimization level in the bytecode metadata

Some have suggested that rather than embedding the optimization level of bytecode in the file name that it be included in the file’s metadata instead. This would mean every interpreter had a single copy of bytecode at any time. Changing the optimization level would thus require rewriting the bytecode, but there would also only be a single file to care about.

This has been rejected due to the fact that Python is often installed as a root-level application and thus modifying the bytecode file for modules in the standard library are always possible. In this situation integrators would need to guess at what a reasonable optimization level was for users for any/all situations. By allowing multiple optimization levels to co-exist simultaneously it frees integrators from having to guess what users want and allows users to utilize the optimization level they want.