PEP 499 – python -m foo
should bind sys.modules['foo']
in addition to sys.modules['__main__']
- Author:
- Cameron Simpson <cs at cskk.id.au>, Chris Angelico <rosuav at gmail.com>, Joseph Jevnik <joejev at gmail.com>
- BDFL-Delegate:
- Nick Coghlan
- Status:
- Deferred
- Type:
- Standards Track
- Created:
- 07-Aug-2015
- Python-Version:
- 3.10
PEP Deferral
The implementation of this PEP isn’t currently expected to be ready for the Python 3.9 feature freeze in April 2020, so it has been deferred 12 months to Python 3.10.
Abstract
When a module is used as a main program on the Python command line, such as by:
python -m module.name …
it is easy to accidentally end up with two independent instances of the module if that module is again imported within the program. This PEP proposes a way to fix this problem.
When a module is invoked via Python’s -m option the module is bound
to sys.modules['__main__']
and its .__name__
attribute is set to
'__main__'
.
This enables the standard “main program” boilerplate code at the
bottom of many modules, such as:
if __name__ == '__main__':
sys.exit(main(sys.argv))
However, when the above command line invocation is used it is a
natural inference to presume that the module is actually imported
under its official name module.name
,
and therefore that if the program again imports that name
then it will obtain the same module instance.
That actuality is that the module was imported only as '__main__'
.
Another import will obtain a distinct module instance, which can
lead to confusing bugs,
all stemming from having two instances of module global objects:
one in each module.
Examples include:
- module level data structures
- Some modules provide features such as caches or registries
as module level global variables,
typically private.
A second instance of a module creates a second data structure.
If that structure is a cache
such as in the
re
module then two caches exist leading to wasteful memory use. If that structure is a shared registry such as a mapping of values to handlers then it is possible to register a handler to one registry and to try to use it via the other registry, where it is unknown. - sentinels
- The standard test for a sentinel value provided by a module
is the identity comparison using
is
, as this avoids unreliable “looks like” comparisons such as equality which can both mismatch two values as “equal” (for example being zeroish) or raise aTypeError
when the objects are incompatible. When there are two instances of a module there are two sentinel instances and only one will be recognised viais
. - classes
- With two modules
there are duplicate class definitions of any classes provided.
All operations which depend on recognising these classes
and subclasses of these are prone to failure
depending where the reference class
(from one of the modules) is obtained
and where the comparison class or instance is obtained.
This impacts
isinstance
,issubclass
and alsotry
/except
constructs.
Proposal
It is suggested that to fix this situation all that is needed is a
simple change to the way the -m
option is implemented: in addition
to binding the module object to sys.modules['__main__']
, it is also
bound to sys.modules['module.name']
.
Nick Coghlan has suggested that this is as simple as modifying the
runpy
module’s _run_module_as_main
function as follows:
main_globals = sys.modules["__main__"].__dict__
to instead be:
main_module = sys.modules["__main__"]
sys.modules[mod_spec.name] = main_module
main_globals = main_module.__dict__
Joseph Jevnik has pointed out that modules which are packages already do something very similar to this proposal: the __init__.py file is bound to the module’s canonical name and the __main__.py file is bound to “__main__”. As such, the double import issue does not occur. Therefore, this PEP proposes to affect only simple non-package modules.
Considerations and Prerequisites
Pickling Modules
Nick has mentioned issue 19702 which proposes (quoted from the issue):
- runpy will ensure that when __main__ is executed via the import system, it will also be aliased in sys.modules as __spec__.name
- if __main__.__spec__ is set, pickle will use __spec__.name rather than __name__ to pickle classes, functions and methods defined in __main__
- multiprocessing is updated appropriately to skip creating __mp_main__ in child processes when __main__.__spec__ is set in the parent process
The first point above covers this PEP’s specific proposal.
A Normal Module’s __name__
Is No Longer Canonical
Chris Angelico points out that it becomes possible to import a
module whose __name__
is not what you gave to “import”, since
“__main__” is now present at “module.name”, so a subsequent
import module.name
finds it already present.
Therefore, __name__
is no longer the canonical name for some normal imports.
Some counter arguments follow:
- As of PEP 451 a module’s canonical name is stored at
__spec__.name
. - Very little code should actually care about
__name__
being the canonical name and any that does should arguably be updated to consult__spec__.name
with fallback to__name__
for older Pythons, should that be relevant. This is true even if this PEP is not approved. - Should this PEP be approved,
it becomes possible to introspect a module by its canonical name
and ask “was this the main program?” by inferring from
__name__
. This was not previously possible.
The glaring counter example is the standard “am I the main program?” boilerplate,
where __name__
is expected to be “__main__”.
This PEP explicitly preserves that semantic.
Reference Implementation
BPO 36375 is the issue tracker entry for the PEP’s reference implementation, with the current draft PR being available on GitHub.
Open Questions
This proposal does raise some backwards compatibility concerns, and these will need to be well understood, and either a deprecation process designed, or clear porting guidelines provided.
Pickle compatibility
If no changes are made to the pickle module, then pickles that were previously being written with the correct module name (due to a dual import) may start being written with __main__ as their module name instead, and hence fail to be loaded correctly by other projects.
Scenarios to be checked:
- python script.py writing, python -m script reading
- python -m script writing, python script.py reading
- python -m script writing, python some_other_app.py reading
- old_python -m script writing, new_python -m script reading
- new_python -m script writing, old_python -m script reading
Projects that special-case __main__
In order to get the regression test suite to pass, the current reference implementation had to patch pdb to avoid destroying its own global namespace.
This suggests there may be a broader compatibility issue where some scripts are relying on direct execution and import giving different namespaces (just as package execution keeps the two separate by executing the __main__ submodule in the __main__ namespace, while the package name references the __init__ file as usual.
Background
I tripped over this issue while debugging a main program via a module which tried to monkey patch a named module, that being the main program module. Naturally, the monkey patching was ineffective as it imported the main module by name and thus patched the second module instance, not the running module instance.
However, the problem has been around as long as the -m
command
line option and is encountered regularly, if infrequently, by others.
In addition to issue 19702, the discrepancy around __main__ is alluded to in PEP 451 and a similar proposal (predating PEP 451) is described in PEP 395 under Fixing dual imports of the main module.
Copyright
This document has been placed in the public domain.
Source: https://github.com/python/peps/blob/main/pep-0499.txt
Last modified: 2022-01-21 11:03:51 GMT