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Showing content from https://python.github.io/peps/pep-0499/ below:

PEP 499 – python -m foo should also bind 'foo' in sys.modules

Author:

Cameron Simpson <cs at cskk.id.au>, Chris Angelico <rosuav at gmail.com>, Joseph Jevnik <joejev at gmail.com>

BDFL-Delegate:

Alyssa Coghlan

Status:

Deferred

Type:

Standards Track

Created:

07-Aug-2015

Python-Version:

3.10

• PEP Deferral
• Abstract
• Proposal
• Considerations and Prerequisites
  • Pickling Modules
  • A Normal Module’s __name__ Is No Longer Canonical
• Reference Implementation
• Open Questions
  • Pickle compatibility
  • Projects that special-case __main__
• Background
• Copyright

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.

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 a TypeError when the objects are incompatible. When there are two instances of a module there are two sentinel instances and only one will be recognised via is.

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 also try/except constructs.

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'].

Alyssa (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.

Alyssa 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.

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.

BPO 36375 is the issue tracker entry for the PEP’s reference implementation, with the current draft PR being available on GitHub.

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.

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

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.

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.

This document has been placed in the public domain.

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