python / python/cpython

`"import"` audit hook documentation is misleading

Abierto
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Descripción

Documentation

The documentation for the import statement states that it:

Raises an auditing event import with arguments module, filename, sys.path, sys.meta_path, sys.path_hooks.

However, this is not the case if the target of the import is already in sys.modules, as the audit event is raised only on first import. For imports of Python source, the event fires from import_find_and_load(), which is called from PyImport_ImportModuleLevelObject() only if the module is not already loaded.

The docs seem to suggest that the audit event is associated with the statement, but the docs are wrong.

Promote to bug?

I'm filing this as a documentation issue because the docs are not describing this behavior, but it seems that it may represent a bug in CPython. I'm willing to write a patch either way and would like a core developer to decide if this is a docs problem or a runtime problem.

It seems to me that there are a lot of use cases where a user really does want an audit event for every import statement, regardless of whether or not the module has already been imported. In fact, I found this quirk specifically because of one of these use cases. I had an application running import torch in a place where that (memory-hungry) import needed to be deferred, but the output of my debugging audit hook showed me only the first import (which luckily was still enough information to fix the problem).

If a core developer agrees that it makes sense to raise this event even if the target module is already present in sys.modules, this could be promoted to a CPython bug.

Sample program

# target_program.py
import sys
import hook
sys.addaudithook(hook.audit_numpy_import)

# BEGIN unmodified target program
import numpy

from helper import somefunc  # also runs `import numpy`


def random_array():
    arr = np.random.randint(0, 255, size=(30, 50))

    return arr, somefunc()
helper.py
import numpy


def somefunc():
    return numpy.array([42])
hook.py
import inspect
import sys


def audit_numpy_import(event, args):
    if event != "import":
        return

    TARGET_MODULE = "numpy"

    module, filename, syspath, sysmeta_path, syspath_hooks = args
    if module == TARGET_MODULE:
        stack = inspect.stack()
        target_frame = stack[1]  # index 0 is *this* frame, index 1 is where the audit event happened
        fn = target_frame.filename
        lineno = target_frame.lineno
        print(f"{TARGET_MODULE} imported at {fn}:{lineno}")

Running the above instrument program produces the output:

$ python3 target_program.py
numpy imported at /tmp/whats-importing-that-module/target_program.py:8

Where the output I wanted is:

numpy imported at /tmp/whats-importing-that-module/target_program.py:8
numpy imported at /tmp/whats-importing-that-module/helper.py:1

I've confirmed that I can get the above output if I add sys.modules.pop("numpy") after the first numpy import, but unfortunately this (unreliable!) workaround cannot be used from the audit hook, since the module is not placed there until after the hook has finished executing.

Guía de contribución

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Línea de trabajo

Comienza con la documentación de la instrucción de importación en Doc/reference/simple_stmts.rst y compara su redacción sobre el evento de auditoría con el comportamiento descrito en Python/import.c, especialmente import_find_and_load() y PyImport_ImportModuleLevelObject(). Ejecuta el programa de ejemplo para reproducir la discrepancia. Se considera terminado cuando el proyecto haya decidido si se trata de un problema de documentación o de ejecución y el comportamiento relevante esté cubierto correctamente.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
python
Área
documentation
Tipo de issue
Documentación
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
35/100

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