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Cloudpickle does not properly register submodule dependencies of a pickled function if the function accesses the submodule via `getattr` (or equivalent means)

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3/5
预计耗时
1-2 天
新手友好度
48/100
Issue 类型
缺陷
描述清晰度
基本清楚
活跃度
停滞
技术栈
python
领域
tooling

调研方向

从 cloudpickle/cloudpickle.py 中大约第 383 行的子模块检测逻辑开始，然后使用报告中的 getattr 或 vars(concurrent)['futures'] 示例重现该问题。跟踪 pickling 期间名称的收集方式，并验证当通过间接方式访问子模块名称时，dump 后的函数可以在另一个会话中加载并成功调用。

由索引模型根据 Issue 内容生成。

描述

As seen on master:

>>> import cloudpickle
>>> cloudpickle.version
'3.2.0.dev0'
>>> import concurrent.futures
>>> def func():                                              
...     x = getattr(concurrent, 'futures').ThreadPoolExecutor
... 
>>> func()  # can be succesfully called
>>> cloudpickle.dump(func, open('/tmp/dump', 'wb'))

Then in another session:

>>> import cloudpickle
>>> cloudpickle.load(open('/tmp/dump', 'rb'))()  # not callable upon load
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in func
AttributeError: module 'concurrent' has no attribute 'futures'

The reason is that at pickle time, the submodule detection logic only registers that a function needs a submodule x.y.z if the strings y, z all appear in the set of names stored in the function's code object. If the pickled function were to access the submodule via concurrent.futures, then both concurrent and futures appear in the set of names. But in the failing example above, 'futures' is a string and so doesn't appear in the set of names.

We can trigger the failure by replacing the getattr call with say vars(concurrent)['futures'] or concurrent.__dict__['futures'] for the same reason.

Relates to this issue about slow performance when pickling functions that use packages.

One could argue that this access pattern is sufficiently abnormal that cloudpickle doesn't need to handle it properly. But in the related issue, a maintainer asked me to make a new issue for this problem.

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