Freeze class-level experiment attributes

[Lucaweihs]

Problem

Currently there is nothing preventing a user from changing the values of class-level attributes of ExperimentConfigs at runtime. This was ok in the old implementation but now, as each light_engine is spawned using "forkserver", we run into a problem, the code:

import torch.multiprocessing as mp
import time

class BaseBabyAIGoToObjExperimentConfig:
    MY_GREAT_VARIABLE = 3

def my_func(config):
    print(config.MY_GREAT_VARIABLE)

if __name__ == "__main__":
    mp = mp.get_context("forkserver") # Broken
    # mp = mp.get_context("fork") # Works
    BaseBabyAIGoToObjExperimentConfig.MY_GREAT_VARIABLE = 5
    cfg = BaseBabyAIGoToObjExperimentConfig()
    p = mp.Process(target=my_func, kwargs=dict(config=cfg))
    print("main", cfg.MY_GREAT_VARIABLE)
    p.start()
    p.join()

will print 5 and then 3 when mp = mp.get_context("forkserver") but 5 then 5 when mp = mp.get_context("fork"). This means that a user might change the value of a class-level attribute before running training but this change will not propagate to the training process. We seem to need "forkserver" for some CUDA reasons (?) so it's probably best to:

1. Disallow the user from changing class-level variables, or
2. Detect any such change and throw an error if the runner is called with such a, modified, config.

Solution

This requires:

Having some means by which to stop changes to class-level variables. One approach that goes part of the way there is the following pattern:

class FrozenClassVariables(type):
    def __setattr__(cls, attr, value):
        raise RuntimeError("Cannot edit class-level attributes.")

class SomeClass(object, metaclass=FrozenClassVariables):
    yar = 3
    

if __name__ == "__main__":
    
    try:
        SomeClass.yar = 6 # Error
    except Exception as _:
        print("Threw exception")
    
    SomeClass().bar = 12 # No error

I'm not sure how to make ExperimentConfigs automatically have this metaclass but I presume it's possible.

Dependencies

None