Caching a function that takes in a jitclass instance

@jitclass([])
class NumbaClass():
    def __init__(self):
        pass

@njit(cache=True)
def calc(numbaObj):
    pass

numbaObject = NumbaClass()
calc(numbaObject)

Each time the script above is ran, it generates a new cache entry for “calc”, likely because NumbaClass.class_type.instance_type changes on every run, making the cache flag effectively not work and growing the __pycache__ folder on every run (mine eneded up being a few gigs). Is there a work-around for this? Numba 0.51.2

[stuartarchibald]

Thanks for the report. I think your assessment of the situation is correct. Should probably warn users this doesn’t work and disable caching.

[valtron]

This is due to id(self) being part of the jitclass’ name:https://github.com/numba/numba/blob/f04f4b164887e5aa5ffeeed1bef317e13ec995f8/numba/core/types/misc.py#L425-L426

I looked through the history but couldn’t figure out why it’s needed. I saw at some point early on (when jitclasses were being added), the sig didn’t include the fielddesc. But since it does include it now, it seems like that should be enough to fully define the type’s layout? Is there some problem I’m not seeing?

I tried removing id(self), which makes caching work, and adding/removing fields works as expected too (just makes new cache entries).