Python LRU Cache Decorator Per Instance

Assuming you don't want to modify the code (e.g., because you want to be able to just port to 3.3 and use the stdlib functools.lru_cache, or use functools32 out of PyPI instead of copying and pasting a recipe into your code), there's one obvious solution: Create a new decorated instance method with each instance.

class Test:    def cached_method(self, x):         return x + 5    def __init__(self):         self.cached_method = lru_cache(maxsize=16)(self.cached_method)

How about this: a function decorator that wraps the method with lru_cache the first time it's called on each instance?

def instance_method_lru_cache(*cache_args, **cache_kwargs):    def cache_decorator(func):        @wraps(func)        def cache_factory(self, *args, **kwargs):            print('creating cache')            instance_cache = lru_cache(*cache_args, **cache_kwargs)(func)            instance_cache = instance_cache.__get__(self, self.__class__)            setattr(self, func.__name__, instance_cache)            return instance_cache(*args, **kwargs)        return cache_factory    return cache_decorator

Use it like this:

class Foo:    @instance_method_lru_cache()    def times_2(self, bar):        return bar * 2foo1 = Foo()foo2 = Foo()print(foo1.times_2(2))# creating cache# 4foo1.times_2(2)# 4print(foo2.times_2(2))# creating cache# 4foo2.times_2(2)# 4

Here's a gist on GitHub with some inline documentation.

These days, methodtools will work

from methodtools import lru_cacheclass Test:    @lru_cache(maxsize=16)    def cached_method(self, x):         return x + 5

You need to install methodtools

pip install methodtools

If you are still using py2, then functools32 also is required

pip install functools32