Python 中 lru_cache 的使用和实现

def factorial(n):    print(f"计算 {n} 的阶乘")    return 1 if n <= 1 else n * factorial(n - 1)
a = factorial(5)print(f'5! = {a}')b = factorial(3)print(f'3! = {b}')

计算 5 的阶乘计算 4 的阶乘计算 3 的阶乘计算 2 的阶乘计算 1 的阶乘5! = 120计算 3 的阶乘计算 2 的阶乘计算 1 的阶乘3! = 6

import functools# 注意 lru_cache 后的一对括号，证明这是带参数的装饰器@functools.lru_cache()def factorial(n):    print(f"计算 {n} 的阶乘")    return 1 if n <= 1 else n * factorial(n - 1)

计算 5 的阶乘计算 4 的阶乘计算 3 的阶乘计算 2 的阶乘计算 1 的阶乘5! = 1203! = 6

def lru_cache(maxsize=128, typed=False):    """Least-recently-used cache decorator.    If *maxsize* is set to None, the LRU features are disabled and the cache    can grow without bound.    If *typed* is True, arguments of different types will be cached separately.    For example, f(3.0) and f(3) will be treated as distinct calls with    distinct results.    Arguments to the cached function must be hashable.    View the cache statistics named tuple (hits, misses, maxsize, currsize)    with f.cache_info().  Clear the cache and statistics with f.cache_clear().    Access the underlying function with f.__wrapped__.    See:  http://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)    """
    # Users should only access the lru_cache through its public API:    #       cache_info, cache_clear, and f.__wrapped__    # The internals of the lru_cache are encapsulated for thread safety and    # to allow the implementation to change (including a possible C version).        if isinstance(maxsize, int):        # Negative maxsize is treated as 0        if maxsize < 0:            maxsize = 0    elif callable(maxsize) and isinstance(typed, bool):        # The user_function was passed in directly via the maxsize argument        user_function, maxsize = maxsize, 128        wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)        wrapper.cache_parameters = lambda : {'maxsize': maxsize, 'typed': typed}        return update_wrapper(wrapper, user_function)    elif maxsize is not None:        raise TypeError(            'Expected first argument to be an integer, a callable, or None')        def decorating_function(user_function):        wrapper = _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo)        wrapper.cache_parameters = lambda : {'maxsize': maxsize, 'typed': typed}        return update_wrapper(wrapper, user_function)        return decorating_function

import functools# 注意 lru_cache 后面没有括号，# 证明这是将其当做不带参数的装饰器@functools.lru_cachedef factorial(n):    print(f"计算 {n} 的阶乘")    return 1 if n <= 1 else n * factorial(n - 1)

def _lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):    # Constants shared by all lru cache instances:    sentinel = object()          # unique object used to signal cache misses    make_key = _make_key         # build a key from the function arguments    PREV, NEXT, KEY, RESULT = 0, 1, 2, 3   # names for the link fields
    cache = {}    hits = misses = 0    full = False    cache_get = cache.get    # bound method to lookup a key or return None    cache_len = cache.__len__  # get cache size without calling len()    lock = RLock()           # because linkedlist updates aren't threadsafe    root = []                # root of the circular doubly linked list    root[:] = [root, root, None, None]     # initialize by pointing to self
    if maxsize == 0:
        def wrapper(*args, **kwds):            # No caching -- just a statistics update            nonlocal misses            misses += 1            result = user_function(*args, **kwds)            return result
    elif maxsize is None:
        def wrapper(*args, **kwds):            # Simple caching without ordering or size limit            nonlocal hits, misses            key = make_key(args, kwds, typed)            result = cache_get(key, sentinel)            if result is not sentinel:                hits += 1                return result            misses += 1            result = user_function(*args, **kwds)            cache[key] = result            return result
    else:
        def wrapper(*args, **kwds):            # Size limited caching that tracks accesses by recency            nonlocal root, hits, misses, full            key = make_key(args, kwds, typed)            with lock:                link = cache_get(key)                if link is not None:                    # Move the link to the front of the circular queue                    link_prev, link_next, _key, result = link                    link_prev[NEXT] = link_next                    link_next[PREV] = link_prev                    last = root[PREV]                    last[NEXT] = root[PREV] = link                    link[PREV] = last                    link[NEXT] = root                    hits += 1                    return result                misses += 1            result = user_function(*args, **kwds)            with lock:                if key in cache:                    # Getting here means that this same key was added to the                    # cache while the lock was released.  Since the link                    # update is already done, we need only return the                    # computed result and update the count of misses.                    pass                elif full:                    # Use the old root to store the new key and result.                    oldroot = root                    oldroot[KEY] = key                    oldroot[RESULT] = result                    # Empty the oldest link and make it the new root.                    # Keep a reference to the old key and old result to                    # prevent their ref counts from going to zero during the                    # update. That will prevent potentially arbitrary object                    # clean-up code (i.e. __del__) from running while we're                    # still adjusting the links.                    root = oldroot[NEXT]                    oldkey = root[KEY]                    oldresult = root[RESULT]                    root[KEY] = root[RESULT] = None                    # Now update the cache dictionary.                    del cache[oldkey]                    # Save the potentially reentrant cache[key] assignment                    # for last, after the root and links have been put in                    # a consistent state.                    cache[key] = oldroot                else:                    # Put result in a new link at the front of the queue.                    last = root[PREV]                    link = [last, root, key, result]                    last[NEXT] = root[PREV] = cache[key] = link                    # Use the cache_len bound method instead of the len() function                    # which could potentially be wrapped in an lru_cache itself.                    full = (cache_len() >= maxsize)            return result
    def cache_info():        """Report cache statistics"""        with lock:            return _CacheInfo(hits, misses, maxsize, cache_len())
    def cache_clear():        """Clear the cache and cache statistics"""        nonlocal hits, misses, full        with lock:            cache.clear()            root[:] = [root, root, None, None]            hits = misses = 0            full = False
    wrapper.cache_info = cache_info    wrapper.cache_clear = cache_clear    return wrapper

CacheInfo(hits=1, misses=5, maxsize=128, currsize=5)