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"""Decorator-based memoizer to count caching stats.
A decorator-based implementation to count hits and misses of the computed
values that various methods cache in memory.
Use of this modules assumes that wrapped methods be coded to cache their
values in a consistent way. In particular, it requires that the class uses a
dictionary named "_memo" to store the cached values.
Here is an example of wrapping a method that returns a computed value,
with no input parameters::
@SCons.Memoize.CountMethodCall
def foo(self):
try: # Memoization
return self._memo['foo'] # Memoization
except KeyError: # Memoization
pass # Memoization
result = self.compute_foo_value()
self._memo['foo'] = result # Memoization
return result
Here is an example of wrapping a method that will return different values
based on one or more input arguments::
def _bar_key(self, argument): # Memoization
return argument # Memoization
@SCons.Memoize.CountDictCall(_bar_key)
def bar(self, argument):
memo_key = argument # Memoization
try: # Memoization
memo_dict = self._memo['bar'] # Memoization
except KeyError: # Memoization
memo_dict = {} # Memoization
self._memo['dict'] = memo_dict # Memoization
else: # Memoization
try: # Memoization
return memo_dict[memo_key] # Memoization
except KeyError: # Memoization
pass # Memoization
result = self.compute_bar_value(argument)
memo_dict[memo_key] = result # Memoization
return result
Deciding what to cache is tricky, because different configurations
can have radically different performance tradeoffs, and because the
tradeoffs involved are often so non-obvious. Consequently, deciding
whether or not to cache a given method will likely be more of an art than
a science, but should still be based on available data from this module.
Here are some VERY GENERAL guidelines about deciding whether or not to
cache return values from a method that's being called a lot:
-- The first question to ask is, "Can we change the calling code
so this method isn't called so often?" Sometimes this can be
done by changing the algorithm. Sometimes the *caller* should
be memoized, not the method you're looking at.
-- The memoized function should be timed with multiple configurations
to make sure it doesn't inadvertently slow down some other
configuration.
-- When memoizing values based on a dictionary key composed of
input arguments, you don't need to use all of the arguments
if some of them don't affect the return values.
"""
# A flag controlling whether or not we actually use memoization.
use_memoizer = None
# Global list of counter objects
CounterList = {}
[docs]class Counter:
"""
Base class for counting memoization hits and misses.
We expect that the initialization in a matching decorator will
fill in the correct class name and method name that represents
the name of the function being counted.
"""
def __init__(self, cls_name, method_name) -> None:
"""
"""
self.cls_name = cls_name
self.method_name = method_name
self.hit = 0
self.miss = 0
[docs] def key(self):
return self.cls_name+'.'+self.method_name
[docs] def display(self) -> None:
print(f" {self.hit:7d} hits {self.miss:7d} misses {self.key()}()")
def __eq__(self, other):
try:
return self.key() == other.key()
except AttributeError:
return True
[docs]class CountValue(Counter):
"""
A counter class for simple, atomic memoized values.
A CountValue object should be instantiated in a decorator for each of
the class's methods that memoizes its return value by simply storing
the return value in its _memo dictionary.
"""
[docs] def count(self, *args, **kw) -> None:
""" Counts whether the memoized value has already been
set (a hit) or not (a miss).
"""
obj = args[0]
if self.method_name in obj._memo:
self.hit = self.hit + 1
else:
self.miss = self.miss + 1
[docs]class CountDict(Counter):
"""
A counter class for memoized values stored in a dictionary, with
keys based on the method's input arguments.
A CountDict object is instantiated in a decorator for each of the
class's methods that memoizes its return value in a dictionary,
indexed by some key that can be computed from one or more of
its input arguments.
"""
def __init__(self, cls_name, method_name, keymaker) -> None:
"""
"""
super().__init__(cls_name, method_name)
self.keymaker = keymaker
[docs] def count(self, *args, **kw) -> None:
""" Counts whether the computed key value is already present
in the memoization dictionary (a hit) or not (a miss).
"""
obj = args[0]
try:
memo_dict = obj._memo[self.method_name]
except KeyError:
self.miss = self.miss + 1
else:
key = self.keymaker(*args, **kw)
if key in memo_dict:
self.hit = self.hit + 1
else:
self.miss = self.miss + 1
[docs]def Dump(title=None) -> None:
""" Dump the hit/miss count for all the counters
collected so far.
"""
if title:
print(title)
for counter in sorted(CounterList):
CounterList[counter].display()
[docs]def EnableMemoization() -> None:
global use_memoizer
use_memoizer = 1
[docs]def CountMethodCall(fn):
""" Decorator for counting memoizer hits/misses while retrieving
a simple value in a class method. It wraps the given method
fn and uses a CountValue object to keep track of the
caching statistics.
Wrapping gets enabled by calling EnableMemoization().
"""
if use_memoizer:
def wrapper(self, *args, **kwargs):
global CounterList
key = self.__class__.__name__+'.'+fn.__name__
if key not in CounterList:
CounterList[key] = CountValue(self.__class__.__name__, fn.__name__)
CounterList[key].count(self, *args, **kwargs)
return fn(self, *args, **kwargs)
wrapper.__name__= fn.__name__
return wrapper
else:
return fn
[docs]def CountDictCall(keyfunc):
""" Decorator for counting memoizer hits/misses while accessing
dictionary values with a key-generating function. Like
CountMethodCall above, it wraps the given method
fn and uses a CountDict object to keep track of the
caching statistics. The dict-key function keyfunc has to
get passed in the decorator call and gets stored in the
CountDict instance.
Wrapping gets enabled by calling EnableMemoization().
"""
def decorator(fn):
if use_memoizer:
def wrapper(self, *args, **kwargs):
global CounterList
key = self.__class__.__name__+'.'+fn.__name__
if key not in CounterList:
CounterList[key] = CountDict(self.__class__.__name__, fn.__name__, keyfunc)
CounterList[key].count(self, *args, **kwargs)
return fn(self, *args, **kwargs)
wrapper.__name__= fn.__name__
return wrapper
else:
return fn
return decorator
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