toggling decorators

Question

What's the best way to toggle decorators on and off, without actually going to each decoration and commenting it out? Say you have a benchmarking decorator:

# deco.py
def benchmark(func):
  def decorator():
    # fancy benchmarking 
  return decorator

and in your module something like:

# mymodule.py
from deco import benchmark

class foo(object):
  @benchmark
  def f():
    # code

  @benchmark
  def g():
    # more code

That's fine, but sometimes you don't care about the benchmarks and don't want the overhead. I have been doing the following. Add another decorator:

# anothermodule.py
def noop(func):
  # do nothing, just return the original function
  return func

And then comment out the import line and add another:

# mymodule.py
#from deco import benchmark 
from anothermodule import noop as benchmark

Now benchmarks are toggled on a per-file basis, having only to change the import statement in the module in question. Individual decorators can be controlled independently.

Is there a better way to do this? It would be nice to not have to edit the source file at all, and to specify which decorators to use in which files elsewhere.

Answer 1

You could add the conditional to the decorator itself:

def use_benchmark(modname):
    return modname == "mymodule"

def benchmark(func):
    if not use_benchmark(func.__module__):
        return func
    def decorator():
        # fancy benchmarking 
    return decorator

If you apply this decorator in mymodule.py, it will be enabled; if you apply it in othermodule.py, it will not be enabled.

Answer 2

I've been using the following approach. It's almost identical to the one suggested by CaptainMurphy, but it has the advantage that you don't need to call the decorator like a function.

import functools

class SwitchedDecorator:
    def __init__(self, enabled_func):
        self._enabled = False
        self._enabled_func = enabled_func

    @property
    def enabled(self):
        return self._enabled

    @enabled.setter
    def enabled(self, new_value):
        if not isinstance(new_value, bool):
            raise ValueError("enabled can only be set to a boolean value")
        self._enabled = new_value

    def __call__(self, target):
        if self._enabled:
            return self._enabled_func(target)
        return target


def deco_func(target):
    """This is the actual decorator function.  It's written just like any other decorator."""
    def g(*args,**kwargs):
        print("your function has been wrapped")
        return target(*args,**kwargs)
    functools.update_wrapper(g, target)
    return g


# This is where we wrap our decorator in the SwitchedDecorator class.
my_decorator = SwitchedDecorator(deco_func)

# Now my_decorator functions just like the deco_func decorator,
# EXCEPT that we can turn it on and off.
my_decorator.enabled=True

@my_decorator
def example1():
    print("example1 function")

# we'll now disable my_decorator.  Any subsequent uses will not
# actually decorate the target function.
my_decorator.enabled=False
@my_decorator
def example2():
    print("example2 function")

In the above, example1 will be decorated, and example2 will NOT be decorated. When I have to enable or disable decorators by module, I just have a function that makes a new SwitchedDecorator whenever I need a different copy.

Answer 3

I think you should use a decorator a to decorate the decorator b, which let you switch the decorator b on or off with the help of a decision function.

This sounds complex, but the idea is rather simple.

So let's say you have a decorator logger:

from functools import wraps 
def logger(f):
    @wraps(f)
    def innerdecorator(*args, **kwargs):
        print (args, kwargs)
        res = f(*args, **kwargs)
        print res
        return res
    return innerdecorator

This is a very boring decorator and I have a dozen or so of these, cachers, loggers, things which inject stuff, benchmarking etc. I could easily extend it with an if statement, but this seems to be a bad choice; because then I have to change a dozen of decorators, which is not fun at all.

So what to do? Let's step one level higher. Say we have a decorator, which can decorate a decorator? This decorator would look like this:

@point_cut_decorator(logger)
def my_oddly_behaving_function

This decorator accepts logger, which is not a very interesting fact. But it also has enough power to choose if the logger should be applied or not to my_oddly_behaving_function. I called it point_cut_decorator, because it has some aspects of aspect oriented programming. A point cut is a set of locations, where some code (advice) has to be interwoven with the execution flow. The definitions of point cuts is usually in one place. This technique seems to be very similar.

How can we implement it decision logic. Well I have chosen to make a function, which accepts the decoratee, the decorator, file and name, which can only say if a decorator should be applied or not. These are the coordinates, which are good enough to pinpoint the location very precisely.

This is the implementation of point_cut_decorator, I have chosen to implement the decision function as a simple function, you could extend it to let it decide from your settings or configuration, if you use regexes for all 4 coordinates, you will end up with something very powerful:

from functools import wraps

myselector is the decision function, on true a decorator is applied on false it is not applied. Parameters are the filename, the module name, the decorated object and finally the decorator. This allows us to switch of behaviour in a fine grained manner.

def myselector(fname, name, decoratee, decorator):
    print fname

    if decoratee.__name__ == "test" and fname == "decorated.py" and decorator.__name__ == "logger":
        return True
    return False 

This decorates a function, checks myselector and if myselector says go on, it will apply the decorator to the function.

def point_cut_decorator(d):
    def innerdecorator(f):
        @wraps(f)
        def wrapper(*args, **kwargs):
            if myselector(__file__, __name__, f, d):
                ps = d(f)
                return ps(*args, **kwargs)
            else:
                return f(*args, **kwargs)
        return wrapper
    return innerdecorator


def logger(f):
    @wraps(f)
    def innerdecorator(*args, **kwargs):
        print (args, kwargs)
        res = f(*args, **kwargs)
        print res
        return res
    return innerdecorator

And this is how you use it:

@point_cut_decorator(logger)
def test(a):
    print "hello"
    return "world"

test(1)

EDIT:

This is the regular expression approach I talked about:

from functools import wraps
import re

As you can see, I can specify somewhere a couple of rules, which decides a decorator should be applied or not:

rules = [{
    "file": "decorated.py",
    "module": ".*",
    "decoratee": ".*test.*",
    "decorator": "logger"
}]

Then I loop over all rules and return True if a rule matches or false if a rule doesn't matches. By making rules empty in production, this will not slow down your application too much:

def myselector(fname, name, decoratee, decorator):
    for rule in rules:
        file_rule, module_rule, decoratee_rule, decorator_rule = rule["file"], rule["module"], rule["decoratee"], rule["decorator"]
        if (
            re.match(file_rule, fname)
            and re.match(module_rule, name)
            and re.match(decoratee_rule, decoratee.__name__)
            and re.match(decorator_rule, decorator.__name__)
        ):
            return True
    return False

Answer 4

Here is what I finally came up with for per-module toggling. It uses @nneonneo's suggestion as a starting point.

Random modules use decorators as normal, no knowledge of toggling.

foopkg.py:

from toggledeco import benchmark

@benchmark
def foo():
    print("function in foopkg")

barpkg.py:

from toggledeco import benchmark

@benchmark
def bar():
    print("function in barpkg")

The decorator module itself maintains a set of function references for all decorators that have been disabled, and each decorator checks for its existence in this set. If so, it just returns the raw function (no decorator). By default the set is empty (everything enabled).

toggledeco.py:

import functools

_disabled = set()
def disable(func):
    _disabled.add(func)
def enable(func):
    _disabled.discard(func)

def benchmark(func):
    if benchmark in _disabled:
        return func
    @functools.wraps(func)
    def deco(*args,**kwargs):
        print("--> benchmarking %s(%s,%s)" % (func.__name__,args,kwargs))
        ret = func(*args,**kwargs)
        print("<-- done")
    return deco

The main program can toggle individual decorators on and off during imports:

from toggledeco import benchmark, disable, enable

disable(benchmark) # no benchmarks...
import foopkg

enable(benchmark) # until they are enabled again
import barpkg

foopkg.foo() # no benchmarking 
barpkg.bar() # yes benchmarking

reload(foopkg)
foopkg.foo() # now with benchmarking

Output:

function in foopkg
--> benchmarking bar((),{})
function in barpkg
<-- done
--> benchmarking foo((),{})
function in foopkg
<-- done

This has the added bug/feature that enabling/disabling will trickle down to any submodules imported from modules imported in the main function.

EDIT:

Here's class suggested by @nneonneo. In order to use it, the decorator must be called as a function ( @benchmark(), not @benchmark ).

class benchmark:
    disabled = False

    @classmethod
    def enable(cls):
        cls.disabled = False

    @classmethod
    def disable(cls):
        cls.disabled = True

    def __call__(cls,func):
        if cls.disabled:
            return func
        @functools.wraps(func)
        def deco(*args,**kwargs):
            print("--> benchmarking %s(%s,%s)" % (func.__name__,args,kwargs))
            ret = func(*args,**kwargs)
            print("<-- done")
        return deco

Answer 5

I would implement a check for a config file inside the decorator's body. If benchmark has to be used according to the config file, then I would go to your current decorator's body. If not, I would return the function and do nothing more. Something in this flavor:

# deco.py
def benchmark(func):
  if config == 'dontUseDecorators': # no use of decorator
      # do nothing
      return func
  def decorator(): # else call decorator
      # fancy benchmarking 
  return decorator

What happens when calling a decorated function ? @ in

@benchmark
def f():
    # body comes here

is syntactic sugar for this

f = benchmark(f)

so if config wants you to overlook decorator, you are just doing f = f() which is what you expect.

Answer 6

I don't think anyone has suggested this yet:

benchmark_modules = set('mod1', 'mod2') # Load this from a config file

def benchmark(func):
  if not func.__module__ in benchmark_modules:
      return func

  def decorator():
    # fancy benchmarking 
  return decorator

Each function or method has a __module__ attribute that is the name of the module where the function is defined. Create a whitelist (or blacklist if you prefer) of modules where benchmarking is to occur, and if you don't want to benchmark that module just return the original undecorated function.

Answer 7

another straight way:

# mymodule.py
from deco import benchmark

class foo(object):

  def f():
    # code

  if <config.use_benchmark>:
    f = benchmark(f)

  def g():
    # more code

  if <config.use_benchmark>:
    g = benchmark(g)

Answer 8

Here's a workaround to automatically toggle a decorator (here: @profile used by line_profiler):

if 'profile' not in __builtins__ or type(__builtins__) is not dict: profile=lambda x: None;

• More info

This conditional (only if needed) instantiation of the profile variable (as an empty lambda function) prevents raising NameError when trying to import our module with user-defined functions where the decorator @profile is applied to every profiled user function. If I ever want to use the decorator for profiling - it will still work, not being overwritten (already existing in an external script kernprof that contains this decorator).