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Fun with generators

published on Tuesday, June 14, 2016

Can you imagine why the two generators in the following snippet can behave very different (in CPython):

def foo():
    yield Yielded()

def bar():
    x = Yielded()
    yield x

You guessed it: reference counts. I came across this phenomenon as an interesting variation of the bug described in Never trust your reference count!. I encountered the issue described in the present article much earlier (already in late 2015) and I'm writing about it now because I found it an intriguing discovery at the time.

A short example

The issue can be observed in the following minimal example. First, note that we slightly modify the above definitions of foo and bar – just to show messages when the action happens:

class Yielded:

    def __init__(self, name): = name

    def __del__(self):
        print("Delete {}".format(

def foo():
        yield Yielded("foo")        # Transfer control to caller.
        print("After yield: foo")   # This will never be printed.
    except GeneratorExit:
        print("Exit foo")           # Show when generator is closed.

def bar():
        x = Yielded("bar")
        yield x
        print("After yield: bar")
    except GeneratorExit:
        print("Exit bar")

Now, we invoke foo and bar in such a way that no reference chain from global scope to the generators is kept after having reached the first yield expression. However, we store a reference to the generator in the yielded Yielded instance:

from asyncio import new_event_loop

def consume_one(generator):
    x = next(generator)
    x.g = generator

if __name__ == '__main__':
    mainloop = new_event_loop()
    mainloop.call_later(0.0, consume_one, foo())
    mainloop.call_later(0.0, consume_one, bar())

This means the Yielded now contains the only reference to the generator. If we do not take care to create a global reference to the Yielded it can be deleted and therefore remove the only reference to the generator. When this happens, the generator is closed and deleted – triggering a GeneratorExit.

However, bar keeps a reference to the yielded object in the form of a local variable x. This establishes a reference cycle between bar and x – which means that (contrary to foo) bar will not be closed immediately due to reference counting.

On CPython the above program immediately outputs:

Delete foo
Exit foo

and then waits indefinitely.

Actual use case

This example may seem a little far fetched, is there any actual use case?

Yes. A more realistic example is extracted from a program of mine for which I hand-made a lightweight asynchronous layer. This was necessary since there was no stable alternative that could be used with PyGI (and also I wanted to keep python2 compatibility) at the time. In the actual code, the issue described here caused a tray icon to vanish in some cases immediately after creation and stop a sequence of asynchronous operations.

The protocol was based on Async objects (replacing Yielded). Similar to asyncio or Twisted, sequential execution of several asynchronous tasks is written with generators, where each yield expression transfers control to the yielded task.

The above backreference x.g to the generator is established by a callbacks that allows to continue the coroutine after having finished the intermediate task.

Reliable behaviour?

In this application, we want to keep the coroutine alive in order to continue its execution after the scheduled subtask is done. Does this mean that the form bar is more appropriate, i.e. can we rely on bar not being deleted?

No. The garbage collector can still detect the reference cycle and clean up the objects. You can check this out by manually triggering a call to gc.collect:

import gc

if __name__ == '__main__':
    mainloop.call_later(3.0, gc.collect)

The program output will now be:

Delete foo
Exit foo

(wait 3s):

Exit bar
Delete bar

This possibility introduces indeterministic behaviour that is hard to debug: The behaviour will generally be influenced by the insertion of debug statements or use of pdb.

Fixing the bug

The fix is to always ensure that there is a reference chain from a global scope to your coroutines. In the easiest case, you could just add a global reference to all executing coroutines. In the realistic example you could modify the Coroutine class like this:

class Coroutine(Async):

    __REFS = []

    def __init__(self, generator):
        self._generator = generator

    def _close(self):

This entry was tagged async, bug, programming, python and refcount