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Debugging Reference Count Problems


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Debugging Reference Count Problems

From: Guido van Rossum <guido@CNRI.Reston.VA.US>
Date: Wed, 27 May 1998 11:09:40 -0400

Mike Fletcher wrote a number of posts about debugging C code that bombs, probably because of reference count problems. His approach to debugging this problem seems typical, but I think it's not very productive, so I would like to suggest a different approach. Basically, it's often more productive to read your code carefully and reason about it than it is to use a bunch of generic debugging techniques. (Those techniques are very useful, but only once you've isolated the problem sufficiently.)

Mike writes:

PyErr_Print() let me know that I'm getting a KeyError on the GI of the node (which only appears as the _value_ in any dictionary). So, thinks I (with nudging from Guido) it's a refcount error... so, I forge ahead and say "dang the memory leaks", adding Py_INCREF's everywhere. No go :( Exactly the same behaviour.

Hmm... This sounds like using an automatic weapon to kill a mosquito. Know thy enemy before choosing thy weapon. The problem is, of course, with what "everywhere" is. You may easily miss the one crucial place because you didn't think of it.

You should start by re-reading section 1.2.1 of the Python/C API manual again, and then carefully read the descriptions of the functions you're calling. (I know, the manual is not complete; but it isn't *that* incomplete, and if you're finding a function that's not in the manual, reading its source usually gives a clue.)

So says I (beginning to talk to self), why not print the environment in which the functions are being run to see what's going on... no sooner said than done. And the error disappeared! Take the printing line out -- error reappears (iterate three or four times in disbelief).

This is a typical example of Heisenberg's law applied to programs: you can't observe something without influencing it.

I'm using:
printf(" Env as rule called:\n\t%s\n",

This creates a new string object that is never collected: the new string object returned by PyObject_Repr(). Since this is presumably a big string and you are allocating a lot of them (one each time you get to this print statement) the malloc pattern of your application becomes very different and this means that you may see very different behavior.

So, (maybe from shock), I eliminate the Py_INCREFs and try with just the printing... still works perfectly (save that I'm printing the entire parse tree on every iteration of the while loop (which isn't good...)).

Apparently, the INCREFs you added don't change your program's allocation behavior -- so obviously they aren't in the right places. This is confirmed by what you said earlier: adding the INCREF calls didn't remove the problem.

So, my questions of the hour:

1) What's the c api equiv for sys.refcount? (so I can watch refcounts across calls and determine which are reference neutral)

(Echoed by Mark Hammond, who believes the reference count is the first 2 bytes of the object -- in fact, it is the first 4 bytes, and this reveals that he is working in a little-endian machine, otherwise he would have said it's the 3rd and 4th byte. :-)

The reference count is the ob_refcnt field. But I don't think this will help you a lot. If the reference count of an object doesn't change during a call, that doesn't mean the call is reference count-neutral -- it could store a copy of the object.

For example, take PyList_SetItem(list, index, item). It doesn't change the reference count of either the list or the item, but it is far from reference count neutral: it is neutral for the list, but it steals a reference from the item, and it expects you to hand it the item with the reference count already incremented. (This particular function and its buddy, PyTuple_SetItem(), are used most often to initialize lists/tuples with new objects that have been created with an initial reference count of 1, which nicely matches their behavior.)

On the other hand, PySequence_SetItem(list, index, item) *does* increment the reference count of the item. And it is considered reference count neutral. (But it doesn't work for tuples, which are immutable; this is why you need PyTuple_SetItem().)

2) What the heck is going on with printing? Am I somehow saving the object from ignoble destruction by calling repr on it just before I need it? Could this be a problem with refcounting objects inserted in the dictionary (doesn't seem likely given that PyDict_SetItem is said to store it's own references to objects).

As I said, it's not the printing, it's the repr() call. I don't expect repr() to save a reference to your object, unless you implemented the object type yourself (then it could be a bug in your tp_repr or tp_str function).

3) Anyone else becoming _really_ interested in a bytecode-to-C translator (as discussed a while ago on the list) :)

[Unfortunately, that's not going to help you as much as you'd like, because of Python's dynamic nature. E.g. for the expression "a+b" it will have to generate a call to PyNumber_Add(a, b) because it can't know the types of a and b without a *lot* (and a mean a LOT) of type inferencing effort.]

Later, Mike writes:

Okay, trying to debug this weird stack corruption thing, I thought along the lines of:

1) Stacks should only be corrupted if an object is decref'd which shouldn't be, or an object is created without a reference to begin with?

No -- corrupt stacks can also come from the use of uninitialized pointer variables, or out-of bounds indexing. There could be some pretty subtle off-by-one errors in your code!

2) You only need to decref objects if you're worried about memory leaks, since I'm just debugging, I'm not worried right now

You're doing yourself a big disfavor here. Sure, core dumps are more serious problems than memory leaks, but memory leaks aren't any easier to find -- in fact, they are probably harder to find, because they hide in otherwise perfectly working code. A memory leak that happens to be triggered in a loop can grow your memory so fast that you have no choice but start debugging right there!

The proper approach is to try and make sure that you have the right INCREF and DECREF calls at each place -- and the only way to go is to know (from the manual) the reference count behavior of each function you call (including functions you wrote yourself!).

3) If I comment out all the DECREF calls, I should only have to worry about objects that I've created which don't have reference counts? So, if I add an incref everywhere a new object is created, I should have a huge memory leak, but no stack corruption.

No, that's not how it works. When an object is created, it already comes with a reference count of one. The API manual says of this situation that you "own" a reference. (You don't own the object -- it may be shared. E.g. small integers and short strings are cached and shared aggressively -- but that doesn't affect whether you own a reference to them.) Many routines that extract objects from other objects also give you the responsibility of owning a reference to the object, e.g. PyObject_GetAttr() and PyObject_GetItem().

On the other hand (and these are the most common examples, but not the only ones), PyList_GetItem(), PyTuple_GetItem(), PyDict_GetItem() and PyDict_GetItemString() all return to you an object without ownership of a reference to the object. This is called a "borrowed" reference. When you pass a borrowed reference to another call that expects you to INCREF its argument (like PyList_SetItem() discussed above), you have a problem.

I suspect that the cause of your problem might be one of these cases, but since you won't post your code I can't be of much more help here -- I don't even know which functions you are calling. Perhaps you could compile a list of Py* functions you are calling and any questions you have regarding their reference count behavior after looking them up in the manual?

Of course, this didn't work or I wouldn't be bothering everyone with it. Am now breaking the thing down into smaller functions to see if that will help in tracking down the error (though it will almost certainly slow the function down). Is there a FAQ on reference-counting woes somewhere?

There's really nothing that replace understanding the reference count behavior of each function you're using. The Python/C API manual is your friend. (And I promise to fix it when you find specific information missing or hard to find.)