Differences between PyPy and CPython

This page documents the few differences and incompatibilities between the PyPy Python interpreter and CPython. Some of these differences are “by design”, since we think that there are cases in which the behaviour of CPython is buggy, and we do not want to copy bugs.

Differences that are not listed here should be considered bugs of PyPy.

Extension modules

List of extension modules that we support:

• Supported as built-in modules (in pypy/module/):

  __builtin__ __pypy__ _ast _codecs _collections _continuation _ffi _hashlib _io _locale _lsprof _md5 _minimal_curses _multiprocessing _random _rawffi _sha _socket _sre _ssl _warnings _weakref _winreg array binascii bz2 cStringIO cmath cpyext crypt errno exceptions fcntl gc imp itertools marshal math mmap operator parser posix pyexpat select signal struct symbol sys termios thread time token unicodedata zipimport zlib

  When translated on Windows, a few Unix-only modules are skipped, and the following module is built instead:

  _winreg

• Supported by being rewritten in pure Python (possibly using cffi): see the lib_pypy/ directory. Examples of modules that we support this way: ctypes, cPickle, cmath, dbm, datetime... Note that some modules are both in there and in the list above; by default, the built-in module is used (but can be disabled at translation time).

The extension modules (i.e. modules written in C, in the standard CPython) that are neither mentioned above nor in lib_pypy/ are not available in PyPy. (You may have a chance to use them anyway with cpyext.)

Differences related to garbage collection strategies

The garbage collectors used or implemented by PyPy are not based on reference counting, so the objects are not freed instantly when they are no longer reachable. The most obvious effect of this is that files are not promptly closed when they go out of scope. For files that are opened for writing, data can be left sitting in their output buffers for a while, making the on-disk file appear empty or truncated. Moreover, you might reach your OS’s limit on the number of concurrently opened files.

Fixing this is essentially impossible without forcing a reference-counting approach to garbage collection. The effect that you get in CPython has clearly been described as a side-effect of the implementation and not a language design decision: programs relying on this are basically bogus. It would anyway be insane to try to enforce CPython’s behavior in a language spec, given that it has no chance to be adopted by Jython or IronPython (or any other port of Python to Java or .NET).

Even the naive idea of forcing a full GC when we’re getting dangerously close to the OS’s limit can be very bad in some cases. If your program leaks open files heavily, then it would work, but force a complete GC cycle every n’th leaked file. The value of n is a constant, but the program can take an arbitrary amount of memory, which makes a complete GC cycle arbitrarily long. The end result is that PyPy would spend an arbitrarily large fraction of its run time in the GC — slowing down the actual execution, not by 10% nor 100% nor 1000% but by essentially any factor.

To the best of our knowledge this problem has no better solution than fixing the programs. If it occurs in 3rd-party code, this means going to the authors and explaining the problem to them: they need to close their open files in order to run on any non-CPython-based implementation of Python.

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Here are some more technical details. This issue affects the precise time at which __del__ methods are called, which is not reliable in PyPy (nor Jython nor IronPython). It also means that weak references may stay alive for a bit longer than expected. This makes “weak proxies” (as returned by weakref.proxy()) somewhat less useful: they will appear to stay alive for a bit longer in PyPy, and suddenly they will really be dead, raising a ReferenceError on the next access. Any code that uses weak proxies must carefully catch such ReferenceError at any place that uses them. (Or, better yet, don’t use weakref.proxy() at all; use weakref.ref().)

There are a few extra implications from the difference in the GC. Most notably, if an object has a __del__, the __del__ is never called more than once in PyPy; but CPython will call the same __del__ several times if the object is resurrected and dies again. The __del__ methods are called in “the right” order if they are on objects pointing to each other, as in CPython, but unlike CPython, if there is a dead cycle of objects referencing each other, their __del__ methods are called anyway; CPython would instead put them into the list garbage of the gc module. More information is available on the blog [1] [2].

Note that this difference might show up indirectly in some cases. For example, a generator left pending in the middle is — again — garbage-collected later in PyPy than in CPython. You can see the difference if the yield keyword it is suspended at is itself enclosed in a try: or a with: block. This shows up for example as issue 736.

Using the default GC (called minimark), the built-in function id() works like it does in CPython. With other GCs it returns numbers that are not real addresses (because an object can move around several times) and calling it a lot can lead to performance problem.

Note that if you have a long chain of objects, each with a reference to the next one, and each with a __del__, PyPy’s GC will perform badly. On the bright side, in most other cases, benchmarks have shown that PyPy’s GCs perform much better than CPython’s.

Another difference is that if you add a __del__ to an existing class it will not be called:

>>>> class A(object):
....     pass
....
>>>> A.__del__ = lambda self: None
__main__:1: RuntimeWarning: a __del__ method added to an existing type will not be called

Even more obscure: the same is true, for old-style classes, if you attach the __del__ to an instance (even in CPython this does not work with new-style classes). You get a RuntimeWarning in PyPy. To fix these cases just make sure there is a __del__ method in the class to start with (even containing only pass; replacing or overriding it later works fine).

Subclasses of built-in types

Officially, CPython has no rule at all for when exactly overridden method of subclasses of built-in types get implicitly called or not. As an approximation, these methods are never called by other built-in methods of the same object. For example, an overridden __getitem__() in a subclass of dict will not be called by e.g. the built-in get() method.

The above is true both in CPython and in PyPy. Differences can occur about whether a built-in function or method will call an overridden method of another object than self. In PyPy, they are generally always called, whereas not in CPython. For example, in PyPy, dict1.update(dict2) considers that dict2 is just a general mapping object, and will thus call overridden keys() and __getitem__() methods on it. So the following code prints 42 on PyPy but foo on CPython:

>>>> class D(dict):
....     def __getitem__(self, key):
....         return 42
....
>>>>
>>>> d1 = {}
>>>> d2 = D(a='foo')
>>>> d1.update(d2)
>>>> print d1['a']
42

Mutating classes of objects which are already used as dictionary keys

Consider the following snippet of code:

class X(object):
    pass

def __evil_eq__(self, other):
    print 'hello world'
    return False

def evil(y):
    d = {x(): 1}
    X.__eq__ = __evil_eq__
    d[y] # might trigger a call to __eq__?

In CPython, __evil_eq__ might be called, although there is no way to write a test which reliably calls it. It happens if y is not x and hash(y) == hash(x), where hash(x) is computed when x is inserted into the dictionary. If by chance the condition is satisfied, then __evil_eq__ is called.

PyPy uses a special strategy to optimize dictionaries whose keys are instances of user-defined classes which do not override the default __hash__, __eq__ and __cmp__: when using this strategy, __eq__ and __cmp__ are never called, but instead the lookup is done by identity, so in the case above it is guaranteed that __eq__ won’t be called.

Note that in all other cases (e.g., if you have a custom __hash__ and __eq__ in y) the behavior is exactly the same as CPython.

Ignored exceptions

In many corner cases, CPython can silently swallow exceptions. The precise list of when this occurs is rather long, even though most cases are very uncommon. The most well-known places are custom rich comparison methods (like __eq__); dictionary lookup; calls to some built-in functions like isinstance().

Unless this behavior is clearly present by design and documented as such (as e.g. for hasattr()), in most cases PyPy lets the exception propagate instead.

Object Identity of Primitive Values, is and id

Object identity of primitive values works by value equality, not by identity of the wrapper. This means that x + 1 is x + 1 is always true, for arbitrary integers x. The rule applies for the following types:

• int
• float
• long
• complex

This change requires some changes to id as well. id fulfills the following condition: x is y <=> id(x) == id(y). Therefore id of the above types will return a value that is computed from the argument, and can thus be larger than sys.maxint (i.e. it can be an arbitrary long).

Miscellaneous

• Hash randomization (-R) is ignored in PyPy. As documented in http://bugs.python.org/issue14621, some of us believe it has no purpose in CPython either.
• sys.setrecursionlimit(n) sets the limit only approximately, by setting the usable stack space to n * 768 bytes. On Linux, depending on the compiler settings, the default of 768KB is enough for about 1400 calls.
• since the implementation of dictionary is different, the exact number which __hash__ and __eq__ are called is different. Since CPython does not give any specific guarantees either, don’t rely on it.
• assignment to __class__ is limited to the cases where it works on CPython 2.5. On CPython 2.6 and 2.7 it works in a bit more cases, which are not supported by PyPy so far. (If needed, it could be supported, but then it will likely work in many more case on PyPy than on CPython 2.6/2.7.)
• the __builtins__ name is always referencing the __builtin__ module, never a dictionary as it sometimes is in CPython. Assigning to __builtins__ has no effect.
• directly calling the internal magic methods of a few built-in types with invalid arguments may have a slightly different result. For example, [].__add__(None) and (2).__add__(None) both return NotImplemented on PyPy; on CPython, only the latter does, and the former raises TypeError. (Of course, []+None and 2+None both raise TypeError everywhere.) This difference is an implementation detail that shows up because of internal C-level slots that PyPy does not have.
• on CPython, [].__add__ is a method-wrapper, and list.__add__ is a slot wrapper. On PyPy these are normal bound or unbound method objects. This can occasionally confuse some tools that inspect built-in types. For example, the standard library inspect module has a function ismethod() that returns True on unbound method objects but False on method-wrappers or slot wrappers. On PyPy we can’t tell the difference, so ismethod([].__add__) == ismethod(list.__add__) == True.
• the __dict__ attribute of new-style classes returns a normal dict, as opposed to a dict proxy like in CPython. Mutating the dict will change the type and vice versa. For builtin types, a dictionary will be returned that cannot be changed (but still looks and behaves like a normal dictionary).
• PyPy prints a random line from past #pypy IRC topics at startup in interactive mode. In a released version, this behaviour is supressed, but setting the environment variable PYPY_IRC_TOPIC will bring it back. Note that downstream package providers have been known to totally disable this feature.