One of purposes of the STACK_GLOBAL opcode introduced in pickle protocol 4 is to avoid repeating module name for different globals of the same module.

>>> pickletools.dis(pickletools.optimize(pickle.dumps([sys.getsizeof, sys.intern], 4)))
    0: \x80 PROTO      4
    2: \x95 FRAME      33
   11: ]    EMPTY_LIST
   12: (    MARK
   13: \x8c     SHORT_BINUNICODE 'sys'
   18: \x94     MEMOIZE    (as 0)
   19: \x8c     SHORT_BINUNICODE 'getsizeof'
   30: \x93     STACK_GLOBAL
   31: h        BINGET     0
   33: \x8c     SHORT_BINUNICODE 'intern'
   41: \x93     STACK_GLOBAL
   42: e        APPENDS    (MARK at 12)
   43: .    STOP
highest protocol among opcodes = 4

But this doesn't work with the itertools module.

>>> pickletools.dis(pickletools.optimize(pickle.dumps([itertools.chain, itertools.accumulate], 4)))
    0: \x80 PROTO      4
    2: \x95 FRAME      47
   11: ]    EMPTY_LIST
   12: (    MARK
   13: \x8c     SHORT_BINUNICODE 'itertools'
   24: \x8c     SHORT_BINUNICODE 'chain'
   31: \x93     STACK_GLOBAL
   32: \x8c     SHORT_BINUNICODE 'itertools'
   43: \x8c     SHORT_BINUNICODE 'accumulate'
   55: \x93     STACK_GLOBAL
   56: e        APPENDS    (MARK at 12)
   57: .    STOP
highest protocol among opcodes = 4

That is because the __module__ attribute of itertools members is not interned.

>>> sys.getsizeof.__module__ is sys.intern.__module__
True
>>> itertools.chain.__module__ is itertools.chain.__module__
False

In addition to inefficient pickle this perhaps leads to small performance hit on accessing the __module__ attribute or using its value as dictionary key.

Actually this is not specific to itertools. Every non-heap class not from the builtins module is affected.

Proposed patch just interns the __module__ value. The patch also cleans up the code.

Interning the __module__ string causes small performance hit:

$ ./python -m timeit -s "from itertools import chain" -- "chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__; chain.__module__"

Unpatched:  1.93 usec per loop
Patched:    4.09 usec per loop

This can be avoided if cache created string in type's __dict__. Following patch makes __module__ retrieving for non-heap types as fast as for heap types:

Patched2:   0.871 usec per loop

Could anyone please make a review?

I don't understand this code:
   type->tp_dict && PyDict_Check(type->tp_dict)
since the code explicitly assume it's not NULL and access it as a dict earlier in the function

Good catch Benjamin! There is yet one bug -- the type type already has the __module__ attribute, but it is a descriptor, not a string.

Updated patch fixes these bugs.

I don't understand why you need to check the validity of tp_dict at all. We generally assume it's a dict.

Indeed, the PyDict_Check() check can be omitted.

I don't think it can be NULL either.

On Wed, Sep 7, 2016, at 11:36, Serhiy Storchaka wrote:
> 
> Serhiy Storchaka added the comment:
> 
> Indeed, the PyDict_Check() check can be omitted.
> 
> ----------
> 
> _______________________________________
> Python tracker <report@bugs.python.org>
> <http://bugs.python.org/issue25856>
> _______________________________________

It can be NULL in very rare cases. See for example issue26906.

I think it's better not write into the type dict in a getter. You might just use PyUnicode_InternFromString every time.

FWIW, __name__ also has this behavior.

This is what my first path does. But this slows down retrieving the __module__ attribute (from 0.2 usec to 0.4 usec on my computer). Maybe I haven't bother.

Interning __name__ and __qualname__ is less important, because different functions and classes usually have different names.

I'm not too worried about slowing down __module__ especially since it's
not any slower for heap types or types in builtins.

On Thu, Sep 8, 2016, at 14:39, Serhiy Storchaka wrote:
> 
> Serhiy Storchaka added the comment:
> 
> This is what my first path does. But this slows down retrieving the
> __module__ attribute (from 0.2 usec to 0.4 usec on my computer). Maybe I
> haven't bother.
> 
> Interning __name__ and __qualname__ is less important, because different
> functions and classes usually have different names.
> 
> ----------
> 
> _______________________________________
> Python tracker <report@bugs.python.org>
> <http://bugs.python.org/issue25856>
> _______________________________________

New changeset 861ddad3e0c1 by Serhiy Storchaka in branch 'default':
Issue #25856: The __module__ attribute of extension classes and functions
https://hg.python.org/cpython/rev/861ddad3e0c1

Thank you for helpful review Benjamin.