My pull request has been merged into numpy. numpy now uses PyMem_RawMalloc() rather than PyMem_Malloc() since it uses the memory allocator without holding the GIL: https://github.com/numpy/numpy/pull/7404
It was proposed to modify numpy to hold the GIL. Maybe it will be done later.
It means that there are no more C extensions known to not use correctly Python memory allocators. So I pushed my change in CPython to use the pymalloc memory allocator in PyMem_Malloc(): https://hg.python.org/cpython/rev/68b2a43d8653
I documented that porting C extensions to Python 3.6 require to run tests with PYTHONMALLOC=debug. This environment variable enables checks at runtime to validate the usage of Python memory allocators, including checks on the GIL. PYTHONMALLOC=debug and the check on the GIL are new in Python 3.6.
By the way, I modified the code to log the fatal error. if a buffer overflow/underflow is detected in a free function like PyObject_Free() and tracemalloc is enabled, the traceback where the memory block was allocated is now displayed: https://docs.python.org/dev/whatsnew/3.6.html#pythonmalloc-environment-varia...
Moreover, the warning logger now also log where file, socket, etc. were allocated on ResourceWarning: https://docs.python.org/dev/whatsnew/3.6.html#warnings
It looks like Python 3.6 will help developers ;-)
2016-04-20 1:33 GMT+02:00 Victor Stinner firstname.lastname@example.org:
Ping? Is someone still opposed to my change #26249 "Change PyMem_Malloc to use pymalloc allocator"? If no, I think that I will push my change.
My change only changes two lines, so it can be easily reverted before CPython 3.6 if we detect major issues in third-party extensions. And maybe it's better to push such change today to get more time to play with it, than pushing it late in the development of CPython 3.6.
The new PYTHONMALLOC=debug feature allows to quickly and easily check the usage of the PyMem_Malloc() API, even if Python is compiled in release mode.
I checked multiple Python extensions written in C. I only found one bug in numpy and I sent a patch (not merged yet).
2016-03-15 0:19 GMT+01:00 Victor Stinner email@example.com:
2016-02-12 14:31 GMT+01:00 M.-A. Lemburg firstname.lastname@example.org:
If your program has bugs, you can use a debug build of Python 3.5 to detect misusage of the API.
Yes, but people don't necessarily do this, e.g. I have for a very long time ignored debug builds completely and when I started to try them, I found that some of the things I had been doing with e.g. free list implementations did not work in debug builds.
I just added support for debug hooks on Python memory allocators on Python compiled in *release* mode. Set the environment variable PYTHONMALLOC to debug to try with Python 3.6.
I added a check on PyObject_Malloc() debug hook to ensure that the function is called with the GIL held. I opened an issue to add a similar check on PyMem_Malloc(): https://bugs.python.org/issue26563
Yes, but those are part of the stdlib. You'd need to check a few C extensions which are not tested as part of the stdlib, e.g. numpy, scipy, lxml, pillow, etc. (esp. ones which implement custom types in C since these will often need the memory management APIs).
It may also be a good idea to check wrapper generators such as cython, swig, cffi, etc.
I ran the test suite of numpy, lxml, Pillow and cryptography (used cffi).
I found a bug in numpy. numpy calls PyMem_Malloc() without holding the GIL: https://github.com/numpy/numpy/pull/7404
Except of this bug, all other tests pass with PyMem_Malloc() using pymalloc and all debug checks.