nogil VS numpy

This might be a dumb question, but why would removing the GIL break FFI? Is it just that existing no-GIL implementations/proposals have discarded/ignored it, or is there a fundamental requirement, e.g. C programs unavoidably interact directly with the GIL? I know that the C-API is only stable between minor releases [0] compiled in the same manner [1], so it's not like the ecosystem is dependent upon it never changing.

I cannot seem to find much discussion about this. I have found a no-GIL interpreter that works with numpy, scikit, etc. [2][3] so it doesn't seem to be a hard limit. (That said, it was not stated if that particular no-GIL implementation requires specially built versions of C-API libs or if it's a drop-in replacement.)

[0]: https://docs.python.org/3/c-api/stable.html#c-api-stability

[1]: https://docs.python.org/3/c-api/stable.html#platform-conside...

[2]: https://github.com/colesbury/nogil

[3]: https://discuss.python.org/t/pep-703-making-the-global-inter...

https://github.com/colesbury/nogil does manage to get rid of the GIL, but it's not certain to make it into Python core. The main problem is the amount of existing libraries that depend on the existence of the GIL without realizing it - breaking those would be extremely disruptive.

CPython is also investigating the removal of the GIL (PEP703, nogil). I think requiring the GIL is a wider thing that libraries will need to address anyway. But also, for the same reason as above I'd be surprised if the Modular team thought that saying "you can run all your python code unchanged" was a good idea if there was a secret "except for code that uses numpy" muttered under the breath.

I'm worried about the speedup

My understanding is that it's based on the most recent attempt to remove the GIL by Sam Gross

https://github.com/colesbury/nogil

In addition to some ways to try to not have nogil have as much overhead he added a lot of unrelated speed improvements so that python without the gil would still be faster not slower in single thread mode. They seem to have merged those performance patches first that means if they add his Gil removal patches in say python 3.12 it will still be substantially slower then 3.11 although faster then 3.10. I hope that doesn't stop them from removing the gil (at least by default)

Looks like it's still active to me:

https://github.com/colesbury/nogil/

I can’t imagine you’ve read the proposal with a comment like this. The interpreter is already patched (twice in the proposal, for two different versions of Python), and Sam Gross has personally already patched many commonly used Python libraries. Here’s numpy patched, a mess of C and Fortran written for high performance code: https://github.com/colesbury/numpy/commits/v1.24.0-nogil

This comment is the definition of FUD.

Numpy.

Here's a patch the author himself wrote to fix a spot where this change break's numpy's thread safety: https://github.com/colesbury/numpy/commit/2ad41a1fb8b0c28fa8...

Maybe that's the only one? Maybe it isn't? But I think the point still stands that people saying this has the potential to break existing Python packages in subtle ways are not just being hyperbolic.

That does not appear to be true. numpy is a heavy user of c extension. The number of changes to make this compatible was like <10 lines. It's these two commits, https://github.com/colesbury/numpy/commit/811868dd47fa8d53cea6c83ee07f6f4da44f041a + https://github.com/colesbury/numpy/commit/c66f8a2e24e7816575c6680bbe070d5ce0c79fa7

Yikes, C extensions can't assume they are under GIL by default:

https://github.com/colesbury/numpy/commits/v1.19.3-nogil