[Numpy-discussion] pre-PEP for making creative forking of NumPy less destructive

Dag Sverre Seljebotn d.s.seljebotn at astro.uio.no
Thu May 17 18:53:57 EDT 2012

I'm repeating myself a bit, but my previous thread of this ended up 
being about something else, and also since then I've been on an 
expedition to the hostile waters of python-dev.

I'm crazy enough to believe that I'm proposing a technical solution to 
alleviate the problems we've faced as a community the past year. No, 
this will NOT be about NA, and certainly not governance, but do please 
allow me one paragraph of musings before the meaty stuff.

I believe the Achilles heel of NumPy is the C API and the PyArrayObject. 
The reliance we all have on the NumPy C API means there can in practice 
only be one "array" type per Python process. This makes people *very* 
afraid of creative forking or new competing array libraries (since they 
just can't live in parallel -- like Cython and Pyrex can!), and every 
new feature has to go into ndarray to fully realise itself. This in turn 
means that experimentation with new features has to happen within one or 
a few release cycles, it cannot happen in the wild and by competition 
and by seeing what works over the course of years before finally making 
it into upstream. Finally, if any new great idea can really only be 
implemented decently if it also impacts thousands of users...that's bad 
both for morale and developer recruitment.

The meat:

There's already of course been work on making the NumPy C API work 
through an indirection layer to make a more stable ABI. This is about 
changing the ideas of how that indirection should happen, so that you 
could in theory implement the C API independently of NumPy.

You could for instance make a "mini-NumPy" that only contains the bare 
essentials, and load that in the same process as the real NumPy, and use 
the C API against objects from both libraries.

I'll assume that we can get a PEP through by waving a magic wand, since 
that makes it easier to focus on essentials. There's many ugly or less 
ugly hacks to make it work on any existing CPython [1], and they 
wouldn't be so ugly if there's PEP blessing for the general idea.

Imagine if PyTypeObject grew an extra pointer "tp_customslots", which 
pointed to an array of these:

typedef struct {
     unsigned long tpe_id;
     void *tpe_data;
} PyTypeObjectCustomSlot;

The ID space is partitioned to anyone who asks, and NumPy is given a 
large chunk. To insert a "custom slot", you stick it in this list. And 
you search it linearly for, say, PYTYPE_CUSTOM_NUMPY_SLOT (each type 
will typically have 0-3 entries so the search is very fast).

I've benchmarked something very similar recently, and the overhead in a 
"hot" situation is on the order of 4-6 cycles. (As for cache, you can at 
least stick the slot array right next to the type object in memory.)

Now, a NumPy array would populate this list with 1-2 entries pointing to 
tables of function pointers for the NumPy C API. This lookup through the 
PyTypeObject would in part replace the current import_array() mechanism.

I'd actually propose two such custom slots for ndarray for starters:

  a) One PEP 3118-like binary description that exposes raw data pointers 
(without the PEP 3118 red tape)

  b) A function pointer table for a suitable subset of the NumPy C API 
(obviously not array construction and so on)

The all-important PyArray_DATA/DIMS/... would be macros that try for a) 
first, but fall back to b). Things like PyArray_Check would actually 
check for support of these slots, "duck typing", rather than the Python 
type (of course, this could only be done at a major revision like NumPy 
2.0 or 3.0).

The overhead should be on the order of 5 cycles per C API call. That 
should be fine for anything but the use of PyArray_DATA inside a tight 
loop (which is a bad idea anyway).

For now I just want to establish if there's support for this general 
idea, and see if I can get some weight behind a PEP (and ideally a 
co-author), which would make this a general approach and something more 
than an ugly NumPy specific hack. We'd also have good use for such a PEP 
in Cython (and, I believe, Numba/SciPy in CEP 1000).


[1] There's many ways of doing similar things in current Python, such as 
standardising across many participating projects on using a common 
metaclass. Here's another alternative that doesn't add such 
inter-project dependencies but is more renegade: 

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