On 05/18/2012 01:48 PM, mark florisson wrote:
On 17 May 2012 23:53, Dag Sverre Seljebotn
wrote: 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).
Well, you have my vote, but you already knew that. I'd also be willing to co-author any PEP etc, but I'm sensing it may be more useful to have support from people from different projects. Personally, I think if this is to succeed, we first need to fix the design to work for subclasses (I think one may just want to memcpy the interface information over to the subclass, e.g. through a convenience function that allows one to add more as well). If we have a solid idea of the technical implementation, we should actually implement it and present the benchmarks, comparing the results to capsules as attributes (and to the _PyType_Lookup approach).
Unless there's any holes in my fresh metaclass implementation, I think that is good enough that we can wait a year and get actual adoption before pushing for a PEP. That would also make the PEP a lot stronger. I do believe it should happen eventually though. Here's my post on the Cython list reposted to this list: """ So I finally got around to implementing this: https://github.com/dagss/pyextensibletype Documentation now in a draft in the NumFOCUS SEP repo, which I believe is a better place to store cross-project standards like this. (The NumPy docstring standard will be SEP 100). https://github.com/numfocus/sep/blob/master/sep200.rst Summary: - No common runtime dependency - 1 ns overhead per lookup (that's for the custom slot *alone*, no fast-callable signature matching or similar) - Slight annoyance: Types that want to use the metaclass must be a PyHeapExtensibleType, to make the binary layout work with how CPython makes subclasses from Python scripts My conclusion: I think the metaclass approach should work really well. """ Dag