[Cython] buffer syntax vs. memory view syntax
Dag Sverre Seljebotn
d.s.seljebotn at astro.uio.no
Tue May 8 09:57:44 CEST 2012
On 05/07/2012 11:21 PM, mark florisson wrote:
> On 7 May 2012 19:40, Dag Sverre Seljebotn<d.s.seljebotn at astro.uio.no> wrote:
>> mark florisson<markflorisson88 at gmail.com> wrote:
>>> On 7 May 2012 17:00, Dag Sverre Seljebotn<d.s.seljebotn at astro.uio.no>
>>>> On 05/07/2012 04:16 PM, Stefan Behnel wrote:
>>>>> Stefan Behnel, 07.05.2012 15:04:
>>>>>> Dag Sverre Seljebotn, 07.05.2012 13:48:
>>>>>>> BTW, with the coming of memoryviews, me and Mark talked about just
>>>>>>> deprecating the "mytype[...]" meaning buffers, and rather treat it
>>>>>>> np.ndarray, array.array etc. being some sort of "template types".
>>>>>>> we disallow "object[int]" and require some special declarations in
>>>>>>> relevant pxd files.
>>>>>> Hmm, yes, it's unfortunate that we have two different types of
>>>>>> one that declares the item type before the brackets and one that
>>>>>> it afterwards.
>>>>> I actually think this merits some more discussion. Should we
>>> consider the
>>>>> buffer interface syntax deprecated and focus on the memory view
>>>> I think that's the very-long-term intention. Then again, it may be
>>> too early
>>>> to really tell yet, we just need to see how the memory views play out
>>>> real life and whether they'll be able to replace np.ndarray[double]
>>>> real users. We don't want to shove things down users throats.
>>>> But the use of the trailing- syntax needs some cleaning up. Me and
>>>> agreed we'd put this proposal forward when we got around to it:
>>>> - Deprecate the "object[double]" form, where [dtype] can be stuck on
>>>> extension type
>>>> - But, do NOT (for the next year at least) deprecate
>>>> array.array[double], etc. Basically, there should be a magic flag in
>>>> extension type declarations saying "I can be a buffer".
>>>> For one thing, that is sort of needed to open up things for templated
>>>> classes/fused types cdef classes, if that is ever implemented.
>>> Deprecating is definitely a good start. I think at least if you only
>>> allow two types as buffers it will be at least reasonably clear when
>>> one is dealing with fused types or buffers.
>>> Basically, I think memoryviews should live up to demands of the users,
>>> which would mean there would be no reason to keep the buffer syntax.
>> But they are different approaches -- use a different type/API, or just try to speed up parts of NumPy..
>>> One thing to do is make memoryviews coerce cheaply back to the
>>> original objects if wanted (which is likely). Writting
>>> np.asarray(mymemview) is kind of annoying.
>> It is going to be very confusing to have type(mymemview), repr(mymemview), and so on come out as NumPy arrays, but not have the full API of NumPy. Unless you auto-convert on getattr to...
> Yeah, the idea is as very simple, as you mention, just keep the object
> around cached, and when you slice construct one lazily.
>> If you want to eradicate the distinction between the backing array and the memory view and make it transparent, I really suggest you kick back alive np.ndarray (it can exist in some 'unrealized' state with delayed construction after slicing, and so on). Implementation much the same either way, it is all about how it is presented to the user.
> You mean the buffer syntax?
>> Something like mymemview.asobject() could work though, and while not much shorter, it would have some polymorphism that np.asarray does not have (based probably on some custom PEP 3118 extension)
> I was thinking you could allow the user to register a callback, and
> use that to coerce from a memoryview back to an object (given a
> memoryview object). For numpy this would be np.asarray, and the
> implementation is allowed to cache the result (which it will).
> It may be too magicky though... but it will be convenient. The
> memoryview will act as a subclass, meaning that any of its methods
> will override methods of the converted object.
My point was that this seems *way* to magicky.
Beyond "confusing users" and so on that are sort of subjective, here's a
fundamental problem for you: We're making it very difficult to
type-infer memoryviews. Consider:
cdef double[:] x = ...
y = x
Now, because y is not typed, you're semantically throwing in a
conversion on line 2, so that line 3 says that you want the attribute
access to be invoked on "whatever object x coerced back to". And we have
no idea what kind of object that is.
If you don't transparently convert to object, it'd be safe to
automatically infer y as a double[:].
On a related note, I've said before that I dislike the notion of
cdef double[:] mview = obj
I'd rather like
cdef double[:] mview = double[:](obj)
I support Robert in that "np.ndarray[double]" is the syntax to use when
you want this kind of transparent "be an object when I need to and a
memory view when I need to".
1) We NEVER deprecate "np.ndarray[double]", we commit to keeping that
in the language. It means exactly what you would like double[:] to mean,
i.e. a variable that is memoryview when you need to and an object
otherwise. When you use this type, you bear the consequences of
early-binding things that could in theory be overridden.
2) double[:] is for when you want to access data of *any* Python
object in a generic way. Raw PEP 3118. In those situations, access to
the underlying object is much less useful.
2a) Therefore we require that you do "mview.asobject()" manually;
doing "mview.foo()" is a compile-time error
2b) To drive the point home among users, and aid type inference and
overall language clarity, we REMOVE the auto-acquisition and require
that you do
cdef double[:] mview = double[:](obj)
2c) Perhaps: Do not even coerce to a Python memoryview and disallow
"print mview"; instead require that you do "print mview.asmemoryview()"
or "print memoryview(mview)" or somesuch.
(A related proposal that's been up earlier has been that a variable can
be annotated with many interfaces; e.g.
cdef A|B|C obj
...and then when you do "obj.method", it is first looked up in C, then
B, then A, then Python getattr. Not sure if we want to reopen that can
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