That's a lot of very good questions! Let me see if I can answer them
one-by-one.
On 06.09.19 09:49, Nathaniel Smith wrote:
Ah, whoops, I definitely missed that :-). That does change things!
So one of the major decision points for any duck-array API work, is
whether to modify the numpy semantics "in place", so user code
automatically gets access to the new semantics, or else to make a new
namespace, that users have to switch over to manually.
The major disadvantage of doing changes "in place" is, of course, that
we have to do all this careful work to move incrementally and make
sure that we don't break things. The major (potential) advantage is
that we have a much better chance of moving the ecosystem with us.
The major advantage of making a new namespace is that it's *much*
easier to experiment, because there's no chance of breaking any
projects that didn't opt in. The major disadvantage is that numpy is
super strongly entrenched, and convincing every project to switch to
something else is incredibly difficult and costly. (I just searched
github for "import numpy" and got 17.7 million hits. That's a lot of
imports to update!) Also, empirically, we've seen multiple projects
try to do this (e.g. DyND), and so far they all failed.
It sounds like unumpy is an interesting approach that hasn't been
tried before – in particular, the promise that you can "just switch
your imports" is a much easier transition than e.g. DyND offered. Of
course, that promise is somewhat undermined by the reality that all
these potential backend libraries *aren't* 100% compatible with numpy,
and can't be...
This is true, however, with minor adjustments it should be possible to
make your code work across backends, if you don't use a few obscure
parts of NumPy.
it might turn out that this ends up like asanyarray,
where you can't really use it reliably because the thing that comes
out will generally support *most* of the normal ndarray semantics, but
you don't know which part. Is scipy planning to switch to using this
everywhere, including in C code?
Not at present I think, however, it should be possible to "re-write"
parts of scipy on top of unumpy in order to make that work, and where
speed is required and an efficient implementation isn't available in
terms of NumPy functions, make dispatchable multimethods and allow
library authors to provide the said implementations. We'll call this
project uscipy, but that's an endgame at this point. Right now, we're
focusing on unumpy.
If not, then how do you expect
projects like matplotlib to switch, given that matplotlib likes to
pass array objects into scipy functions? Are you planning to take the
opportunity to clean up some of the obscure corners of the numpy API?
That's a completely different thing, and to answer that question
requires a distinction between uarray and unumpy... uarray is a
backend-mechanism, independent of array computing. We hope that
matplotlib will adopt it to switch around it's GUI back-ends for example.
But those are general questions about unumpy, and I'm guessing no-one
knows all the answers yet... and these question actually aren't super
relevant to the NEP. The NEP isn't inventing unumpy. IIUC, the main
thing the NEP is proposes is simply to make "numpy.overridable" an
alias for "unumpy".
It's not clear to me what problem this alias is solving. If all
downstream users have to update their imports anyway, then they can
write "import unumpy as np" just as easily as they can write "import
numpy.overridable as np". I guess the main reason this is a NEP is
because the unumpy project is hoping to get an "official stamp of
approval" from numpy?
That's part of it. The concrete problems it's solving are threefold:
1. Array creation functions can be overridden.
2. Array coercion is now covered.
3. "Default implementations" will allow you to re-write your NumPy
array more easily, when such efficient implementations exist in
terms of other NumPy functions. That will also help achieve similar
semantics, but as I said, they're just "default"...
The import numpy.overridable part is meant to help garner adoption, and
to prefer the unumpy module if it is available (which will continue to
be developed separately). That way it isn't so tightly coupled to the
release cycle. One alternative Sebastian Berg mentioned (and I am on
board with) is just moving unumpy into the NumPy organisation. What we
fear keeping it separate is that the simple act of a pip install unumpy
will keep people from using it or trying it out.
But even that could be accomplished by just
putting something in the docs. And adding the alias has substantial
risks: it makes unumpy tied to the numpy release cycle and
compatibility rules, and it means that we're committing to maintaining
unumpy ~forever even if Hameer or Quansight move onto other things.
That seems like a lot to take on for such vague benefits?
I can assure you Travis has had the goal of "replatforming SciPy" from
as far back as I met him, he's spawned quite a few efforts in that
direction along with others from Quansight (and they've led to nice
projects). Quansight, as I see it, is unlikely to abandon something like
this if it becomes successful (and acceptance of this NEP will be a huge
success story).
On Tue, Sep 3, 2019 at 2:04 AM Hameer Abbasi<einstein.edi...@gmail.com> wrote:
The fact that we're having to design more and more protocols for a lot
of very similar things is, to me, an indicator that we do have holistic
problems that ought to be solved by a single protocol.
But the reason we've had trouble designing these protocols is that
they're each different :-). If it was just a matter of copying
__array_ufunc__ we'd have been done in a few minutes...
uarray borrows heavily from __array_function__. It allows substituting
(for example) __array_ufunc__ by overriding ufunc.__call__, ufunc.reduce
and so on. It takes, as I mentioned, a holistic approach: There are
callables that need to be overriden, possibly with nothing to dispatch
on. And then it builds on top of that, adding coercion/conversion.
-n
--
Nathaniel J. Smith --https://vorpus.org
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