[Numpy-discussion] UFunc out argument not forcing high precision loop?
Sebastian Berg
sebastian at sipsolutions.net
Fri Sep 27 19:11:07 EDT 2019
On Fri, 2019-09-27 at 15:50 -0700, Nathaniel Smith wrote:
> It is pretty weird that these two statements don't necessarily
> produce the same result:
>
> someufunc(*inputs, out=out_arr)
> out_arr[...] = someufunc(*inputs)
>
Ooopst, fair point. I am not sure I agree, since currently the (mental)
model is typically:
loop_dtype = np.result_type(*arguments)
the question now is, if it is arguments or outputs. However, the oops
is, that I did not realize that right now do – effectively – ignore the
output argument completely for the type resolution. (i.e. I could
probably work with that assumption, without actually breaking
anything.)
- Sebastian
> On Fri, Sep 27, 2019, 15:02 Sebastian Berg <
> sebastian at sipsolutions.net> wrote:
> > On Fri, 2019-09-27 at 11:50 -0700, Sebastian Berg wrote:
> > > Hi all,
> > >
> > > Looking at the ufunc dispatching rules with an `out` argument, I
> > was
> > > a
> > > bit surprised to realize this little gem is how things work:
> > >
> > > ```
> > > arr = np.arange(10, dtype=np.uint16) + 2**15
> > > print(arr)
> > > # array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18], dtype=uint16)
> > >
> >
> > Whoops, copied that print wrong of course.
> >
> > Just to be clear, I personally will consider this an
> > accuracy/precision
> > bug and assume that we can just switch the behaviour failry
> > unceremoniously at some point (and if someone feels that should be
> > a
> > major release, I do not mind).
> > It seems like one of those things that will definitely fix some
> > bugs
> > but could break the odd system/assumption somewhere. Similar to
> > fixing
> > the memory overlap issues.
> >
> > - Sebastian
> >
> >
> > > out = np.zeros(10)
> > >
> > > np.add(arr, arr, out=out)
> > > print(repr(out))
> > > # array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18.])
> > > ```
> > >
> > > This is strictly speaking correct/consistent. What the ufunc
> > tries to
> > > ensure is that whatever the loop produces fits into `out`.
> > > However, I still find it unexpected that it does not pick the
> > full
> > > precision loop.
> > >
> > > There is currently only one way to achieve that, and this by
> > using
> > > `dtype=out.dtype` (or similar incarnations) which specify the
> > exact
> > > dtype [0].
> > >
> > > Of course this is also because I would like to simplify things
> > for a
> > > new dispatching system, but I would like to propose to disable
> > the
> > > above behaviour. This would mean:
> > >
> > > ```
> > > # make the call:
> > > np.add(arr, arr, out=out)
> > >
> > > # Equivalent to the current [1]:
> > > np.add(arr, arr, out=out, dtype=(None, None, out.dtype))
> > >
> > > # Getting the old behaviour requires (assuming inputs have same
> > > dtype):
> > > np.add(arr, arr, out=out, dtypes=arr.dtype)
> > > ```
> > >
> > > and thus force the high precision loop. In very rare cases, this
> > > could
> > > lead to no loop being found.
> > >
> > > The main incompatibility is if someone actually makes use of the
> > > above
> > > (integer over/underflow) behaviour, but wants to store it in a
> > higher
> > > precision array.
> > >
> > > I personally currently think we should change it, but am curious
> > if
> > > we
> > > think that we may be able to get away with an accelerate process
> > and
> > > not a year long FutureWarning.
> > >
> > > Cheers,
> > >
> > > Sebastian
> > >
> > >
> > > [0] You can also use `casting="no"` but in all relevant cases
> > that
> > > should find no loop, since the we typically only have homogeneous
> > > loop
> > > definitions, and
> > >
> > > [1] Which is normally the same as the shorter spelling
> > > `dtype=out.dtype` of course.
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