It is pretty weird that these two statements don't necessarily produce the same result:
someufunc(*inputs, out=out_arr) out_arr[...] = someufunc(*inputs) On Fri, Sep 27, 2019, 15:02 Sebastian Berg <sebast...@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. > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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