Den tis 8 nov. 2022 kl 11:44 skrev Sebastian Berg < sebastian@sipsolutions.net>:

On Thu, 2022-11-03 at 11:37 +0100, Oscar Gustafsson wrote:

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Hi all,

I hope this is the correct way to propose a new feature. https://github.com/numpy/numpy/issues/22522

Thanks for the proposal. I don't have much of an opinion on this and right now I am mainly wondering whether there is prior art which can inform us that this is relatively widely useful?

Well, if designing hardware it is typically more useful than decimal rounding. Not sure what to bring up as prior art, but the whole idea is to use it to emulate fixed-point behavior. So quantizing/rounding a floating-point value to the nearest fixed-point value with a given number of fractional bits. bround(0.37, 3) = 0.011 = 0.375. Also, see ac_fixed below which is part of Algorithmic C types, ac_types, https://hlslibs.org/ supported in most high-level synthesis tools, see e.g. https://www.intel.com/content/www/us/en/develop/documentation/oneapi-fpga-op...

Currently, the around-function supports rounding to a given number of

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decimal digits. It is often quite convenient to be able to round to a given number of binary digits to mimic fixed-point representations. Currently this can be readily achieved using e.g.

fractional_bits = 5 scale = 2**fractional_bits x_round = np.around(x*scale)/scale

However, it would be more convenient (and probably faster) to provide dedicated support for it.

This seems more like a a place for some bit-fiddling, but I am not sure. Also a question is whether rounding modes make sense and what you want (truncate, round to even?).

The "correct" way would of course be to do this using integers or ac_fixed https://github.com/hlslibs/ac_types/blob/master/include/ac_fixed.h You are correct that one would like to support different rounding modes (although for fixed-point one usually do not care as much about that as for floating-point, standard "add half LSB" rounding and truncation are typically enough). I didn't want to bring that up at this early stage.

I suspect that this would be more something for a project similar to Warrens ufunclab:

https://github.com/WarrenWeckesser/ufunclab

I.e. written as a NumPy ufunc, but not in NumPy iself.

You may be correct. I'll have a look at that approach for a relatively simple way to obtain it.

Not sure how straightforward it is, but if one could supply the different ac_*-types as custom data types to NumPy that would be a great way of doing it. However, from a quick check it seems like it is only possible to add custom types which are "structs" of existing types. ac_typed are that in some sense (everything is internally represented as integer-types), but probably not close enough for a simple implementation. BR Oscar Gustafsson

- Sebastian

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I see a few different ways to obtain this: 1. Provide a separate function (binaryround?) 2. Provide a base argument to around which defaults to 10. 3. Provide a quant(ization) function where the argument is the step- size. (For completeness, one may think of having multiple quantization modes, not just rounding)

Any opinions?

BR Oscar Gustafsson _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: sebastian@sipsolutions.net

_______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-leave@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: oscar.gustafsson@gmail.com