On Thu, 2021-03-25 at 17:27 -0500, Sebastian Berg wrote: > On Wed, 2021-03-17 at 17:12 -0500, Sebastian Berg wrote: > > On Wed, 2021-03-17 at 07:56 -0500, Lee Johnston wrote: > > <snip> > > > 3. In parallel, I will create a small "toy" DType based on that > > experimental API. Probably in a separate repo (in the NumPy > > organization?). > >
As a small update to the experimental user DTypes. The branches now include the merge of the PR https://github.com/numpy/numpy/pull/18905 which implementes most of NEP 43 to refactor ufuncs and allow new user DTypes for them. (The PR does not cover reductions, so those are also missing here.) That means, the unit dtype's multiplication can be written as `np.multiply(unit_arr, unit_arr)` or just `unit_arr * unit_arr`. And after importing the right experimental module string comparison can use `np.equal` directly. With that, the most central parts of dtypes exists far enough to play around. (For units the simple re-use of existing math functions is missing, though). Cheers, Sebastian > > So this is started. What you need to do right now if you want to try > is > work of this branch in NumPy: > > > https://github.com/numpy/numpy/compare/main...seberg:experimental-dtype-api > > Install NumPy with `NPY_USE_NEW_CASTINGIMPL=1 python -mpip install .` > or your favorite alternative. > (The `NPY_USE_NEW_CASTINGIMPL=1` should be unnecessary very soon, > working of a branch and not "main" will hopefully also be unnecessary > soon.) > > > Then fetch: https://github.com/seberg/experimental_user_dtypes > and install it as well in the same environment. > > > After that, you can jump through the hoop of setting: > > NUMPY_EXPERIMENTAL_DTYPE_API=1 > > And you can enjoy these type of examples (while expecting hard > crashes > when going too far beyond!): > > from experimental_user_dtypes import float64unit as u > import numpy as np > > F = np.array([u.Quantity(70., "Fahrenheit")]) > C = F.astype(u.Float64UnitDType("Celsius")) > print(repr(C)) > # array([21.11111111111115 °C], dtype='Float64UnitDType(degC)') > > m = np.array([u.Quantity(5., "m")]) > m_squared = u.multiply(m, m) > print(repr(m_squared)) > # array([25.0 m**2], dtype='Float64UnitDType(m**2)') > > # Or conversion to SI the long route: > pc = np.arange(5., > dtype="float64").view(u.Float64UnitDType("pc")) > pc.astype(pc.dtype.si()) > # array([0.0 m, 3.085677580962325e+16 m, 6.17135516192465e+16 m, > # 9.257032742886974e+16 m, 1.23427103238493e+17 m], > # dtype='Float64UnitDType(m)') > > > Yes, the code has some horrible hacks around creating the DType, but > the basic mechanism i.e. "functions you need to implement" are not > expected to change lot. > > Right now, it forces you to use and implement the scalar `u.Quantity` > and the code sample uses it. But you can also do: > > np.arange(3.).view(u.Float64UnitDType("m")) > > I do have plans to "not have a scalar" so the 0-D result would still > be > an array. But that option doesn't exist yet (and right now the > scalar > is used for printing). > > > (There is also a `string_equal` "ufunc-like" that works on "S" > dtypes.) > > Cheers, > > Sebastian > > > > PS: I need to figure out some details about how to create DTypes and > DType instances with regards to our stable ABI. The current > "solution" > is some weird subclassing hoops which are probably not good. > > That is painful unfortunately and any ideas would be great :). > Unfortunately, it requires a grasp around the C-API and > metaclassing... > > > > > > > Anyone using the API, should expect bugs, crashes and changes for a > > while. But hopefully will only require small code modifications > > when > > the API becomes public. > > > > My personal plan for a toy example is currently a "scaled integer". > > E.g. a uint8 where you can set a range `[min_double, max_double]` > > that > > it maps to (which makes the DType "parametric"). > > We discussed some other examples, such as a "modernized" rational > > DType, that could be nice as well, lets see... > > > > Units would be a great experiment, but seem a bit complex to me (I > > don't know units well though). So to keep it baby steps :) I would > > aim > > for doing the above and then we can experiment on Units together! > > > > > > Since it came up: I agree that a Python API would be great to > > have. > > It > > is something I firmly kept on the back-burner... It should not be > > very > > hard (if rudimentary), but unless it would help experiments a lot, > > I > > would tend to leave it on the back-burner for now. > > > > Cheers, > > > > Sebastian > > > > > > [1] Maybe a `uint8` storage that maps to evenly spaced values on a > > parametric range `[double_min, double_max]`. That seems like a > > good > > trade-off in complexity. > > > > > > > > > On Tue, Mar 16, 2021 at 4:11 PM Sebastian Berg < > > > sebast...@sipsolutions.net> > > > wrote: > > > > > > > On Tue, 2021-03-16 at 13:17 -0500, Lee Johnston wrote: > > > > > Is the work on NEP 42 custom DTypes far enough along to > > > > > experiment > > > > > with? > > > > > > > > > > > > > TL;DR: Its not quite ready, but if we work together I think we > > > > could > > > > experiment a fair bit. Mainly ufuncs are still limited (though > > > > not > > > > quite completely missing). The main problem is that we need to > > > > find a > > > > way to expose the currently private API. > > > > > > > > I would be happy to discuss this also in a call. > > > > > > > > > > > > ** The long story: ** > > > > > > > > There is one more PR related to casting, for which merge should > > > > be > > > > around the corner. And which would bring a lot bang to such an > > > > experiment: > > > > > > > > https://github.com/numpy/numpy/pull/18398 > > > > > > > > > > > > At that point, the new machinery supports (or is used for): > > > > > > > > * Array-coercion: `np.array([your_scalar])` or > > > > `np.array([1], dtype=your_dtype)`. > > > > > > > > * Casting (practically full support). > > > > > > > > * UFuncs do not quite work. But short of writing `np.add(arr1, > > > > arr2)` > > > > with your DType involved, you can try a whole lot. (see > > > > below) > > > > > > > > * Promotion `np.result_type` should work very soon, but > > > > probably > > > > isn't > > > > is not very relevant anyway until ufuncs are fully > > > > implemented. > > > > > > > > That should allow you to do a lot of good experimentation, but > > > > due > > > > to > > > > the ufunc limitation, maybe not well on "existing" python code. > > > > > > > > > > > > The long story about limitations is: > > > > > > > > We are missing exposure of the new public API. I think I > > > > should > > > > be > > > > able to provide a solution for this pretty quickly, but it > > > > might > > > > require working of a NumPy branch. (I will write another email > > > > about > > > > it, hopefully we can find a better solution.) > > > > > > > > > > > > Limitations for UFuncs: UFuncs are the next big project, so to > > > > try > > > > it > > > > fully you will need some patience, unfortunately. > > > > > > > > But, there is some good news! You can write most of the > > > > "ufunc" > > > > already, you just can't "register" it. > > > > So what I can already offer you is a "DType-specific UFunc", > > > > e.g.: > > > > > > > > unit_dtype_multiply(np.array([1.], > > > > dtype=Float64UnitDType("m")), > > > > np.array([2.], > > > > dtype=Float64UnitDtype("s"))) > > > > > > > > And get out `np.array([2.], dtype=Float64UnitDtype("m s"))`. > > > > > > > > But you can't write `np.multiple(arr1, arr2)` or `arr1 * arr2` > > > > yet. > > > > Both registration and "promotion" logic are missing. > > > > > > > > I admit promotion may be one of the trickiest things, but > > > > trying > > > > this a > > > > bit might help with getting a clearer picture for promotion as > > > > well. > > > > > > > > > > > > The main last limitation is that I did not replace or create > > > > "fallback" > > > > solutions and/or replacement for the legacy `dtype->f-><slots>` > > > > yet. > > > > This is not a serious limitation for experimentation, though. > > > > It > > > > might > > > > even make sense to keep some of them around and replace them > > > > slowly. > > > > > > > > > > > > And of course, all the small issues/limitations that are not > > > > fixed > > > > because nobody tried yet... > > > > > > > > > > > > > > > > I hope this doesn't scare you away, or at least not for long > > > > :/. > > > > It > > > > could be very useful to start experimentation soon to push > > > > things > > > > forward a bit quicker. And I really want to have at least an > > > > experimental version in NumPy 1.21. > > > > > > > > Cheers, > > > > > > > > Sebastian > > > > > > > > > > > > > Lee > > > > > _______________________________________________ > > > > > 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 > > > > > > > _______________________________________________ > > > 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 > > _______________________________________________ > 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
