Thanks Sebastian, I have your example running and will start experimenting with DType. Lee On Thu, Mar 25, 2021 at 5:32 PM Sebastian Berg <sebastian@sipsolutions.net> 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?).
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 < sebastian@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
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