Compatibility with the Array API standard #7848

TomNicholas · 2023-05-18T20:34:43Z

What is your issue?

Meta-issue to track all the smaller issues around making xarray and the array API standard compatible with each other.

We've already had

and there will likely be many others.

I suspect this might require changes to the standard as well as to xarray - in particular see this list of common numpy functions which are not currently in the array API standard. Of these xarray currently uses (FYI @ralfgommers ):

np.clip
np.diff
np.pad
np.repeat
~~np.take~~
~~np.tile~~

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TomNicholas · 2023-05-18T20:36:35Z

np.pad is an interesting example in the context of chunked arrays (xref #6807) - dask implements it (parallelized using various approaches, including map_blocks internally), but cubed currently doesn't implement it because it's not part of the array API standard. (cc @tomwhite)

shoyer · 2023-08-14T21:43:13Z

Probably the most important missing feature for Xarray in the Array API standard is support for indexing with integer arrays: data-apis/array-api#669

kgryte · 2023-10-19T03:20:15Z

Quick update from the Array API standard, both take and tile have been merged in the standard. take is available as of the 2022.12 revision and tile will be included in the 2023 revision.

TomNicholas · 2023-10-19T21:51:37Z

Thanks @kgryte! We also just bumped our minimum-supported numpy to 1.22 (#8346), which means we could potentially start using numpy.array_api internally for various operations...

allen-adastra · 2024-05-19T00:40:17Z

Hi! I'm wondering if this is still a concerted effort? Def would love to see tight Xarray <-> Jax integration.

keewis · 2024-05-19T11:02:23Z

I don't have any knowledge about jax specifically, but packages like cubed have been wrapped successfully by xarray using the existing Array API support. So I'd say that as long as jax fully supports the Array API, you should be able to do this already. If not, could you please open a dedicated issue?

There might be a few operations that are needlessly verbose, though, so someone would have to create a integration package (jax-xarray or xarray-jax, not sure).

TomNicholas · 2024-05-19T18:22:34Z

Hey @allen-adastra, great to see you here.

Wrapping JAX with Xarray is a great idea in need of a champion / beta-tester. As Justus says it should just work, but most of the Xarray devs concentrate on making the high-level wrappers compliant, rather than exposing all the edge cases the specific libraries.

If you or one of your colleagues wants to just have a go and report back what happens that would be a great start!

allen-adastra · 2024-05-19T21:19:33Z

Gotcha, so it sounds like Xarray should have compliance with the Array API?

Probably too much for me to take on, but going to talk around to see if we can drum up sufficient support somehow...

keewis · 2024-05-19T22:01:29Z

so it sounds like Xarray should have compliance with the Array API?

at least as far as dispatching to wrapped arrays goes, yes. The full Array API is not a good fit for xarray's objects and adopting just a subset is not allowed, so we don't implement it for those.

TomNicholas · 2024-05-20T14:29:58Z

To clarify, xarray aims to be able to wrap any array-API compliant object. This should already work, and is already being used to wrap some array types successfully (e.g. cubed), but it's likely that trying this with JAX will turn up some corner cases.

@keewis I think is referring to the separate idea of xarray objects (e.g. xr.Variable) following the array API, (as wrappable objects rather than wrappers), which we decided against.

So all I'm suggesting that you guys do @allen-adastra is try wrapping a JAX array with xarray and see what happens, and report back any failures. I'm not suggesting you take on the task of making xarray work for any array-API compliant object (as we're already trying to do that for the general case).

mjwillson · 2024-08-30T11:23:19Z

Hiya, since jax now supports the array-API I've been playing around with it a bit. Mostly things work well, but one thing that seems missing is a nice way to use ufuncs specific to the array API implementation of the wrapped array.

I don't think this is necessarily specific to JAX, but I'll use JAX to illustrate it:

import xarray 
import jax.numpy as jnp
import numpy as np
v = xarray.Variable(('foo', 'bar'), jnp.ones((2,3)))

All goes well so far, but what if we want to apply a ufunc like np.exp? Applying np.exp directly will convert the underlying jax array to numpy:

type(np.exp(v).data)
=> numpy.ndarray

Could this be avoided? If xarray is intercepting these ufunc calls from numpy, perhaps it could dispatch them via data.__array_namespace__() ?

Applying the jax equivalent jnp.exp to the xarray just doesn't work:

jnp.exp(v)
=> TypeError: Argument '<xarray.Variable (foo: 2, bar: 3)> ... ' of type <class 'xarray.core.variable.Variable'> is not a valid JAX type.

The following works:

xarray.apply_ufunc(jnp.exp, v)

but is more verbose and requires one to know the implementation of the underlying array in order to pick the right namespace (np vs jnp vs ...). We can work around the 'need to know the implementation' problem with something like:

xarray.apply_ufunc(lambda v: v.__array_namespace__().exp(v), v)

But this is crazy verbose, and doesn't work for numpy < 2. A helper could make it slighly less bad, e.g.:

xarray.apply_api_ufunc(lambda np: np.exp, v)
or
xarray.apply_api_ufunc(lambda x, np: np.exp(x), v)

although still not great. Perhaps a better alternative could be something like

import xarray.ufuncs as xnp
xnp.exp(v)

? This avoids either the need for boilerplate, or any confusion that might arise from e.g. a np.exp(data_array) call being dispatched to a different array API implementation under the hood. But I'm not sure how much work involved.

shoyer · 2024-08-30T15:37:42Z

xarray.ufuncs used to be a thing. We should probably bring it back!

…

On Fri, Aug 30, 2024 at 4:23 AM Matthew ***@***.***> wrote: Hiya, since jax now supports the array-API I've been playing around with it a bit. Mostly things work well, but one thing that seems missing is a nice way to use ufuncs specific to the array API implementation of the wrapped array. I don't think this is necessarily specific to JAX, but I'll use JAX to illustrate it: import xarray import jax.numpy as jnpimport numpy as npv = xarray.Variable(('foo', 'bar'), jnp.ones((2,3))) All goes well so far, but what if we want to apply a ufunc like np.exp? Applying np.exp directly will convert the underlying jax array to numpy: type(np.exp(v).data)=> numpy.ndarray whereas applying the jax equivalent jnp.exp to the xarray just doesn't work: type(jnp.exp(v).data)=> TypeError: Argument '<xarray.Variable (foo: 2, bar: 3)> ... ' of type <class 'xarray.core.variable.Variable'> is not a valid JAX type. The following works: xarray.apply_ufunc(jnp.exp, v) but is more verbose and requires one to know the implementation of the underlying array in order to pick the right namespace (np vs jnp vs ...). We can work around the 'need to know the implementation' problem with something like: xarray.apply_ufunc(lambda v: v.__array_namespace__().exp(v), v) But this is crazy verbose, and doesn't work for numpy < 2. A helper could make it slighly less bad, e.g.: xarray.apply_api_ufunc(lambda np: np.exp, v)orxarray.apply_api_ufunc(lambda x, np: np.exp(x), v) although still not great. Perhaps a better alternative could be something like import xarray.ufuncs as xnp xnp.exp(v) ? — Reply to this email directly, view it on GitHub <#7848 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAJJFVSKQSXETA4VWSS6BITZUBI33AVCNFSM6AAAAAAYG5XI4CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMRQHEYTEMJTGM> . You are receiving this because you commented.Message ID: ***@***.***>

TomNicholas · 2024-08-30T16:06:27Z

If xarray is intercepting these ufunc calls from numpy, perhaps it could dispatch them via data.array_namespace() ?

I think so. I might be ignoring history here but couldn't we make np.exp(v) dispatch to jnp.exp just by changing v.__array_function__ to look in v.data.__array_namespace__?

Not sure if that would be confusing though, to use np.exp to call jnp.exp...

mjwillson · 2024-08-30T16:07:59Z

Now I think of it: this is not really xarray-specific, but I would have expected there to be a canonical 'generic' array API namespace that dispatches based on array_namespace of the arguments, e.g.

import array_api.generic_ufuncs as xnp

xnp.exp(jax_array) -> jax_array
xnp.exp(numpy_array) -> numpy_array

Does anything like this exist, and if not, should it exist? and could xarray piggy-pack off it without implementing the array API for xarray datatypes directly?

[EDIT: to answer my own question, I guess polymorphic dispatch for multiple-argument ufuncs isn't entirely trivial, although still feels like some reasonable heuristics could be implemented or worst-case errors raised if arguments are incompatible]

mjwillson · 2024-08-30T19:18:43Z

Yeah I'd be happy with this solution too. In that case, when dispatching you might have to special-case versions of numpy that don't directly implement the array API (including __array_namespace__).

…

On Fri, 30 Aug 2024, 17:06 Tom Nicholas, ***@***.***> wrote: I might be ignoring history here but couldn't we make np.exp(v) dispatch to jnp.exp by changing v.__array__function__ to look in v.data.? Not sure if that would be confusing though, to use np.exp to call jnp.exp ... — Reply to this email directly, view it on GitHub <#7848 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAABDFTA3IX4GZV2LQAMP3LZUCKBRAVCNFSM6AAAAAAYG5XI4CVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMRRG44TINRUG4> . You are receiving this because you commented.Message ID: ***@***.***>

TomNicholas · 2024-08-30T19:57:46Z

FYI @mjwillson you might also be interested in the discussion here about how xarray could call jax's vmap within apply_ufunc.

#9286 (comment)

allen-adastra · 2024-09-07T17:24:40Z

@mjwillson Hello! I'm curious how you're avoiding conversion to a numpy array? Running this:

import xarray 
import jax.numpy as jnp
import numpy as np
v = xarray.Variable(('foo', 'bar'), jnp.ones((2,3)))
print(type(v.data))

Gives <class 'numpy.ndarray'>

allen-adastra · 2024-09-07T17:28:09Z

(on the latest versions of xarray + jax btw)

pip list | grep -E "xarray|jax"
jax               0.4.31
jaxlib            0.4.31
xarray            2024.7.0

TomNicholas · 2024-09-07T18:06:47Z

Hi @allen-adastra ! I suspect that for some reason the JAX array is not making it through the as_compatible_data function that gets called by Variable.__init__, and is being coerced to np.ndarray by a np.asarray call (maybe this one).

This is not supposed to happen if JAX correctly implements __array_namespace__ (see this check), so this might indicate some non-compliance on JAX's part...

EDIT: It looks like __array_namespace__ is defined on the JAX Array class (see https://github.com/google/jax/blob/02b7a767683dd92c7cd3503fbb0d60b2a7440bb9/jax/_src/numpy/array_methods.py#L956), so maybe there is something else going on? But regardless I bet that coercion is happening inside as_compatible_data.

EDIT2: Looks like the Variable wrapping was working okay for @mjwillson above?

TomNicholas · 2024-09-07T19:12:42Z

Going back to @mjwillson 's question in #7848 (comment):

a canonical 'generic' array API namespace that dispatches based on __array_namespace__ of the arguments,

Does anything like this exist, and if not, should it exist? and could xarray piggy-pack off it without implementing the array API for xarray datatypes directly?

xarray.ufuncs used to be a thing. We should probably bring it back!

I just spoke to @lucascolley at the NumFOCUS summit about this, and we decided we agree with @shoyer that we should bring back xarray.ufuncs. We think the syntax convention should be:

import xarray.ufuncs as xu
xu.exp(v)

The reasoning is:

Using np for this would imply a breaking change to the existing behaviour of np.exp(v). It's possible some users are relying on this returning a np.ndarray, so it's be better to have a separate namespace for this.
Using xp for this would get confusing with the array API standard encouraging using xp to mean the entire array API namespace, including creation functions and so on. (It's a little unfortunate that both xarray and the array API standard like to use x-prefixes).
Using np or xnp (or anything with an n) for this would be confusing because numpy is not actually involved at all! We are using an agnostic interface to call a jax function on a jax array.
Using the actual jax.numpy namespace jnp would technically be a violation of the array API standard, which doesn't allow supporting some operations (i.e. ufuncs) but not others on the xarray classes (see also Should NamedArray be interchangeable with other array types? or Should we support the axis kwarg? #8333 and the linked discussion partial adoption of Array API for Xarray's NamedArray data-apis/array-api#698).
Literally using the actual array API namespace convention xp would imply having user code that does import jax.numpy as xp, which we don't want because it's confusing to users. It also would be violating the standard too.
xu is nice because it doesn't have any of those problems, and it still indicates both that its an xarray thing and that it's specifically for ufuncs.

The original xarray.ufuncs module from yesteryear actually had an explicit set of numpy-dispatching functions that were defined, but due to the array API standard we can now just point to that list in our documentation instead. EDIT: Actually there is no list of "ufuncs" in the array API standard, so maybe we do just have to explicitly list which functions we do and don't support.

If anyone wants to take a stab at adding this xarray.ufuncs module it would just have to be defined at the top-level and then examine the wrapped array type .data to decide which namespace to use on it for the particular operation.

allen-adastra · 2024-09-07T19:21:08Z

Hi @allen-adastra ! I suspect that for some reason the JAX array is not making it through the as_compatible_data function that gets called by Variable.__init__, and is being coerced to np.ndarray by a np.asarray call (maybe this one).

This is not supposed to happen if JAX correctly implements __array_namespace__ (see this check), so this might indicate some non-compliance on JAX's part...

EDIT: It looks like __array_namespace__ is defined on the JAX Array class (see https://github.com/google/jax/blob/02b7a767683dd92c7cd3503fbb0d60b2a7440bb9/jax/_src/numpy/array_methods.py#L956), so maybe there is something else going on? But regardless I bet that coercion is happening inside as_compatible_data.

EDIT2: Looks like the Variable wrapping was working okay for @mjwillson above?

Ah, found the issue. __array_namespace__ is introduced in 0.4.32 https://jax.readthedocs.io/en/latest/jax.numpy.html#python-array-api-standard which is not yet in PyPi, so this will work intended once 0.4.32 is out https://github.com/google/jax/releases

allen-adastra · 2024-09-07T21:40:47Z

For now, I implemented some small hacks in a fork, and am getting somewhere experimenting with a Xarray + JAX integration (experimental repo).

Probably best to continue a more in depth convo in the existing thread, but @mjwillson I'd be curious to hear your thoughts on the "dummy pytrees" issue I mention here

lucascolley · 2024-09-07T21:55:28Z

In case anyone is interested in using JAX<0.4.32 for some reason, it would be possible to modify the xarray logic to use (or just copy from) array-api-compat's array_namespace: https://github.com/data-apis/array-api-compat/blob/main/array_api_compat/common/_helpers.py#L529-L543, which basically amounts to using jax.experimental.array_api instead.

shoyer · 2024-09-10T01:01:41Z

Now I think of it: this is not really xarray-specific, but I would have expected there to be a canonical 'generic' array API namespace that dispatches based on array_namespace of the arguments, e.g.
import array_api.generic_ufuncs as xnp

xnp.exp(jax_array) -> jax_array
xnp.exp(numpy_array) -> numpy_array
Does anything like this exist, and if not, should it exist? and could xarray piggy-pack off it without implementing the array API for xarray datatypes directly?

The problem is that Xarray objects shouldn't implement __array_namespace__, because they are not fully array API compliant. We talked about this pretty extensively in data-apis/array-api#698

mjwillson · 2024-09-16T16:27:27Z

If anyone wants to take a stab at adding this xarray.ufuncs module it would just have to be defined at the top-level and then examine the wrapped array type .data to decide which namespace to use on it for the particular operation.

I would have a go at this, although it looks like it would be easier for someone with more context about the old ufuncs.py. The old version contains some multi-argument dispatch logic which I'm not sure if you'd want to preserve or not, for example. And it goes via _unary_op and _binary_op, so not clear to me if the dispatch based on underlying array type should live there, or in ufuncs.py. If it lives in _unary_op/_binary_op, could that be disentangled from the dispatch logic in ufuncs.py, etc.

mjwillson · 2024-09-17T10:10:20Z

Alternatively would ufuncs.py now be better implemented just as thin wrappers around apply_ufunc?

shoyer · 2024-09-17T14:27:56Z

Alternatively would ufuncs.py now be better implemented just as thin wrappers around apply_ufunc?

Yes, I would lean towards this. If I recall correctly, xarray.ufuncs pre-dated apply_ufunc.

Small change that adds the `__array_namespace__` method from the [Array API standard](https://data-apis.org/array-api/2022.12/API_specification/generated/array_api.array.__array_namespace__.html) to autograd's `ArrayBox`. This makes autograd (partially) compatible with libraries that check for array API compatibility, such as [xarray](https://github.com/pydata/xarray), because xarray will coerce types that do not implement the array API into regular numpy arrays, see relevant discussion [here](pydata/xarray#7848). The following code snippet: ```python import xarray as xr import autograd.numpy as np from autograd import grad def f(x): data = xr.DataArray(x, dims=range(x.ndim)).data print(data) return np.sum(data) x = np.random.uniform(-1, 1, 10) grad(f)(x) ``` prints ``` [<autograd.numpy.numpy_boxes.ArrayBox object at 0x7f9465347640> <autograd.numpy.numpy_boxes.ArrayBox object at 0x7f94652f7000> <autograd.numpy.numpy_boxes.ArrayBox object at 0x7f94652f7c40>] ``` currently, because the traced `ArrayBox` is being coerced into a regular numpy array of `dtype=object` with `ArrayBox` elements. Differentiating through this works (with some limitations), but is very inefficient. With the addition suggested in this PR, the snippet instead prints: ``` Autograd ArrayBox with value [1. 1. 1.] ``` i.e. `data` is now a "proper" `ArrayBox`. I might also be interested in working on a full xarray wrapper in the future, but for now just this change would already help a lot. --------- Co-authored-by: Fabian Joswig <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

slevang · 2024-11-13T14:21:21Z

I ran into the ufuncs issue trying to do basic ops on objects containing jax arrays. Opened #9776 for discussion.

lucascolley · 2024-11-18T20:34:47Z

might be good to reopen this for further discussion

slevang · 2024-11-18T21:20:13Z

Opened #9798 as another follow up, curious to get feedback.

TomNicholas added API design upstream issue topic-arrays related to flexible array support topic-chunked-arrays Managing different chunked backends, e.g. dask labels May 18, 2023

TomNicholas mentioned this issue May 31, 2023

Useful functions not in the Array API Standard cubed-dev/cubed#193

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jax and xarray integration for automatic differentiation? jax-ml/jax#17107

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TomNicholas added the array API standard Support for the Python array API standard label Jan 25, 2024

jthielen mentioned this issue Aug 28, 2024

Consistent Handling of Type Casting Hierarchy #3950

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Array API standard compatibility PyDMD/PyDMD#545

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yaugenst-flex mentioned this issue Oct 10, 2024

Add __array_namespace__ to ArrayBox HIPS/autograd#647

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Namespace-aware xarray.ufuncs #9776

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Improved duck array wrapping #9798

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Compatibility with the Array API standard #7848

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Compatibility with the Array API standard #7848

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