issue_comments

changing the xarray internals is not too much work: we need to get xarray.core.utils.is_duck_array to return true if the object has either __array_namespace__ or __array_ufunc__ and __array_function__ (or all three) defined, and we'd need a short test demonstrating that objects that implement only __array_namespace__ survive unchanged when wrapped by a xarray object (i.e. something like isinstance(xr.DataArray(pytorch_object).mean().data, pytorch.Tensor)).

We might still be a bit too early with this, though: the PR which adds __array_namespace__ to numpy has not been merged into numpy:main yet.

I don't, unfortunately (there's the partial example in https://github.com/pydata/xarray/issues/3232#issuecomment-769789746, though).

This is nothing usable right now, but the pytorch maintainers are currently looking into providing support for __array_namespace__ (NEP47). Once there has been sufficient progress in both numpy and pytorch we don't have to change much in xarray (i.e. allowing __array_namespace__ instead of __array_ufunc__ / _array_function__ for duck arrays) to make this work without any wrapper code.

You (or anyone interested) might still want to maintain a "pytorch-xarray" convenience library to allow something like arr.torch.grad(dim="x").

@Duane321: with xarray>=0.17.0 you should be able to remove the __getattributes__ trick.

I can't reproduce that: python In [4]: da.loc["a1"] Out[4]: <xarray.DataArray (b: 2)> tensor([0.4793, 0.7493], dtype=torch.float32) Coordinates: a <U2 'a1' * b (b) <U2 'b1' 'b2' with numpy: 1.19.5 xarray: 0.16.2 pytorch: 1.7.1.post2 pandas: 1.2.1 maybe this is a environment issue?

Edit: the missing feature list includes loc (and sel) because it is currently not possible to have a duck array in a dimension coordinate, so this: python xr.DataArray( [0, 1, 2], coords={"x": XArrayTensor(torch.Tensor([10, 12, 14]))}, dims="x", ).loc[{"x": XArrayTensor(torch.Tensor([10, 14]))}] does not work, but python xr.DataArray( XArrayTensor(torch.Tensor([0, 1, 2])), coords={"x": [10, 12, 14]}, dims="x", ).loc[{"x": [10, 14]}] should work just fine.

I added a torch_array_type to pycompat.py

torch.Tensor defines values, so the issue is this: https://github.com/pydata/xarray/blob/8cc34cb412ba89ebca12fc84f76a9e452628f1bc/xarray/core/variable.py#L221 @shoyer, any ideas?

For now, I guess we can remove it using __getattribute__. With that you will have to cast the data first if you want to access torch.Tensor.values: python torch.Tensor(tensor).values()

Not sure if that's the best way, but that would look like this:

with that, I can execute mean and get back a torch.Tensor wrapped by a DataArray without modifying the xarray code. For a list of features where duck arrays are not supported, yet, see Working with numpy-like arrays (that list should be pretty complete, but if you think there's something missing please open a new issue).

For np.mean(da): be aware that DataArray does not define __array_function__, yet (see #3917), and that with it you have to fall back to np.mean(da, axis=0) instead of da.mean(dim="a").

If the API matching were complete, would the following be possible?

no, it won't be because this is fragile: any new method of DataArray could shadow the methods of the wrapped object. Also, without tight integration xarray does not know what to do with the result, so you would always get the underlying data instead of a new DataArray.

Instead, we recommend extension packages (extending xarray), so with a hypothetical xarray-pytorch library you would write some_sum.torch.backward() instead of some_sum.backward(). That is a bit more work, but it also gives you a lot more control. For an example, see pint-xarray.

Looks like you need to patch that internally just a bit, probably adding pytorch to NON_NUMPY_SUPPORTED_ARRAY_TYPES.

defining __array_function__ (and the other properties listed in the docs) should be enough: https://github.com/pydata/xarray/blob/a0c71c1508f34345ad7eef244cdbbe224e031c1b/xarray/core/variable.py#L232-L235