Rolling operations with numbagg produce invalid values after numpy.inf

What is your issue?

If an array contains np.inf and a rolling operation is applied, all values after this one are nan if numbagg is used. Take the following example:

import xarray as xr
import numpy as np
xr.set_options(use_numbagg=False)
da=xr.DataArray([1,2,3,np.inf,4,5,6,7,8,9,10], dims=['x'])
da.rolling(x=2).sum()

Output

<xarray.DataArray (x: 11)> Size: 88B
array([nan,  3.,  5., inf, inf,  9., 11., 13., 15., 17., 19.])
Dimensions without coordinates: x

With Numbagg:

xr.set_options(use_numbagg=True)
da=xr.DataArray([1,2,3,np.inf,4,5,6,7,8,9,10], dims=['x'])
print(da.rolling(x=2).sum())

Output

<xarray.DataArray (x: 11)> Size: 88B
array([nan,  3.,  5., inf, inf, nan, nan, nan, nan, nan, nan])
Dimensions without coordinates: x

What did I expect?

I expected no user-visible changes in the output values if numbagg is activated.

Maybe, this is not a bug, but expected behaviour for numbagg. The following warning was raised from the second call:

.../Local/virtual_environments/xarray_performance/lib/python3.10/site-packages/numbagg/decorators.py:247: RuntimeWarning: invalid value encountered in move_sum
  return gufunc(*arr, window, min_count, axis=axis, **kwargs)

If this is expected, I think it would be good to have a page in the documentation which lists the downsides and limitations of the various tool to accelerate xarray. From the current installation docs, I assumed I just need to install numbagg/bottleneck to make xarray faster without any changes in output values.

Environment

xarray==2024.2.0
numbagg==0.8.0

Start by running the provided DataArray rolling-sum example with use_numbagg enabled and disabled, then trace the rolling path used for the numbagg case. Compare results around np.inf; done means either matching the non-numbagg output or documenting the confirmed limitation and its expected behavior.