Jax tensors are not correctly recognized if they are stored on GPUs

[zimmerrol]

I noticed that the conversion of ndarrays in JAX to JAXTensors in eagerpy does not work on machines with GPUs, since here JAX will use the jaxlib namespace to represent the array s(as DeviceArrays) instead of the normal jax namespace. This PR adds jaxlib as an alias to detect JAX tensors.

[codecov]

Codecov Report

Merging #31 (b2b48c9) into master (850a905) will not change coverage.
The diff coverage is 100.00%.

@@            Coverage Diff            @@
##            master       #31   +/-   ##
=========================================
  Coverage   100.00%   100.00%           
=========================================
  Files           16        16           
  Lines         1753      1753           
=========================================
  Hits          1753      1753           

Impacted Files	Coverage Δ
eagerpy/astensor.py	100.00% <100.00%> (ø)

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[jonasrauber]

Apparently, this was changed in a recent JAX version. In 0.1.70 it is jax.interpreters.xla.DeviceArray, in 0.2.12 it is jaxlib.xla_extension.DeviceArray.

But let's just add it to the existing if to simplify testing: if (name == "jax" or name == "jaxlib") and

[zimmerrol]

@jonasrauber I think this doesn't work because of the isinstance check in the same line (

eagerpy/eagerpy/astensor.py

Line 55 in b1d7289

if name == "jax" and isinstance(x, m[name].numpy.ndarray): # type: ignore

)

This check only works if name="jax" since there is no class jaxlib.numpy.ndarray. So should we change this line to?:

if (name == "jax" or name == "jaxlib") and isinstance(x, m["jax"].numpy.ndarray):

[jonasrauber]

So should we change this line to?:

Yes 👍