cuPyNumeric aims to be a drop-in replacement for NumPy, with full API compatibility achieved by automatically falling back to NumPy for any functionality which isn't yet implemented.
I suspect this should make it a trivial addition to array-api-compat, although the way it falls back to NumPy may need to be explicitly handled as it returns NumPy ndarrays from any function marked with a dot in this table: https://docs.nvidia.com/cupynumeric/latest/api/comparison.html
For example:
>>> import cupynumeric as xp
>>> from array_api_compat import is_numpy_array
>>>
>>> a = xp.linspace(0.0, 1.0, 21)
>>> is_numpy_array(a)
False
>>> b = xp.acos(a)
<cupynumeric internals>:241: RuntimeWarning: cuPyNumeric has not implemented numpy.acos and is falling back to canonical NumPy. You may notice significantly decreased performance for this function call.
Set CUPYNUMERIC_FALLBACK_STACKTRACE=1 and re-run to include a full stack trace with this warning.
>>> is_numpy_array(b)
True
>>>
I don't know if there's a preferred method to do so, but it's not difficult to programmatically check which functions do not yet have a native implementation. E.g.
>>> xp.acos.__module__
'numpy'
>>> xp.cos.__module__
'cupynumeric._ufunc.ufunc'
cuPyNumeric aims to be a drop-in replacement for NumPy, with full API compatibility achieved by automatically falling back to NumPy for any functionality which isn't yet implemented.
I suspect this should make it a trivial addition to array-api-compat, although the way it falls back to NumPy may need to be explicitly handled as it returns NumPy ndarrays from any function marked with a dot in this table: https://docs.nvidia.com/cupynumeric/latest/api/comparison.html
For example:
I don't know if there's a preferred method to do so, but it's not difficult to programmatically check which functions do not yet have a native implementation. E.g.