Description

At the moment I am trying to use cp.vectorize to improve the performance over np.vectorize. However the function that I am trying to vectorize accepts an array like argument that I want to be consistent for each function call, so I dont want to vectorize over this argument, here is a minimal working example:

import numpy as np
from bisect import bisect_left

def myfunc(a,b):
    return bisect_left(a, b)

vfunc = np.vectorize(myfunc, excluded = ['a'])
vfunc(a= np.array([1,2,3,4]),b=np.array([1.5,2.5]))

In other words, I would like the following code to work as expected:

import cupy as cp
from bisect import bisect_left

def myfunc(a,b):
    return bisect_left(a, b)

vfunc = cp.vectorize(myfunc, excluded = ['a'])
vfunc(a= cp.array([1,2,3,4]),b=cp.array([1.5,2.5]))

Note that help(cp.vectorize) produces the following output:

Help on class vectorize in module cupy._functional.vectorize:
class vectorize(builtins.object)
| vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None)
|
| Generalized function class.
|
| .. seealso:: :class:numpy.vectorize
|
| Methods defined here:
|
| call(self, *args)
| Call self as a function.
|
| init(self, pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None)
| Args:
| pyfunc (callable): The target python function.
| otypes (str or list of dtypes, optional): The output data type.
| doc (str or None): The docstring for the function.
| excluded: Currently not supported.
| cache: Currently Ignored.
| signature: Currently not supported.

I would like to request that the excluded option be implemented.

In the meantime, I tried to workaround this by vectorizing a function with arbitrary number of arguments *args, but cp.vectorize does not support this either:

import cupy as cp
from bisect import bisect_left

def myfunc(b, *a):
    return bisect_left(a, b)
testlist = [4,5,6,7,8]
vfunc = cp.vectorize(myfunc)
vfunc(cp.array([1,2,3,4]),*testlist)

Output:

NotImplementedError: *args is not supported currently.

Any other potential workarounds would be greatly appreciated!

Additional Information

No response