Does numba support returning closures from a @jitted function?

Numba Support: How do I do ...?
3

I mean in principle those suggestions are perfectly sensible and in terms of code running to completion, they make sense.

I just find very frustrating I can get bar work if I write things out completely:

@jit
def baz(x, y):

    # CUT CUT CUT
    def bar(y):
        # Do something with x and y
        # potentially very long function
        # that I might want to reuse somewhere else
        return x + y
    # CUT CUT CUT

    s = 0
    for i in range(10):
        s += bar(y)
    return s

but not if I want to extract it outside… even when forcing numba-side inline…

@jit(inline="always")
def foo(x):

    def bar(y):
        # Do something with x and y
        # potentially very long function
        # that I might want to reuse somewhere else
        return x + y

    return bar

@jit
def baz(x, y):
    # CUT CUT CUT
    bar = foo(x)
    # CUT CUT CUT

    s = 0
    for i in range(10):
        s += bar(y)
    return s

With anything other than scalar types, LLVM cannot assume x is gonna be constant throughout the loop and therefore won’t generate vectorised code (well maybe in reduction code like this). In the first instance, I don’t believe bar will specialise over x anyway (will just be inlined). The second case would force specialisation or at least might be enough from LLVM to understand x is constant…