Nsryjdtyk

I'm trying to speed up my algorithm with numba, after no further improvements where left by using numpy and optimizing.

I have a function that does some computations in a large 2-fold nested loop:

import random

from numba import njit



@njit()

def decide_if_vaild():

    return bool(random.getrandbits(1))



@njit()

def decide_what_bin(bins):

    return random.randint(0, bins-1)



@njit()

def foo(bins, loops):

    results = [ for _ in range(bins)]



    for i in range(loops):

        for j in range(i+1, loops):

            happy = decide_if_vaild()

            bin = decide_what_bin(bins)

            if happy:

                results[bin].append( (i,j) )

                # or

                # results[bin].append( [i,j] )

    return results



if __name__ == '__main__':

    x = foo(3,100)

If I run this minimal example above I (as expected) get a typing error:

  File "C:UsersxxxAppDataLocalProgramsPythonPython36libsite-packagesnumbatypeinfer.py", line 104, in getone

    assert self.type is not None

numba.errors.InternalError: 

[1] During: typing of call at C:/Users/xxx/minimal_example.py (21)

--%<-----------------------------------------------------------------



File "minimal_example.py", line 21

The problem is the: "results[bin].append( (i,j) )" where I try to add a tuple (does not work with lists either) to the list.

The number of bins is kown in advance, but how many elements (2-tuple or list or np.array) depends on how often decide_if_vaild evaluates to True, and as I don't know how often this will be and the computation is quite expensive I don't know any other workaround.

Any good ideas how I could generate the result in the jitted function and return it, or pass a global container that this function could fill?

This probably falls back to:

numba.errors.LoweringError: Failed at nopython (nopython mode backend)

list(list(list(int64))): unsupported nested memory-managed object

where a similiar issue for list(list(int64)) (https://github.com/numba/numba/issues/2560) was resolved in numba 0.39.0 as of https://github.com/numba/numba/pull/2840

I have now implemented following workaround, even though it does not answer the question completly, it might be a suitable approach for others struggeling with this issue:

@njit()

def foo(bins, loops):

    results = 

    mapping = 



    for i in range(loops):

        for j in range(loops+1, size):

            happy = decide_if_vaild()

            bin = decide_what_bin(bins)

            if happy:

                results.append( (i,j) )

                mapping.append( bin )

    return results, mapping

This will return a list of tuples (supported as of numba 0.39.0) and a list of mappings, where mapping[i] contains the bin for results[i]. Now the jit-compiler works smoothly and I can unpack the results outside of jit.