numba: overload of function

Question

I'm trying out numba, the python package that is said to make my nparray super fast. I want to run my function in nonpython mode. What it essentially does is that it takes in an 20x20 array, assigns random numbers to each of its elements, calculate its inverse matrix, then return it. But here's the problem, when I initialize the array result with np.zeros(), my script crashes and gives me an error message 'overload of function zeros'. Could someone kindly tell me what is going on? Much appreciated.

from numba import njit
import time
import numpy as np
import random

arr = np.zeros((20,20),dtype = float)
@njit
def aFunctionWithNumba (incomingArray):
    result = np.zeros(np.shape(incomingArray), dtype = float)
    for i in range(len(incomingArray[0])):
        for j in range(len(incomingArray[1])):
            incomingArray[i,j] = random.randrange(105150,1541586)
    result = np.linalg.inv(incomingArray)
    return result

t0 = time.time()
fastArray = aFunctionWithNumba(arr)
t1 = time.time()
s1 = t1 - t0

Here's the full error message:

Exception has occurred: TypingError Failed in nopython mode pipeline (step: nopython frontend) No implementation of function Function(<built-in function zeros>) found for signature:
 
 >>> zeros(UniTuple(int64 x 2), dtype=Function(<class 'float'>))   There are 2 candidate implementations:
  - Of which 2 did not match due to:   Overload of function 'zeros': File: numba\core\typing\npydecl.py: Line 511.
    With argument(s): '(UniTuple(int64 x 2), dtype=Function(<class 'float'>))':    No match.

During: resolving callee type: Function(<built-in function zeros>) During: typing of call at c:\Users\Eric\Desktop\testNumba.py (9)


File "testNumba.py", line 9: def aFunctionWithNumba (incomingArray):
    result = np.zeros(np.shape(incomingArray), dtype = float)
    ^   File "C:\Users\Eric\Desktop\testNumba.py", line 25, in <module>
    fastArray = aFunctionWithNumba(arr)

Answer 1

The error

You should use Numpy or Numba types inside JITted functions.

Changing the following line your code works:

result = np.zeros(np.shape(incomingArray), dtype=np.float64)

But your code will be more generic using:

result = np.zeros(incomingArray.shape, dtype=incomingArray.dtype)

Or, even better:

result = np.zeros_like(incomingArray)

The timing

The first time you call a JITted function it will take some time to compile it, much longer than the time it will take to execute it. So you should call the function with the same parameter types once before you make any timings.

Additional optimization

If you are interested in comparing the execution time of nested loops with or without Numba, your code is fine. Otherwise you can replace the loops with something like:

incomingArray[:] = np.random.random(incomingArray.shape) * (1541586 - 105150) + 105150