Numba: двойное освобождение или коррупция (! Prev) Прервано (ядро сброшено)

Question

Я пытаюсь ускорить следующую функцию, используя numba.

import numpy as np
from numba import jit, prange

@jit(nopython=True, parallel=True)
def find_reg_numba(states):
    reg = []    
    states_sum = np.sum(states, axis=1)
    for i in prange(states.shape[0]):
        if states_sum[i] > 0 and states_sum[i] < 5:
            reg.append(states[i])
    return reg

states генерируется с использованием следующей функции

def generate_states(size):
    # size is a natural number
    states = np.array(list(map(list, itertools.product([0., 1.], repeat = size))))
    return states

Когда я пытаюсь использовать функцию find_reg, я получаю следующую трассировку ошибки.

double free or corruption (!prev)
Aborted (core dumped)

Моя версия numba 0.48.0.

Как решить эту проблему?

Answer 1

Не уверен, почему ваш код выдает ошибку. Связанные сообщения об ошибках:

• numpy ошибка двойного освобождения или повреждения (! Prev) сегментации

• Ошибка в `python ': двойное освобождение или повреждение (! Prev)

• Python двойное освобождение ошибки для огромных наборов данных

Однако это оказалось бесполезным.

Вот альтернативная версия find_reg_numba для Numba, которая:

1. Работает без ошибок
2. Производит тот же результат, что и исходный код без Numba (т. е. исходный код выдает ошибки только с Numba).

Рефакторинг кода

import numpy as np
from numba import jit
import itertools

@jit(nopython=True, parallel=True)
def find_reg_numba(states):
    states_sum = np.sum(states, axis=1)

    # Find indexes satisfying condition using np.where as described https://www.geeksforgeeks.org/numpy-where-in-python/
    indexes = np.where((states_sum > 0) & (states_sum < 5))
    return states[indexes]

def generate_states(size):
    # size is a natural number
    states = np.array(list(map(list, itertools.product([0., 1.], repeat = size))))
    return states

Тесты

for size in range(10):
  s = generate_states(size)
  r  = find_reg_numba(s)
  print(f'Size: {size}\n Result: \n{r}')

Результаты

 Size: 0
 Result:
[]
Size: 1
 Result:
[[1.]]
Size: 2
 Result:
[[0. 1.]
 [1. 0.]
 [1. 1.]]
Size: 3
 Result:
[[0. 0. 1.]
 [0. 1. 0.]
 [0. 1. 1.]
 [1. 0. 0.]
 [1. 0. 1.]
 [1. 1. 0.]
 [1. 1. 1.]]
Size: 4
 Result:
[[0. 0. 0. 1.]
 [0. 0. 1. 0.]
 [0. 0. 1. 1.]
 [0. 1. 0. 0.]
 [0. 1. 0. 1.]
 [0. 1. 1. 0.]
 [0. 1. 1. 1.]
 [1. 0. 0. 0.]
 [1. 0. 0. 1.]
 [1. 0. 1. 0.]
 [1. 0. 1. 1.]
 [1. 1. 0. 0.]
 [1. 1. 0. 1.]
 [1. 1. 1. 0.]
 [1. 1. 1. 1.]]
Size: 5
 Result:
[[0. 0. 0. 0. 1.]
 [0. 0. 0. 1. 0.]
 [0. 0. 0. 1. 1.]
 [0. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1.]
 [0. 0. 1. 1. 0.]
 [0. 0. 1. 1. 1.]
 [0. 1. 0. 0. 0.]
 [0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0.]
 [0. 1. 0. 1. 1.]
 [0. 1. 1. 0. 0.]
 [0. 1. 1. 0. 1.]
 [0. 1. 1. 1. 0.]
 [0. 1. 1. 1. 1.]
 [1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1.]
 [1. 0. 0. 1. 0.]
 [1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1.]
 [1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 1.]
 [1. 1. 0. 0. 0.]
 [1. 1. 0. 0. 1.]
 [1. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1.]
 [1. 1. 1. 0. 0.]
 [1. 1. 1. 0. 1.]
 [1. 1. 1. 1. 0.]]
Size: 6
 Result:
[[0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1. 1.]
 [0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0. 1.]
 [0. 0. 1. 0. 1. 0.]
 [0. 0. 1. 0. 1. 1.]
 [0. 0. 1. 1. 0. 0.]
 [0. 0. 1. 1. 0. 1.]
 [0. 0. 1. 1. 1. 0.]
 [0. 0. 1. 1. 1. 1.]
 [0. 1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 1.]
 [0. 1. 0. 0. 1. 0.]
 [0. 1. 0. 0. 1. 1.]
 [0. 1. 0. 1. 0. 0.]
 [0. 1. 0. 1. 0. 1.]
 [0. 1. 0. 1. 1. 0.]
 [0. 1. 0. 1. 1. 1.]
 [0. 1. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 1.]
 [0. 1. 1. 0. 1. 0.]
 [0. 1. 1. 0. 1. 1.]
 [0. 1. 1. 1. 0. 0.]
 [0. 1. 1. 1. 0. 1.]
 [0. 1. 1. 1. 1. 0.]
 [1. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1.]
 [1. 0. 0. 1. 0. 0.]
 [1. 0. 0. 1. 0. 1.]
 [1. 0. 0. 1. 1. 0.]
 [1. 0. 0. 1. 1. 1.]
 [1. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1.]
 [1. 0. 1. 0. 1. 0.]
 [1. 0. 1. 0. 1. 1.]
 [1. 0. 1. 1. 0. 0.]
 [1. 0. 1. 1. 0. 1.]
 [1. 0. 1. 1. 1. 0.]
 [1. 1. 0. 0. 0. 0.]
 [1. 1. 0. 0. 0. 1.]
 [1. 1. 0. 0. 1. 0.]
 [1. 1. 0. 0. 1. 1.]
 [1. 1. 0. 1. 0. 0.]
 [1. 1. 0. 1. 0. 1.]
 [1. 1. 0. 1. 1. 0.]
 [1. 1. 1. 0. 0. 0.]
 [1. 1. 1. 0. 0. 1.]
 [1. 1. 1. 0. 1. 0.]
 [1. 1. 1. 1. 0. 0.]]
Size: 7
 Result:
[[0. 0. 0. 0. 0. 0. 1.]
 [0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 1. 1.]
 [0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 1. 0. 1.]
 [0. 0. 0. 0. 1. 1. 0.]
 [0. 0. 0. 0. 1. 1. 1.]
 [0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 1.]
 [0. 0. 0. 1. 0. 1. 0.]
 [0. 0. 0. 1. 0. 1. 1.]
 [0. 0. 0. 1. 1. 0. 0.]
 [0. 0. 0. 1. 1. 0. 1.]
 [0. 0. 0. 1. 1. 1. 0.]
 [0. 0. 0. 1. 1. 1. 1.]
 [0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 1.]
 [0. 0. 1. 0. 0. 1. 0.]
 [0. 0. 1. 0. 0. 1. 1.]
 [0. 0. 1. 0. 1. 0. 0.]
 [0. 0. 1. 0. 1. 0. 1.]
 [0. 0. 1. 0. 1. 1. 0.]
 [0. 0. 1. 0. 1. 1. 1.]
 [0. 0. 1. 1. 0. 0. 0.]
 [0. 0. 1. 1. 0. 0. 1.]
 [0. 0. 1. 1. 0. 1. 0.]
 [0. 0. 1. 1. 0. 1. 1.]
 [0. 0. 1. 1. 1. 0. 0.]
 [0. 0. 1. 1. 1. 0. 1.]
 [0. 0. 1. 1. 1. 1. 0.]
 [0. 1. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 1.]
 [0. 1. 0. 0. 0. 1. 0.]
 [0. 1. 0. 0. 0. 1. 1.]
 [0. 1. 0. 0. 1. 0. 0.]
 [0. 1. 0. 0. 1. 0. 1.]
 [0. 1. 0. 0. 1. 1. 0.]
 [0. 1. 0. 0. 1. 1. 1.]
 [0. 1. 0. 1. 0. 0. 0.]
 [0. 1. 0. 1. 0. 0. 1.]
 [0. 1. 0. 1. 0. 1. 0.]
 [0. 1. 0. 1. 0. 1. 1.]
 [0. 1. 0. 1. 1. 0. 0.]
 [0. 1. 0. 1. 1. 0. 1.]
 [0. 1. 0. 1. 1. 1. 0.]
 [0. 1. 1. 0. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1.]
 [0. 1. 1. 0. 0. 1. 0.]
 [0. 1. 1. 0. 0. 1. 1.]
 [0. 1. 1. 0. 1. 0. 0.]
 [0. 1. 1. 0. 1. 0. 1.]
 [0. 1. 1. 0. 1. 1. 0.]
 [0. 1. 1. 1. 0. 0. 0.]
 [0. 1. 1. 1. 0. 0. 1.]
 [0. 1. 1. 1. 0. 1. 0.]
 [0. 1. 1. 1. 1. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 0. 1. 1.]
 [1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 0. 0. 1. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0.]
 [1. 0. 0. 0. 1. 1. 1.]
 [1. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 1. 0. 0. 1.]
 [1. 0. 0. 1. 0. 1. 0.]
 [1. 0. 0. 1. 0. 1. 1.]
 [1. 0. 0. 1. 1. 0. 0.]
 [1. 0. 0. 1. 1. 0. 1.]
 [1. 0. 0. 1. 1. 1. 0.]
 [1. 0. 1. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1.]
 [1. 0. 1. 0. 0. 1. 0.]
 [1. 0. 1. 0. 0. 1. 1.]
 [1. 0. 1. 0. 1. 0. 0.]
 [1. 0. 1. 0. 1. 0. 1.]
 [1. 0. 1. 0. 1. 1. 0.]
 [1. 0. 1. 1. 0. 0. 0.]
 [1. 0. 1. 1. 0. 0. 1.]
 [1. 0. 1. 1. 0. 1. 0.]
 [1. 0. 1. 1. 1. 0. 0.]
 [1. 1. 0. 0. 0. 0. 0.]
 [1. 1. 0. 0. 0. 0. 1.]
 [1. 1. 0. 0. 0. 1. 0.]
 [1. 1. 0. 0. 0. 1. 1.]
 [1. 1. 0. 0. 1. 0. 0.]
 [1. 1. 0. 0. 1. 0. 1.]
 [1. 1. 0. 0. 1. 1. 0.]
 [1. 1. 0. 1. 0. 0. 0.]
 [1. 1. 0. 1. 0. 0. 1.]
 [1. 1. 0. 1. 0. 1. 0.]
 [1. 1. 0. 1. 1. 0. 0.]
 [1. 1. 1. 0. 0. 0. 0.]
 [1. 1. 1. 0. 0. 0. 1.]
 [1. 1. 1. 0. 0. 1. 0.]
 [1. 1. 1. 0. 1. 0. 0.]
 [1. 1. 1. 1. 0. 0. 0.]]
Size: 8
 Result:
[[0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 0.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 1. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]]
Size: 9
 Result:
[[0. 0. 0. ... 0. 0. 1.]
 [0. 0. 0. ... 0. 1. 0.]
 [0. 0. 0. ... 0. 1. 1.]
 ...
 [1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]
 [1. 1. 1. ... 0. 0. 0.]]