将一个大对象的方法传递给imap:通过包装方法加速1000倍

假设yo = Yo()是一个带有方法的大对象double,它返回其参数乘以2.

如果我通过yo.double到imap的multiprocessing,那么它是非常缓慢的,因为每一个函数调用创建一个副本yo,我认为.

即,这很慢:

from tqdm import tqdm
from multiprocessing import Pool
import numpy as np


class Yo:
    def __init__(self):
        self.a = np.random.random((10000000, 10))

    def double(self, x):
        return 2 * x

yo = Yo()    

with Pool(4) as p:
    for _ in tqdm(p.imap(yo.double, np.arange(1000))):
        pass

输出:

0it [00:00, ?it/s]
1it [00:06,  6.54s/it]
2it [00:11,  6.17s/it]
3it [00:16,  5.60s/it]
4it [00:20,  5.13s/it]

...

但是,如果我yo.double用函数包装double_wrap并将其传递给它imap,那么它基本上是瞬时的.

def double_wrap(x):
    return yo.double(x)

with Pool(4) as p:
    for _ in tqdm(p.imap(double_wrap, np.arange(1000))):
        pass

输出:

0it [00:00, ?it/s]
1000it [00:00, 14919.34it/s]

如何以及为什么包装函数会改变行为？

我使用Python 3.6.6.