我有以下代碼，我試圖使用并行循環，并且：numbafunctools.reduce()mulimport numpy as npfrom itertools import productfrom functools import reducefrom operator import mulfrom numba import jit, prangelst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]arr = np.array(lst)n = 3flat = np.ravel(arr).tolist()gen = np.array([list(a) for a in product(flat, repeat=n)])@jit(nopython=True, parallel=True)def mtp(gen): results = np.empty(gen.shape[0]) for i in prange(gen.shape[0]): results[i] = reduce(mul, gen[i], initializer=None) return resultsmtp(gen)但這給了我一個錯誤：---------------------------------------------------------------------------TypingError Traceback (most recent call last)<ipython-input-503-cd6ef880fd4a> in <module> 10 results[i] = reduce(mul, gen[i], initializer=None) 11 return results---> 12 mtp(gen)~\Anaconda3\lib\site-packages\numba\dispatcher.py in _compile_for_args(self, *args, **kws) 399 e.patch_message(msg) 400 --> 401 error_rewrite(e, 'typing') 402 except errors.UnsupportedError as e: 403 # Something unsupported is present in the user code, add help info~\Anaconda3\lib\site-packages\numba\dispatcher.py in error_rewrite(e, issue_type) 342 raise e 343 else:--> 344 reraise(type(e), e, None) 345 346 argtypes = []~\Anaconda3\lib\site-packages\numba\six.py in reraise(tp, value, tb) 666 value = tp() 667 if value.__traceback__ is not tb:--> 668 raise value.with_traceback(tb) 669 raise value 670 我不確定我哪里做錯了。任何人都可以給我指出正確的方向嗎？非常感謝。
1 回答
Cats萌萌
TA貢獻1805條經驗 獲得超9個贊
您可以在 numba jitted 函數中使用 np.prod：
n = 3
lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
arr = np.array(lst)
flat = np.ravel(arr).tolist()
gen = [list(a) for a in product(flat, repeat=n)]
@jit(nopython=True, parallel=True)
def mtp(gen):
results = np.empty(len(gen))
for i in prange(len(gen)):
results[i] = np.prod(gen[i])
return results
或者，您可以使用如下所示的reduce（感謝@stuartarchibald指出這一點），盡管并行化在下面不起作用（至少從numba 0.48開始）：
import numpy as np
from itertools import product
from functools import reduce
from operator import mul
from numba import njit, prange
lst = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
arr = np.array(lst)
n = 3
flat = np.ravel(arr).tolist()
gen = np.array([list(a) for a in product(flat, repeat=n)])
@njit
def mul_wrapper(x, y):
return mul(x, y)
@njit
def mtp(gen):
results = np.empty(gen.shape[0])
for i in prange(gen.shape[0]):
results[i] = reduce(mul_wrapper, gen[i], None)
return results
print(mtp(gen))
或者，因為Numba內部有一點魔力，可以發現將轉義函數并編譯它們的閉包。（再次感謝@stuartarchibald），你可以這樣，在下面：
@njit
def mtp(gen):
results = np.empty(gen.shape[0])
def op(x, y):
return mul(x, y)
for i in prange(gen.shape[0]):
results[i] = reduce(op, gen[i], None)
return results
但同樣，并行在numba 0.48之前在這里不起作用。
請注意，核心開發團隊成員推薦的方法是采用第一個使用 .它可以與并行標志一起使用，并具有更直接的實現。np.prod
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