关于python：如何将numpy.ndarray分配给cython中nogil循环下的临时变量？

How to assign numpy.ndarray to temporary variable under nogil loop in cython?

我正在尝试实施隐式推荐模型，但在代码运行时间计算前 5 条建议时遇到了问题，该建议针对超过 10 万个项目的约 11 万用户。

我能够通过 numpy 和一些 cython sparkles(在 jupyter notebook 中)部分解决问题。使用 numpy 排序的行仍然使用单核：

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%%cython -f
# cython: language_level=3
# cython: boundscheck=False
# cython: wraparound=False
# cython: linetrace=True
# cython: binding=True
# distutils: define_macros=CYTHON_TRACE_NOGIL=1
from cython.parallel import parallel, prange
import numpy as np
from tqdm import tqdm

def test(users_items=np.random.rand(11402139//1000, 134751//100)
, int N=5, show_progress=True, int num_threads=1):
# Define User count and loops indexes
cdef int users_c = users_items.shape[0], u, i
# Predefine zero 2-D C-ordered array for recommendations
cdef int[:,::1] users_recs = np.zeros((users_c, N), dtype=np.intc)
for u in tqdm(range(users_c), total=users_c, disable=not show_progress):
# numpy .dot multiplication using multiple cores
scores = np.random.rand(134751//1000, 10).dot(np.random.rand(10))
# numpy partial sort
ids_partial = np.argpartition(scores, -N)[-N:]
ids_top = ids_partial[np.argsort(scores[ids_partial])]
# Fill predefined 2-D array
for i in range(N):
users_recs[u, i] = ids_top[i]
return np.asarray(users_recs)
# Working example
tmp = test()

我对其进行了分析 - np.argpartition 消耗 60% 的函数时间并使用 onde 核心。我正在尝试使其并行，因为我有一台具有 80 个内核的服务器。因此，我对用户子集(使用多核)执行 .dot 操作并计划通过并行的 numpy 排序结果(使用单核)填充空的预定义数组，但我遇到了问题标题中的错误：

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%%cython -f
# cython: language_level=3
# cython: boundscheck=False
# cython: wraparound=False
# cython: linetrace=True
# cython: binding=True
# distutils: define_macros=CYTHON_TRACE_NOGIL=1
from cython.parallel import parallel, prange
import numpy as np
from tqdm import tqdm
from math import ceil
def test(int N=10, show_progress=True, int num_threads=1):
# Define User and Item count and loops indexes
cdef int users_c = 11402139//1000, items_c = 134751//100, u, i, u_b
# Predefine zero 2-D C-ordered array for recommendations
cdef int[:,::1] users_recs = np.zeros((users_c, N), dtype=np.intc)
# Define memoryview var
cdef float[:,::1] users_items_scores_mv
progress = tqdm(total=users_c, disable=not show_progress)
# For a batch of Users
for u_b in range(5):
# Use .dot operation which use multiple cores
users_items_scores = np.random.rand(num_threads, 10).dot(np.random.rand(134751//100, 10).T)
# Create memory view to 2-D array, which I'm trying to sort row wise
users_items_scores_mv = users_items_scores
# Here it starts, try to use numpy sorting in parallel
for u in prange(num_threads, nogil=True, num_threads=num_threads):
ids_partial = np.argpartition(users_items_scores_mv[u], items_c-N)[items_c-N:]
ids_top = ids_partial[np.argsort(users_items_scores_mv[u][ids_partial])]
# Fill predefined 2-D array
for i in range(N):
users_recs[u_b + u, i] = ids_top[i]
progress.update(num_threads)
progress.close()
return np.asarray(users_recs)

并得到这个(完全错误)：

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Error compiling Cython file:
------------------------------------------------------------
...
# Create memory view to 2-D array,
# which I'm trying to sort row wise
users_items_scores_mv = users_items_scores
# Here it starts, try to use numpy sorting in parallel
for u in prange(num_threads, nogil=True, num_threads=num_threads):
ids_partial = np.argpartition(users_items_scores_mv[u], items_c-N)[items_c-N:]
^
------------------------------------------------------------

/datascc/enn/.cache/ipython/cython/_cython_magic_201b296cd5a34240b4c0c6ed3e58de7c.pyx:31:12: Assignment of Python object not allowed without gil

我阅读了内存视图和 malloc-ating 但没有找到适用于我的情况的示例。