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`threadpool_limits(1, user_api="blas")` also limits OpenMP to 1 thread with OpenBLAS's OpenMP threading layer (3.7.0)

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难度
4/5
预计耗时
3-5 天
新手友好度
35/100
Issue 类型
缺陷
描述清晰度
基本清楚
活跃度
活跃
技术栈
python
领域
performance

调研方向

Start from the OpenBLAS controller's set_num_threads in threadpoolctl, where it now calls omp_set_num_threads for the OpenMP threading layer, and from the reproduction script that prints the OpenMP thread count under threadpool_limits(limits=1, user_api="blas"). Done means the BLAS limit no longer changes the OpenMP count for the current thread, with the 3.6.0 behaviour restored. The maintainer has not settled the fix, so check the comment thread for a decided approach before writing code.

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描述

Describe the bug

Since 3.7.0, with OpenBLAS's OpenMP threading layer, limiting BLAS also limits the OpenMP runtime of the current thread: inside threadpool_limits(limits=1, user_api="blas"), omp_get_max_threads() returns 1, so any OpenMP parallel region started from this thread without an explicit num_threads runs on a single thread. With 3.6.0 and the same OpenBLAS (0.3.34), it stays at 8.

It comes from set_num_threads of the OpenBLAS controller, which now calls omp_set_num_threads when the threading layer is OpenMP, to get a limit per thread rather than per process. But OpenBLAS and the caller share the same OpenMP runtime, so the BLAS limit becomes an OpenMP limit too.

In scikit-learn, the usual pattern for nested parallelism is to limit BLAS to 1 thread around Cython code that calls BLAS inside OpenMP prange loops. Any code that reads the number of OpenMP threads inside that limit now gets 1.

For instance, a KMeans fitted under threadpool_limits(1, user_api="blas") now runs its OpenMP code on 1 thread (see below).

I'm not sure what the best fix is, maybe just some documentation?

Steps/Code to Reproduce
import numpy as np  # loads OpenBLAS
from threadpoolctl import threadpool_info, threadpool_limits


def openmp_num_threads():
    return [i["num_threads"] for i in threadpool_info() if i["user_api"] == "openmp"]


print("openmp num_threads:", openmp_num_threads())
with threadpool_limits(limits=1, user_api="blas"):
    print("openmp num_threads under blas limit 1:", openmp_num_threads())

With scikit-learn:

import numpy as np
from threadpoolctl import threadpool_limits
from sklearn.cluster import KMeans

X = np.random.RandomState(0).randn(1000, 5)
with threadpool_limits(limits=1, user_api="blas"):
    print(KMeans(3, n_init=1).fit(X)._n_threads)
Expected Results

With OMP_NUM_THREADS=8:

openmp num_threads: [8]
openmp num_threads under blas limit 1: [8]

and 8 for KMeans._n_threads. That's what 3.6.0 gives.

Actual Results
openmp num_threads: [8]
openmp num_threads under blas limit 1: [1]

and 1 for KMeans._n_threads.

Versions

MacBook Pro M2 (macOS 26.6.2), conda-forge packages:

python         3.13.16
numpy          2.5.4
libopenblas    0.3.34  openmp_h5e6e99c_1  (threading_layer: openmp, VORTEX)
llvm-openmp    23.1.3
threadpoolctl  3.7.0
主要语言
Python
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434
派生
38
平均合并
2 天 9 小时
30 天内合并 PR
7

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