python - 在 numpy/openblas 上设置运行时的最大线程数

Question

我想知道是否可以在(Python)运行时更改 OpenBLAS 在 numpy 后面使用的最大线程数？

我知道可以在通过环境变量 OMP_NUM_THREADS 运行解释器之前设置它，但我想在运行时更改它。

通常，当使用 MKL 而不是 OpenBLAS 时，有可能:

import mkl
mkl.set_num_threads(n)

Answer 1

您可以通过使用 ctypes 调用 openblas_set_num_threads 函数来完成此操作。我经常发现自己想这样做，所以我写了一个小上下文管理器:

import contextlib
import ctypes
from ctypes.util import find_library

# Prioritize hand-compiled OpenBLAS library over version in /usr/lib/
# from Ubuntu repos
try_paths = ['/opt/OpenBLAS/lib/libopenblas.so',
             '/lib/libopenblas.so',
             '/usr/lib/libopenblas.so.0',
             find_library('openblas')]
openblas_lib = None
for libpath in try_paths:
    try:
        openblas_lib = ctypes.cdll.LoadLibrary(libpath)
        break
    except OSError:
        continue
if openblas_lib is None:
    raise EnvironmentError('Could not locate an OpenBLAS shared library', 2)


def set_num_threads(n):
    """Set the current number of threads used by the OpenBLAS server."""
    openblas_lib.openblas_set_num_threads(int(n))


# At the time of writing these symbols were very new:
# https://github.com/xianyi/OpenBLAS/commit/65a847c
try:
    openblas_lib.openblas_get_num_threads()
    def get_num_threads():
        """Get the current number of threads used by the OpenBLAS server."""
        return openblas_lib.openblas_get_num_threads()
except AttributeError:
    def get_num_threads():
        """Dummy function (symbol not present in %s), returns -1."""
        return -1
    pass

try:
    openblas_lib.openblas_get_num_procs()
    def get_num_procs():
        """Get the total number of physical processors"""
        return openblas_lib.openblas_get_num_procs()
except AttributeError:
    def get_num_procs():
        """Dummy function (symbol not present), returns -1."""
        return -1
    pass


@contextlib.contextmanager
def num_threads(n):
    """Temporarily changes the number of OpenBLAS threads.

    Example usage:

        print("Before: {}".format(get_num_threads()))
        with num_threads(n):
            print("In thread context: {}".format(get_num_threads()))
        print("After: {}".format(get_num_threads()))
    """
    old_n = get_num_threads()
    set_num_threads(n)
    try:
        yield
    finally:
        set_num_threads(old_n)

你可以这样使用它:

with num_threads(8):
    np.dot(x, y)

如评论中所述，openblas_get_num_threads 和 openblas_get_num_procs 在撰写本文时是非常新的功能，因此可能无法使用，除非您从最新版本编译 OpenBLAS源代码。