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I like this approach. If we ever have a second case of process wide shared threadpool, we could even introduce a mixin class for those controller methods. Or we could use ducktyping on the controllers from joblib. |
Contributor
Author
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Here's what the corresponding joblib changes would look like, as a sketch: 2 files changed, 38 insertions(+)
joblib/_parallel_backends.py | 36 ++++++++++++++++++++++++++++++++++++
joblib/parallel.py | 2 ++
modified joblib/_parallel_backends.py
@@ -13,6 +13,8 @@ from abc import ABCMeta, abstractmethod
from dataclasses import dataclass
from typing import Any, Callable
+from threadpoolctl import OpenBLASController
+
try:
from threadpoolctl import get_cached_controller as _get_threadpool_controller
except ImportError:
@@ -360,6 +362,24 @@ class ParallelBackendBase(metaclass=ABCMeta):
return isinstance(threading.current_thread(), threading._MainThread)
+class _OpenBLASOwnership(threading.local):
+ """Count how many ``Parallel`` have modified OpenBLAS limits."""
+
+ count = 0
+
+ def increment(self):
+ """Increment ownership."""
+ self.count += 1
+
+ def decrement(self):
+ """Decrement ownership, return whether it hit zero."""
+ self.count -= 1
+ return self.count <= 0
+
+
+_OPENBLAS_OWNERSHIP = _OpenBLASOwnership()
+
+
class SequentialBackend(ParallelBackendBase):
"""A ParallelBackend which will execute all batches sequentially.
@@ -373,6 +393,10 @@ class SequentialBackend(ParallelBackendBase):
supports_retrieve_callback = False
supports_sharedmem = True
+ def configure(self, *args, **kwargs):
+ _OPENBLAS_OWNERSHIP.increment()
+ return super().configure(*args, **kwargs)
+
def effective_n_jobs(self, n_jobs):
"""Determine the number of jobs which are going to run in parallel"""
if n_jobs == 0:
@@ -394,6 +418,13 @@ class SequentialBackend(ParallelBackendBase):
# default backend or the number of jobs. Just return the current one.
return get_active_backend()
+ def terminate(self):
+ if _OPENBLAS_OWNERSHIP.decrement() and hasattr(
+ OpenBLASController, "clear_controlling_thread"
+ ):
+ OpenBLASController.clear_controlling_thread()
+ return super().terminate()
+
class PoolManagerMixin(object):
"""A helper class for managing pool of workers."""
@@ -582,6 +613,7 @@ class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
def configure(self, n_jobs=1, parallel=None, **backend_kwargs):
"""Build a process or thread pool and return the number of workers"""
+ _OPENBLAS_OWNERSHIP.increment()
self._threadpool_controller = _get_threadpool_controller()
# Used to restore limits when when done:
self._thread_limiter = self._threadpool_controller.limit()
@@ -611,6 +643,10 @@ class ThreadingBackend(PoolManagerMixin, ParallelBackendBase):
def terminate(self):
self._thread_limiter.restore_original_limits()
+ if _OPENBLAS_OWNERSHIP.decrement() and hasattr(
+ OpenBLASController, "clear_controlling_thread"
+ ):
+ OpenBLASController.clear_controlling_thread()
super().terminate()
modified joblib/parallel.py
@@ -1951,6 +1951,8 @@ class Parallel(Logger):
self._iterating = False
self._original_iterator = None
self.print_progress()
+ if not self._managed_backend:
+ self._backend.terminate()
def _reset_run_tracking(self):
"""Reset the counters and flags used to track the execution.""" |
Contributor
Author
|
Once #280 is merged can rerun and see if CI will be green. |
itamarst
marked this pull request as ready for review
October 26, 2026 14:42
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The expected joblib usage pattern is that it has a thread-local counter, which gets incremented on
ThreadedBackend.configure(), and decremented onThreadedBackend.finalize(), and if it hits zero thenOpenBLASController.clear_controlling_thread()is called.The result is that so long as you have
Parallel()running in a particular thread, typically the main thread, other threads can't set OpenBLAS-pthreads thread pool size.