No safe way to set the CPU thread count from Python: _unsafe_reset_threadpool after load_method crashes or hangs XNNPACK

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@shoumikhin ya está trabajando en esto.

Desde el 7/11/2026.

  • #24770 de @shoumikhin — abierto

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
25/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
linux, python, pytorch
Área
backend, machine-learning

Línea de trabajo

Start with the Python binding for _unsafe_reset_threadpool and the thread pool lifetime rules in threadpool.h; trace how loaded methods retain the pool pointer. The issue includes a reproducer using XNNPACK and Runtime.load_method, so use it to check the reset-before-load and reset-after-load cases. Done means Python has a supported way to choose the thread count or rejects a reset after loading before it can crash or hang; an open linked pull request indicates work is already underway.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

bug
🐛 Describe the bug

From Python, _unsafe_reset_threadpool(n) is the only way to set the thread count that XNNPACK uses. Called after a method is loaded, it makes the following execute calls segfault or hang forever (all threads waiting on a futex). Called before load_method, it works.

import torch
from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
from executorch.exir import to_edge_transform_and_lower
from executorch.extension.pybindings.portable_lib import (
    _threadpool_get_thread_count,
    _unsafe_reset_threadpool,
)
from executorch.runtime import Runtime

model = torch.nn.Sequential(torch.nn.Linear(256, 256), torch.nn.ReLU(), torch.nn.Linear(256, 256)).eval()
x = torch.randn(64, 256)
ep = torch.export.export(model, (x,))
pte = to_edge_transform_and_lower(ep, partitioner=[XnnpackPartitioner()]).to_executorch()
method = Runtime.get().load_program(pte.buffer).load_method("forward")
_unsafe_reset_threadpool(2 * _threadpool_get_thread_count())
for _ in range(20):
    method.execute([x])  # segfaults or hangs

On Linux x86_64 with 8 threads by default, a reset to 16 after load_method crashed or hung in 10 of 11 runs, and a reset to 4 in 3 of 5. The same resets before load_method passed 10 of 10. Linux aarch64 shows the same pattern, including the hang and an abort with The futex facility returned an unexpected error code. A reset to the current count does nothing, so it always looks fine.

Cause: the XNNPACK runtime gets the thread pool pointer when the method is loaded (xnn_create_runtime_v4(..., get_pthreadpool(), ...)), and the reset frees that pool and creates a new one.

threadpool.h warns that other code may still hold the old pointer and says to reset before loading. The Python function has no such note and does not stop the call. This is different from #14321 (a macOS crash from two copies of pthreadpool): here there is one copy, and the trigger is the order of the calls.

Ask:

  1. A supported way to set the thread count from Python, for example a runtime or backend option.
  2. Until then, raise an error instead of crashing when the reset comes after a method is loaded.
Versions
  • executorch 1.6.0 (PyPI) and the 2026-11-05 nightly
  • torch 2.15.0, and 2.16 nightly with the ExecuTorch nightly
  • Linux x86_64 and Linux aarch64, CPU
Lenguaje dominante
Python
Estrellas
5.2k
Forks
1.2k
Merge medio
1 d 23 h
PR fusionados (30 d)
700

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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