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BUG: `SeedSequence.spawn` and `np.random.set_bit_generator` are not thread-safe

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Tranquilo
Stack tecnológico
python
Área
data

Línea de trabajo

Start with the named entry points, SeedSequence.spawn and RandomState._initialize_bit_generator, and reproduce both threaded examples from the issue. Trace how spawn keys and the singleton's bit-generator state are accessed during concurrent work. Done means regression coverage demonstrates unique spawned keys and safe behavior when set_bit_generator races with draws, with the RNG design questions resolved.

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

Descripción

00 - Bug 39 - free-threading component: numpy.random
Describe the issue:

Two independent thread-safety problems in numpy.random, found while reviewing the fix for gh-32059.

1. SeedSequence.spawn hands out duplicate spawn keys

SeedSequence.spawn reads self.n_children_spawned, constructs the children (Python-level work, so the GIL can be released between bytecodes), and only then increments the counter. Two threads spawning from the same SeedSequence can both observe the same starting index and return children with identical spawn keys, i.e. bit generators that produce identical streams. That silently breaks the central guarantee of the spawning API for parallel work.

The BitGenerator lock can't help here: a SeedSequence is an independent object that can be shared by multiple bit generators (or none), so it needs its own synchronization. Note that a per-instance lock interacts with pickling (the auto-generated cdef pickling would try to serialize it).

BitGenerator.spawn and Generator.spawn delegate to SeedSequence.spawn, so they inherit the bug.

Reproduce:
import sys
import threading

import numpy as np

# not required, just makes it reproduce quickly on the GIL-enabled build
sys.setswitchinterval(1e-5)  

ss = np.random.SeedSequence(0)
barrier = threading.Barrier(4)


def spawn(out):
    barrier.wait()
    out.extend(ss.spawn(500))


results = [[] for _ in range(4)]
threads = [threading.Thread(target=spawn, args=(r,)) for r in results]
for t in threads:
    t.start()
for t in threads:
    t.join()

keys = [c.spawn_key for r in results for c in r]
print(f"spawned {len(keys)} children, {len(set(keys))} unique spawn keys, "
      f"n_children_spawned={ss.n_children_spawned}")

Observed (3 out of 3 runs on a GIL build):

spawned 2000 children, 500 unique spawn keys, n_children_spawned=2000

i.e. all four threads returned the same 500 children.

2. np.random.set_bit_generator races with concurrent draws from the singleton

set_bit_generator calls RandomState._initialize_bit_generator with no synchronization. That method rewrites, non-atomically with respect to running draws:

  • self._bit_generator (dropping the reference to the old one),
  • self._bitgen, the copied bitgen_t struct of function pointers plus state pointer,
  • self._aug_state.bit_generator, and
  • self.lock itself.

Distribution fills run with self.lock, nogil: — without the GIL — reading those function/state pointers, so a concurrent swap lets a fill observe a mixed struct (e.g. MT19937's next_double called on a pcg64_state*, which reads/writes far past the end of the smaller struct) or a state pointer into an already-deallocated bit generator.

Taking the lock inside set_bit_generator would not be sufficient: the lock attribute is replaced by the swap, so a thread still blocked on the old lock and a thread acquiring the new lock can be inside the critical section simultaneously. A real fix needs a stable guard owned by RandomState that is never swapped or the API has to explicitly require that replacement happen only while no other thread uses the singleton.

IMO the simplest fix is to detect and raise an error when we detect that set_bit_generator is used on a shared RandomState.

Reproduce:
import threading

import numpy as np

barrier = threading.Barrier(2)
stop = threading.Event()


def draw():
    barrier.wait()
    while not stop.is_set():
        np.random.random(100_000)


def swap():
    barrier.wait()
    for i in range(500):
        bg = np.random.PCG64(i) if i % 2 else np.random.MT19937(i)
        np.random.set_bit_generator(bg)
    stop.set()


threads = [threading.Thread(target=draw), threading.Thread(target=swap)]
for t in threads:
    t.start()
for t in threads:
    t.join()
print("survived")

This script segfaults most times I run it on the GIL-enabled build in my testing.

Python and NumPy Versions:

NumPy main, reproduced using Python 3.14.5.

cc @rkern since fixing these issues requires answering some design questions for the RNG module

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