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Children forked after a run generate identical sampler data

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  • ¿Algo no está bien?
#1,076 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

@binaryaaron ya está trabajando en esto.

Desde el 22/10/2026.

  • #1077 de @binaryaaron — abierto

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
25/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
numpy, python
Área
data-engineering

Línea de trabajo

Start at engine/sampling_gen/utils.py:36, where check_random_state(None) selects NumPy's global generator, and trace Faker and distribution sampling paths used by preview() and create(). Use the issue's multiprocessing reproduction to compare fork and spawn, preserving in-process seeding tested by scripts/benchmarks/benchmark_engine_v2.py. Done means forked workers produce independent sampler data while explicit process-local seeding remains repeatable; note that linked PR #1077 already targets this issue.

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

Descripción

bug
Priority Level

Medium (Annoying but has workaround)

Describe the bug

Every child forked after the parent has run preview() or create() generates the same sampler data. Nothing fails or warns; a fork pool of N workers returns N copies of one dataset's sampler columns.

Samplers draw from numpy's global RandomState (check_random_state(None) returns np.random.mtrand._rand, engine/sampling_gen/utils.py:36) and from Faker's shared Random (faker.generator.random, used by every Faker(locale) instance). A fork copies their state, and nothing reseeds it: CPython reseeds the stdlib random module in a forked child, but not numpy's global generator or Faker's. Each child therefore draws the same sequence.

Before #1069 the forked child hung on its first run (#1068), so this was not reachable. With that fix merged, any fork pool created after a first run hits it. fork is the Linux default on Python 3.10–3.13.

Affected: every sampler that draws from scipy/numpy (category, uniform, gaussian, and the other distribution samplers), person_from_faker, and distribution-valued inference parameters (temperature, top_p), which also use numpy's global generator. Not affected: UUIDs, managed-dataset person sampling (DuckDB), and code using the stdlib random module.

Steps/Code to reproduce bug

No model provider needed:

import multiprocessing as mp

import data_designer.config as dd
from data_designer.interface import DataDesigner


def preview(_: int = 0) -> list:
    builder = dd.DataDesignerConfigBuilder()
    builder.add_column(dd.SamplerColumnConfig(
        name="category", sampler_type=dd.SamplerType.CATEGORY,
        params=dd.CategorySamplerParams(values=list("abcdefgh"))))
    builder.add_column(dd.SamplerColumnConfig(
        name="uniform", sampler_type=dd.SamplerType.UNIFORM,
        params=dd.UniformSamplerParams(low=0, high=1000)))
    df = DataDesigner().preview(builder, num_records=4).dataset
    return [f"{c}:{u:.1f}" for c, u in zip(df["category"], df["uniform"])]


if __name__ == "__main__":
    preview()  # a parent run, e.g. checking the config before fanning out
    for method in ("fork", "spawn"):
        with mp.get_context(method).Pool(3) as pool:
            rows = pool.map(preview, range(3))
        print(f"{method:<5} distinct worker outputs: {len(set(map(tuple, rows)))} of 3")
        for r in rows:
            print("     ", r)

Output on main (2c9b66ef, CPython 3.14.0, numpy 2.4.3, Faker 20.1.0, Linux):

fork  distinct worker outputs: 1 of 3
      ['b:965.9', 'c:719.3', 'h:599.7', 'a:869.9']
      ['b:965.9', 'c:719.3', 'h:599.7', 'a:869.9']
      ['b:965.9', 'c:719.3', 'h:599.7', 'a:869.9']
spawn distinct worker outputs: 3 of 3
      ['h:910.8', 'g:252.2', 'g:270.8', 'b:558.7']
      ['g:1.1', 'a:485.1', 'e:968.6', 'a:27.8']
      ['a:624.4', 'f:66.1', 'g:708.3', 'e:586.6']
Expected behavior

Forked children generate independent sampler data, as spawn children do.

Agent Diagnostic / Prior Investigation

Found by an agent sweeping the codebase for process-global state after #1069. It confirmed:

  • numpy.random is not reseeded after fork once the parent has drawn from it. numpy seeds the global generator lazily, so children forked before any draw happen to differ.
  • faker.generator.random is not reseeded after fork, and Faker(locale).random is faker.generator.random.
  • The stdlib random module is reseeded after fork by CPython.

The repo's own benchmark (scripts/benchmarks/benchmark_engine_v2.py:585-586) seeds random and numpy.random for repeatable sampler output, so a fix should keep in-process seeding working.

Additional context

Workaround: use mp.get_context("spawn") or "forkserver", or call numpy.random.seed() and faker.Faker.seed() with no argument at the start of each forked worker.

Related: #1068, #1069.

Lenguaje dominante
Python
Estrellas
2.4k
Forks
234
Merge medio
22 h 5 min
PR fusionados (30 d)
70

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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