[BUG] Out-of-bounds read when solving a DataModel with lower/upper constraint bounds

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@ramakrishnap-nv ya está trabajando en esto.

Desde el 2/11/2026.

  • #2253 de @ramakrishnap-nv — abierto

Evaluación

Dificultad
3/5
Tiempo estimado
Medio día
Aptitud para principiantes
55/100
Tipo de issue
Error
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
backend

Línea de trabajo

Start with _compute_slack_csr, the function the report names as reading the rhs and sense arrays, and compare it with how a DataModel built through set_constraint_lower_bounds() and set_constraint_upper_bounds() fills those arrays. Run the reproducer in the issue, which should print slack near zero for x >= 1. Done means the slack is computed from the constraint bounds without reading the empty arrays, and the Cython bounds-checked run no longer reports an out-of-bounds access.

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

Descripción

bug

(AI-assisted bug report)

cuopt.linear_programming.Solve accepts a DataModel whose constraints are supplied through set_constraint_lower_bounds() and set_constraint_upper_bounds(). When it constructs a Solution with a primal vector, its Python/Cython slack calculation instead reads the alternative RHS and row-sense arrays. Those arrays are empty for this valid DataModel representation. Because Cython bounds checking is disabled, the code reads outside both arrays. The read can produce an incorrect slack or terminate the process with SIGSEGV.

Reproducer:

import numpy as np
from cuopt.linear_programming.data_model import DataModel
from cuopt.linear_programming.solver import Solve
from cuopt.linear_programming.solver_settings import SolverMethod, SolverSettings

# Minimize x subject to x >= 1.
dm = DataModel()
dm.set_csr_constraint_matrix(
    np.array([1.0]),
    np.array([0], dtype=np.int32),
    np.array([0, 1], dtype=np.int32),
)
dm.set_objective_coefficients(np.array([1.0]))
dm.set_variable_lower_bounds(np.array([0.0]))
dm.set_variable_upper_bounds(np.array([np.inf]))
dm.set_variable_types(np.array(["C"], dtype="S1"))
dm.set_constraint_lower_bounds(np.array([1.0]))
dm.set_constraint_upper_bounds(np.array([np.inf]))

print(
    "rows=1",
    "rhs=", len(dm.get_constraint_bounds()),
    "sense=", len(dm.get_row_types()),
    "lower=", len(dm.get_constraint_lower_bounds()),
    "upper=", len(dm.get_constraint_upper_bounds()),
    flush=True,
)
settings = SolverSettings()
settings.set_parameter("method", SolverMethod.PDLP)
settings.set_parameter("log_to_console", False)
solution = Solve(dm, settings)
print(solution.get_termination_reason())
print("x=", solution.get_primal_solution())
print("slack=", solution.get_slack())

Observed locally:

rows=1 rhs= 0 sense= 0 lower= 1 upper= 1
Optimal
x= [1.]
slack= [-1.]

The constraint is x >= 1 so we'd expect the slack to be zero. I observed segfaults on other instances.

The source of the issue is _compute_slack_csr which reads from the rhs and sense vectors which aren't initialized in this DataModel code path. Running with bounds checking enabled in Cython confirms the out of bounds access.

Lenguaje dominante
Cuda
Estrellas
1.1k
Forks
249
Merge medio
1 d 16 h
PR fusionados (30 d)
127

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