Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

onnx-graphsurgeon: Graph.toposort() hits RecursionError when a graph has a local function and an If/Loop node

Abierto Apto para principiantes
  • ¿Algo no está bien?
#4,874 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

@gabortar ya está trabajando en esto.

Desde el 12/10/2026.

  • #4875 de @gabortar — abierto

Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
82/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

El error está en get_used_funcs dentro de Graph.toposort en onnx_graphsurgeon/ir/graph.py, donde el bucle itera sobre self.subgraphs() en lugar de sobre los subgrafos de cada nodo que se pasa. Empieza ejecutando el script de reproducción del issue para reproducir el RecursionError y luego lee get_used_funcs y get_hierarchy_level. Está terminado cuando la reproducción ya no entra en recursión y un caso anidado, en el que la función A llama a B solo dentro de una rama If, se ordena como [B, A], con pruebas de regresión añadidas en tests/ir/test_graph.py.

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

Descripción

Module:GraphSurgeon

Description

Graph.toposort() in ONNX-GraphSurgeon fails with RecursionError when a graph has at least one local function and at least one node with a subgraph (If, Loop, Scan). The node doesn't need to call the function.

  File "onnx_graphsurgeon/ir/graph.py", line 594, in get_used_funcs
    inputs.update(get_used_funcs(subgraph.nodes))
  [Previous line repeated 990 more times]
RecursionError: maximum recursion depth exceeded

The problem is in get_used_funcs in onnx_graphsurgeon/ir/graph.py, which toposort() uses to order functions (default mode="full"):

def get_used_funcs(nodes):
    inputs = {}
    for subgraph in self.subgraphs():
        inputs.update(get_used_funcs(subgraph.nodes))
    ...

The loop uses self.subgraphs(), the subgraphs of the outer graph, not the subgraphs of the nodes that were passed in. self is the same on every recursive call, so each call loops over the same subgraphs again and the recursion never stops.

Even when the graph has no top-level subgraphs and it doesn't crash, the function never looks inside the subgraphs of the nodes it was given. If function A calls function B only from inside an If branch, toposort() misses the dependency and returns the functions in the order [A, B] instead of [B, A].

cleanup(), fold_constants() and export_onnx() work fine on the same graph. Only toposort() fails.

Environment

TensorRT Version: N/A, ONNX-GraphSurgeon only. Reproduced on main (16ed063, onnx-graphsurgeon 0.6.2) and on onnx-graphsurgeon 0.6.1 from PyPI. The code was added in the TensorRT 10.0 release.

NVIDIA GPU: N/A

NVIDIA Driver Version: N/A

CUDA Version: N/A

CUDNN Version: N/A

Operating System: Linux

Python Version (if applicable): 3.14 (onnx 1.23.2, numpy 2.5.4)

Tensorflow Version (if applicable): N/A

PyTorch Version (if applicable): N/A

Baremetal or Container (if so, version): Baremetal

Relevant Files

Model link: N/A, the script below builds the graph directly.

Steps To Reproduce

Commands or scripts:

import numpy as np
import onnx_graphsurgeon as gs

# Any local function
x = gs.Variable("x", dtype=np.float32, shape=(1,))
y = gs.Variable("y", dtype=np.float32, shape=(1,))
func = gs.Function("MyFunc", inputs=[x], outputs=[y], nodes=[gs.Node("Identity", inputs=[x], outputs=[y])], opset=18)

# Any node with a subgraph
def branch(name):
    out = gs.Variable(f"{name}_out", dtype=np.float32, shape=(1,))
    const = gs.Constant(f"{name}_const", np.ones((1,), dtype=np.float32))
    return gs.Graph(nodes=[gs.Node("Identity", inputs=[const], outputs=[out])], outputs=[out])

cond = gs.Variable("cond", dtype=np.bool_, shape=())
if_out = gs.Variable("if_out", dtype=np.float32, shape=(1,))
if_node = gs.Node("If", inputs=[cond], outputs=[if_out],
                  attrs={"then_branch": branch("then"), "else_branch": branch("else")})

graph = gs.Graph(nodes=[if_node], inputs=[cond], outputs=[if_out], functions=[func], opset=18)
graph.toposort()

Full traceback:

  File "onnx_graphsurgeon/ir/graph.py", line 636, in toposort
    func, level=get_hierarchy_level(func)
  File "onnx_graphsurgeon/ir/graph.py", line 611, in get_hierarchy_level
    for inp in get_inputs(node_or_func)
  File "onnx_graphsurgeon/ir/graph.py", line 604, in get_inputs
    inputs = get_used_funcs(node_or_func.nodes).values()
  File "onnx_graphsurgeon/ir/graph.py", line 594, in get_used_funcs
    inputs.update(get_used_funcs(subgraph.nodes))
  File "onnx_graphsurgeon/ir/graph.py", line 594, in get_used_funcs
    inputs.update(get_used_funcs(subgraph.nodes))
  [Previous line repeated 990 more times]
  File "onnx_graphsurgeon/ir/graph.py", line 593, in get_used_funcs
    for subgraph in self.subgraphs():
  File "onnx_graphsurgeon/ir/graph.py", line 381, in subgraphs
    for subgraph in node.subgraphs(recursive=recursive):
RecursionError: maximum recursion depth exceeded

Have you tried the latest release?: Yes. It reproduces on the latest onnx-graphsurgeon from PyPI (0.6.1) and on current main.

Can this model run on other frameworks?: N/A. This is a bug in graph manipulation, not in inference. onnx.checker.check_model accepts the exported graph.

Proposed fix

Recurse into the subgraphs of each node in nodes:

def get_used_funcs(nodes):
    inputs = {}
    for node in nodes:
        for subgraph in node.subgraphs():
            inputs.update(get_used_funcs(subgraph.nodes))
        func_id = (node.domain, node.op)
        if func_id in func_id_to_func:
            inputs[func_id] = func_id_to_func[func_id]
    return inputs

With this change, the repro above passes and the nested A/B case is sorted as [B, A]. I'd like to send a PR with this fix and regression tests in tests/ir/test_graph.py.

Lenguaje dominante
C++
Estrellas
13.4k
Forks
2.4k
Merge medio
15 min
PR fusionados (30 d)
2

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.

Más de NVIDIA/TensorRT

Todos los issues de NVIDIA/TensorRT

Issues similares

Más issues de C++

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.