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Enable NaN-safe mode by default

Abierto
  • ¿Algo no está bien?
#857 0 comentarios 1 reacción 0 asignados Ver en GitHub

@devmotion ya está trabajando en esto.

Desde el 12/10/2026.

  • #858 de @devmotion — abierto
  • #859 de @KristofferC — abierto

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Área
backend

Línea de trabajo

Read the discussion in #719 and the behavior changes in #777 and #856, then trace nansafe_mode and the Dual^Real branch. Decide whether NaN-safe mode should become the default, accounting for correctness and the reported performance cost; done means the default behavior and opt-out preference are settled and documented.

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

Descripción

I'd like to propose enabling NaN-safe mode by default, and making the current behaviour opt-in via the nansafe_mode preference.

This was discussed in #719 (starting at https://github.com/JuliaDiff/ForwardDiff.jl/issues/719#issuecomment-2471545577), but since switching the default is not breaking, the question was not resolved there and #719 was closed with the 1.0 release. So I open a separate issue for it, as suggested in https://github.com/JuliaDiff/ForwardDiff.jl/issues/719#issuecomment-2474602850.

IMO NaN-safe mode is not primarily about NaNs but about the definition of dual numbers: a dual number with a zero partial is unperturbed in that direction, so it must not contribute to the derivative in that direction, regardless of the derivative of the primal function at that point. The default mode violates this whenever the derivative is not finite (0 * Inf and 0 * NaN are NaN). Our documentation even says so ("oops, this NaN should be 0.0").

As a consequence, the default mode returns incorrect derivatives:

julia> f(v) = sqrt(v[1]) + v[2];

julia> ForwardDiff.gradient(f, [0.0, 1.0])  # ∂f/∂v[2] = 1
2-element Vector{Float64}:
 Inf
 NaN

Moreover, results depend on implementation details that should be irrelevant:

  • whether an unperturbed argument is a Real or a Dual with zero partials (see #856),
  • the chunk size: with the existing all-or-nothing check in the Dual^Real branch of ^, v[1]^0.5 + v[2] returns [Inf, 1.0] with Chunk{1} and [Inf, NaN] with Chunk{2}.

In NaN-safe mode, all of these examples return [Inf, 1.0] for any chunk size (since #777 the check is applied to each partial separately).

IMO NaN-safe mode should be the default regardless of its performance impact. Correctness should be the default, and faster but incorrect results should be opt-in (as with, e.g., @fastmath) - one can always be fast by returning nonsense. The documentation states a performance impact of ~5-10%, but that's outdated: in the Rosenbrock benchmark in #777 the gradient took 853 μs with NaN-safe mode vs 597 μs without (median), i.e., about 40% more. That's worth documenting for users who want to opt out, but it shouldn't decide the default. Also note that since #777 NaN-safe mode does not turn the partials multiplication into branchy code anymore (one of the concerns in https://github.com/JuliaDiff/ForwardDiff.jl/issues/719#issuecomment-2484955066): _mul_partials compiles to SIMD instructions with an additional comparison and mask per partial (see the native code in #777).

Lenguaje dominante
Julia
Estrellas
1k
Forks
162
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Primeros pasos

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  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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