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`gradient!` leaves part of the result unwritten when `f` does not depend on its argument

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@devmotion 已经在做这个了。

开始于 2026年9月1日。

  • #848 来自 @devmotion —— 未关闭

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难度
3/5
预计耗时
1-2 天
新手友好度
78/100
Issue 类型
缺陷
描述清晰度
描述清楚
活跃度
活跃
技术栈
julia
领域
backend-api-design

调研方向

从 src/gradient.jl 中 extract_gradient! 附近开始，检查在进行 array 和 DiffResults 梯度提取时如何分派嵌套的 Dual 值。重现所提供的嵌套微分示例，然后验证梯度会向每个条目写入零，并验证 DiffResult 形式可以在没有转换错误的情况下完成。为这些情况添加或运行针对性的回归测试。

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描述

When f does not depend on its argument but carries a perturbation from an enclosing differentiation, ForwardDiff.gradient! writes only part of the result and leaves the rest exactly as it found it. The gradient is zero in this situation, so every entry should be written.

using ForwardDiff, DiffResults
x = [1.0, 2.0, 3.0]

ForwardDiff.derivative(1.0) do a
    out = fill(a * 111.0, 3)                    # a buffer with recognisable junk
    ForwardDiff.gradient!(out, z -> a * 2.0, x)
    @show ForwardDiff.value.(out)               # [0.0, 111.0, 111.0], should be [0.0, 0.0, 0.0]
    return zero(a)
end

The allocating form has the same defect against the buffer it just allocated, so it returns uninitialized memory. That is often zero, which hides the bug, but not always:

julia> ForwardDiff.derivative(1.0) do a
           @show ForwardDiff.gradient(z -> a * 2.0, x)
           return zero(a)
       end
gradient = Dual{…}[Dual(0.0,0.0), Dual(2.3611913415e-314,2.361521164e-314), Dual(5.0e-324,2.260386021e-314)]

The DiffResult form errors instead:

ForwardDiff.derivative(1.0) do a
    r = DiffResults.GradientResult(fill(a * 0.0, 3))
    ForwardDiff.gradient!(r, z -> a * 2.0, x)
    return zero(a)
end
# ERROR: MethodError: Cannot `convert` an object of type Dual{…} to an object of type Float64

The StaticArray path is correct and returns [0.0, 0.0, 0.0], because its @generated extract_gradient is built from length(x) and uses the indexed partials(T, y, i).

Cause

src/gradient.jl#L65-L72:

extract_gradient!(::Type{T}, result::AbstractArray, y::Real) where {T} = fill!(result, zero(y))
function extract_gradient!(::Type{T}, result::AbstractArray, dual::Dual) where {T}
    idxs = structural_eachindex(result)
    for (i, idx) in zip(1:npartials(dual), idxs)
        result[idx] = partials(T, dual, i)
    end
    return result
end

A Dual{S,V,N} with S ≺ T carries no T-perturbation, so its gradient is zero everywhere and the ::Real method above is what it needs — but it is a Dual, so it takes the second method, where npartials reports N for the wrong layer. Whenever that N is smaller than length(x) the tail of the result is never written.

L59-L63 shares the dispatch and then passes partials(T, dual) to DiffResults.gradient!; for this case that is zero(dual), a scalar Dual rather than a Partials, which is the same shape problem as in #846.

Suggested fix

Dispatch on Dual{T} rather than on Dual, so a value carrying no T-perturbation falls through to the ::Real method, which already fills the whole result with zeros.

Version: ForwardDiff master (v1.4.5), Julia 1.12.7, StaticArrays 1.9.19.

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