Improve ForwardDiff Dual N=0 handling with clear ArgumentError and regression test#927
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AJ0070 wants to merge 3 commits intoSciML:mainfrom
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Improve ForwardDiff Dual N=0 handling with clear ArgumentError and regression test#927AJ0070 wants to merge 3 commits intoSciML:mainfrom
AJ0070 wants to merge 3 commits intoSciML:mainfrom
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devmotion
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Mar 26, 2026
ext/LinearSolveForwardDiffExt.jl
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| if nA === 0 || nb === 0 | ||
| throw(ArgumentError( | ||
| "LinearSolve does not support ForwardDiff.Dual values with zero partials (N = 0). " * | ||
| "Use primal values with ForwardDiff.value(...) or construct Dual numbers with at least one partial." |
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ForwardDiff.value is internal, but more importantly it's also generally unsafe and hence usually one should use ForwardDiff.value(T, x) instead (which is internal as well though, so I don't think it belongs into a user-facing error message).
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Then I’ll just replace it with a note advising not to use non-Dual numbers.
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Yeah I don't think we should ever tell users to use ForwardDiff.value 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 😅 that's just asking for punishment.
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Checklist
What changed
ArgumentErrormessage to make the failure mode explicit and actionable.Additional context
This follows discussion on #914: zero-partial Duals are ill-formed for the intended AD path, but users should still get a clear error instead of an internal failure.
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