I was surprised by this:
using ForwardDiff: Dual, value
x1 = Dual.(1:4, 2:5, 3:6)
value.(float.(x1)) # Vector{Float16}
float.(value.(x1)) # Vector{Float64}
While one could argue that Julia should have different promotion / widening rules, it seems hard to argue that Dual types should disagree. This example caught me when trying to wrap a function which only accepts Float32 & Float64, as seems fairly common for C libraries.
Edit -- xref #419, about AbstractFloat.
And #362, about Int -> Float16. Example from here is ForwardDiff.derivative(first∘sincos, 1) isa Float16
I was surprised by this:
While one could argue that Julia should have different promotion / widening rules, it seems hard to argue that Dual types should disagree. This example caught me when trying to wrap a function which only accepts Float32 & Float64, as seems fairly common for C libraries.
Edit -- xref #419, about AbstractFloat.
And #362, about Int -> Float16. Example from here is
ForwardDiff.derivative(first∘sincos, 1) isa Float16