Making `smf` ForwardDiff-friendly

We should be able to tell ForwardDiff that the derivative of `smf` is `logdensityof`. Here are timings for these separately for a `StdNormal`:
```julia
julia> using MeasureBase, ForwardDiff, BenchmarkTools

julia> using ForwardDiff: Dual

julia> @btime smf(StdNormal(), x) setup=(x=randn())
5.660 ns (0 allocations: 0 bytes)
0.373722

julia> @btime logdensityof(StdNormal(), x) setup=(x=randn())
2.294 ns (0 allocations: 0 bytes)
-0.935558
```
If we compute both, there's no extra overhead, and we even save a little;
```julia
julia> @btime (smf(StdNormal(), x), logdensityof(StdNormal(), x)) setup=(x=randn())
7.151 ns (0 allocations: 0 bytes)
(0.206069, -1.25525)
```

It would be good to "teach" ForwardDiff to do this, because it's currently much slower:
```julia
julia> @btime ForwardDiff.derivative(x -> smf(StdNormal(), x), x) setup=(x=randn())
13.181 ns (0 allocations: 0 bytes)
0.122241

julia> @btime smf(StdNormal(), Dual{}(x, one(x))) setup=(x=randn())
14.506 ns (0 allocations: 0 bytes)
Dual{Nothing}(0.572796,0.392282)
```

I'd think this ought to do it:
```julia
julia> function MeasureBase.smf(μ::StdNormal, x::Dual{TAG}) where TAG
val = ForwardDiff.value(x)
Δ = ForwardDiff.partials(x)
Dual{TAG}(smf(μ, val), Δ * densityof(μ, val))
end
```

But it doesn't:
```julia
julia> @btime ForwardDiff.derivative(x -> smf(StdNormal(), x), x) setup=(x=randn())
13.081 ns (0 allocations: 0 bytes)
0.370831

julia> @btime smf(StdNormal(), Dual{}(x, one(x))) setup=(x=randn())
14.898 ns (0 allocations: 0 bytes)
Dual{Nothing}(0.724177,0.334163)
```

How can we get this working properly?

It should also work if we call `ForwardDiff.derivative(smf(StdNormal()), x)`