maybescalarize instead of can_turbo?

[chriselrod]

See here for some background: JuliaSIMD/StrideArrays.jl#62 (comment)

The idea is not to check whether a function has a SIMD implementation, but to scalarize in case we do not have one.

@inline function maybescalarize(f::F, x::Vararg{Any,K}) where {K}
    T = Base.promote_op(f, x...)
    T === Union{} && scalarize(f, x...)
    return f(x...)
end
using VectorizationBase: AbstractSIMDVector
@inline function scalarize(f::F, x::AbstractSIMDVector{W}) where {W}
    Vec(ntuple(f ∘ x, Val(8))...)
end

Two things are missing:

1. Add all the missing scalarize methods I didn't include above. This means VecUnroll and things that are neither VecUnroll or AbstractSIMDVector. We also need to consider functions with all sorts of different numbers of arguments. Thankfully, we don't need to consider memory operations like vload or vstore, because these should always vectorize (our checks on the arrays should handle that).
2. Updating LV's code generation to call maybescalarize(f, args...) instead of f(args...). This might only mean editing here:
   

   LoopVectorization.jl/src/modeling/costs.jl

   Lines 17 to 23 in 35f8310

   	function callexpr(instr::Instruction)
   	if instr.mod === :LoopVectorization
   	Expr(:call, lv(instr.instr))
   	else#if instr.mod === :Main
   	Expr(:call, instr.instr)
   	end
   	end

   
   and this file
   https://github.com/JuliaSIMD/LoopVectorization.jl/blob/main/src/codegen/lower_compute.jl
   because all of the other places are probably related to load/store or address calculation.

With respect to JuliaSIMD/StrideArrays.jl#62 this will vectorize the call (exactly what we want), because our type check will be using the actually correct argument types to the function, and not just Vec{2,Int}.

Seems like this should be fairly straightforward, and may be a nice improvement.

I'd be happy to provide instructions/guidance/answer questions if anyone wants to take this on!