Le vendredi 22 avril 2016 à 09:15 -0400, Yichao Yu a écrit :
> On Fri, Apr 22, 2016 at 9:02 AM, Milan Bouchet-Valat wrote:
> >
> > Hi! Yet more explorations regarding inference and varargs/tuple
> > arguments.
> >
> > I have a function taking a tuple of vectors, and I need a way to
> > extract the element types of each of them, in order to create a Dict.
> > The following code works fine, but inference is not able to compute T
> > at compile time:
> > function f(x::Tuple)
> > T = Tuple{map(eltype, x)...}
> > Dict{T,Int}()
> > end
> >
> > @code_warntype f(([1,2], [1.,2.],))
> >
> > The same happens with varargs, i.e. f(x...).
> >
> > Is there any solution to this (other than generated functions)? Am I
> > missing an existing bug report again?
> This is the case `@pure` is replacing `@generated` on 0.5-dev (More
> flexible type computation without non-trivial code generation)
>
> ```
> julia> Base.@pure f(x) = Tuple{map(eltype, x.parameters)...}
> f (generic function with 1 method)
>
> julia> function g(x::Tuple)
> Dict{f(typeof(x)),Int}()
> end
> g (generic function with 1 method)
>
> julia> @code_warntype g(([1, 2], [1., 2.]))
> Variables:
> #self#::#g
> x::Tuple{Array{Int64,1},Array{Float64,1}}
>
> Body:
> begin # REPL[2], line 2:
> return
> (Dict{Tuple{Int64,Float64},Int64})()::Dict{Tuple{Int64,Float64},Int64}
> end::Dict{Tuple{Int64,Float64},Int64}
> ```
Great, thanks! I could have thought about this myself. Should I file an
issue about achieving this optimization automatically, though?
While I'm at it, I'm having problems with the next step:
function f(x::Tuple)
for (i, el) in enumerate(zip(x...))
@show i, el
end
end
@code_warntype f((1:2,))
Where does Union{Int64,Tuple{Int64}} come from? I've fixed zip() some
time ago to ensure it always returns a tuple...
Regards
