Add AbstractOneTo and have OneTo be its subtype

[jishnub]

Currently, Base defines similar for Base.OneTo, with the understanding that offset axes will be handled elsewhere. However, Base.OneTo is just one possible one-based range, and there are others such as StaticArrays.SOneTo that exist in the ecosystem. Base doesn't provide a method to handle a combination of different one-based ranges in similar, which leaves the packages in an awkward position: they need to define methods like

similar(A::AbstractArray, ::Type{T}, shape::HeterogeneousShapeTuple) where {T} = similar(A, T, homogenize_shape(shape))

where HeterogeneousShapeTuple is defined as

Tuple{Vararg{Union{Integer, Base.OneTo, SOneTo}}}

https://github.com/JuliaArrays/StaticArrays.jl/blob/07c12450d1b3481dda4b503564ae4a5cb4e27ce4/src/abstractarray.jl#L141-L146
Unfortunately, such methods are borderline type-piracy, as noted in JuliaArrays/StaticArrays.jl#1248. In particular, if the narrower Base method that handles Union{Integer, OneTo} is removed, then this method explicitly becomes pirating.

A solution to this situation is to have Base handle all one-based ranges, such that arbitrary combinations of one-based ranges hit fallback methods in Base. This PR is a first step in this direction. We add the abstract type AbstractOneTo, and have OneTo be its subtype. We also add methods to similar and reshape that accept AbstractOneTo arguments. This makes it unnecessary for packages to dispatch on awkward combinations of Union{Integer, OneTo} and custom one-based axes, as the base implementation would handle such cases already.

There may be other methods that accept an AbstractOneTo instead of a OneTo, but these may be addressed in separate PRs. Also, there may be one-based ranges that can't subtype AbstractOneTo, and a full solution that accepts such ranges as well needs to be implemented through a trait. This may also be handled in a separate PR.

[jishnub]

Test failure onx86-64 apple darwin is

Error in testset Compiler/codegen:
Test Failed at /Users/julia/.julia/scratchspaces/a66863c6-20e8-4ff4-8a62-49f30b1f605e/agent-cache/default-grannysmith-C07ZM05NJYVY.0/build/default-grannysmith-C07ZM05NJYVY-0/julialang/julia-master/julia-3742f4f789/share/julia/Compiler/test/codegen.jl:412
  Expression: abs(#= /Users/julia/.julia/scratchspaces/a66863c6-20e8-4ff4-8a62-49f30b1f605e/agent-cache/default-grannysmith-C07ZM05NJYVY.0/build/default-grannysmith-C07ZM05NJYVY-0/julialang/julia-master/julia-3742f4f789/share/julia/Compiler/test/codegen.jl:412 =# @allocated(f_dict_hash_alloc()) / #= /Users/julia/.julia/scratchspaces/a66863c6-20e8-4ff4-8a62-49f30b1f605e/agent-cache/default-grannysmith-C07ZM05NJYVY.0/build/default-grannysmith-C07ZM05NJYVY-0/julialang/julia-master/julia-3742f4f789/share/julia/Compiler/test/codegen.jl:412 =# @allocated(g_dict_hash_alloc()) - 1) < 0.1
   Evaluated: 0.2642807983482449 < 0.1

I wonder if someone might be able to comment on why this fails?

Edit: oddly, this test passed on the second invocation, so it's unlikely to be related to this PR.