use ProjectTo functor everywhere, and update to v1 of CRC and CRTU

[oxinabox]

This is the partner to JuliaDiff/ChainRulesCore.jl#385
it matches against #457

It is much cleaner and it should be good again inference problems from closing over types,
as the type gets put into a type-parameter of project_A and project_B

Following minimized version works find for me, haven't tested real thing properly.

using Test, LinearAlgebra, ChainRulesCore
functionprojectto_infers(x::AbstractArray)
project =ProjectTo(x)
functionmyclosure(y)
returnproject(y)
endreturn myclosure
endconst pb =projectto_infers(Diagonal([1.0, 2.0]))
@inferredpb([1.02.0; 3.04.0])

[oxinabox]

I have made changes that make the following all work for me locally

using ChainRulesCore, ChainRules, Test
@inferred(
rrule(hvcat, 1, rand(3)', transpose(rand(3)))[2](rand(2,3))
)
using ChainRulesTestUtils
test_rrule(hvcat, 1, rand(3)', transpose(rand(3)) ⊢rand(1,3))

[mcabbott]

One way you could avoid boilerplate here is to define a macro which turns

@ProjectEverything function rrule(f, x, ys...)
# do stuff
return y, back
end

into

function rrule(f, x, ys...)
# do stuff
extra = MultiProject(f, x, ys...)
return y, extra∘back
end

In fact even without a macro, a MultiProject which you compose like this would cut a lot of lines here. It's just this, right? (untested)

struct MultiProject{T}
funs::T
function MultiProject(xs...)
funs = map(xs) do x
if x isa Number || x isa AbstractArray
ProjectTo
else
identity
end
end
new{typeof(funs)}(funs)
end
end
(m::MultiProject)(dxs::Tuple) = map((f,dx) -> f(dx), m.funs, dxs)

[mzgubic]

still failing:
y, pb = rrule(/, rand(3), rand(3)); @inferred pb(rand(3, 3)) (rrule inference)
y, pb = rrule(rc, sum, sum, [[2.0, 4.0], [4.0,1.9]]); pb(y) (can't deal with project receiving an InplaceableThunk, rc is the fake ruleconfig)
test_rrule(dot, Diagonal(rand(2)), rand(2, 2)) (unthunk inference)
test_rrule(adjoint, rand(2, 3); output_tangent=rand(3,2)) (rrule inference)
y, pb = rrule(Symmetric, rand(3, 3), :U); @inferred pb(ΔΩ)[2] (rrule inference)

it seems that solving

julia>@inferredProjectTo(rand(3, 3))(Diagonal(rand(3)))
ERROR:return type Diagonal{Float64, Vector{Float64}} does not match inferred return type Union{Base.ReshapedArray{Float64, 2, Diagonal{Float64, Vector{Float64}}, Tuple{Base.MultiplicativeInverses.SignedMultiplicativeInverse{Int64}}}, Diagonal{Float64, Vector{Float64}}}

could solve some (maybe all?) of the inference issues

[oxinabox]

For the inference problems, I am happy enough to open an issue and come back to them.
For

y, pb = rrule(rc, sum, sum, [[2.0, 4.0], [4.0,1.9]]); pb(y) (can't deal with project receiving an InplaceableThunk, rc is the fake ruleconfig)

Can we insert a unthunk here before the project ?
We do not always want to project InplacableThunks via unthunking but in this case we do,
The kinda function people realistically use with sum(f,...) are ones that we would basically never want to preserve thunking through.

[mzgubic]

Is this permanent or until we have something like JuliaDiff/ChainRulesCore.jl#393?

[oxinabox]

Yeah,
more generally I hate the InplacableThunks that are just deferring broadcast materialization.
They are so boilerplatey.
That has its own issue JuliaDiff/ChainRulesCore.jl#274
and is probably going to be easier to solve.

I am more than happy to delete them.
Can always add them back