Pullback for sparse-array vector product very inefficient

Our current rrule for sparse matrix vector products is very inefficient, and causes out-of-memory with large sparse CPU or GPU arrays. Our current rrule(*, sparse(A), x) is implemented like this

function rrule(
    ::typeof(*),
    A::AbstractVecOrMat{<:CommutativeMulNumber},
    B::AbstractVecOrMat{<:CommutativeMulNumber},
)
    project_A = ProjectTo(A)
    ...
        dA = @thunk(project_A(Ȳ * B'))
    ...
end

So we first compute a non-sparse Ȳ * B' (may easily exceed memory if A was very large but very sparse) and then project back to a sparse tangent.

The best way to fix this (at least if Ȳ' and 'B' are vectors) might be adding a specific "vector-outer-product" array type for read-only vector * adjoint-vector products (might be useful in general) that computes getindex on the fly. Or maybe we already have that somewhere?

Start at the rrule for sparse matrix–vector products described in the issue and trace how the dA thunk computes Ȳ * B'. Investigate whether an existing lazy vector-outer-product representation can avoid materializing a dense intermediate; done means sparse CPU and GPU cases no longer risk dense-memory allocation while preserving the sparse tangent result.