use a functor for projection

docs/Manifest.toml

@@ -13,7 +13,7 @@ uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 deps = ["Compat", "LinearAlgebra", "SparseArrays"]
 path = ".."
 uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
-version = "0.10.7"
+version = "0.10.11"
 
 [[Compat]]
 deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]

docs/src/api.md

@@ -41,6 +41,11 @@ Pages = ["config.jl"]
 Private = false
 ```
 
+## ProjectTo
+```@docs
+ProjectTo
+```
+
 ## Internal
 ```@docs
 ChainRulesCore.AbstractTangent

docs/src/writing_good_rules.md

@@ -63,6 +63,40 @@ Examples being:
 - There is only one derivative being returned, so from the fact that the user called
   `frule`/`rrule` they clearly will want to use that one.
 
+## Ensure you remain in the primal's subspace (i.e. use `ProjectTo` appropriately)
+
+Rules with abstractly-typed arguments may return incorrect answers when called with certain concrete types.
+A classic example is the matrix-matrix multiplication rule, a naive definition of which follows:
+```julia
+function rrule(::typeof(*), A::AbstractMatrix, B::AbstractMatrix)
+    function times_pullback(ȳ)
+        dA = ȳ * B'
+        dB = A' * ȳ
+        return NoTangent(), dA, dB
+    end
+    return A * B, times_pullback 
+end
+```
+When computing `*(A, B)`, where `A isa Diagonal` and `B isa Matrix`, the output will be a `Matrix`.
+As a result, `ȳ` in the pullback will be a `Matrix`, and consequently `dA` for a `A isa Diagonal` will be a `Matrix`, which is wrong.
+Not only is it the wrong type, but it can contain non-zeros off the diagonal, which is not possible, it is outside of the subspace.
+While a specialised rules can indeed be written for the `Diagonal` case, there are many other types and we don't want to be forced to write a rule for each of them.
+Instead, `project_A = ProjectTo(A)` can be used (outside the pullback) to extract an object that knows how to project onto the type of `A` (e.g. also knows the size of the array).
+This object can be called with a tangent `ȳ * B'`, by doing `project_A(ȳ * B')`, to project it on the tangent space of `A`.
+The correct rule then looks like
+```julia
+function rrule(::typeof(*), A::AbstractMatrix, B::AbstractMatrix)
+    project_A = ProjectTo(A)
+    project_B = ProjectTo(B)
+    function times_pullback(ȳ)
+        dA = ȳ * B'
+        dB = A' * ȳ
+        return NoTangent(), project_A(dA), project_B(dB)
+    end
+    return A * B, times_pullback
+end
+```
+
 ## Structs: constructors and functors
 
 To define an `frule` or `rrule` for a _function_ `foo` we dispatch on the type of `foo`, which is `typeof(foo)`.

src/ChainRulesCore.jl

@@ -10,7 +10,7 @@ export RuleConfig, HasReverseMode, NoReverseMode, HasForwardsMode, NoForwardsMod
 export frule_via_ad, rrule_via_ad
 # definition helper macros
 export @non_differentiable, @scalar_rule, @thunk, @not_implemented
-export canonicalize, extern, unthunk  # differential operations
+export ProjectTo, canonicalize, extern, unthunk  # differential operations
 export add!!  # gradient accumulation operations
 # differentials
 export Tangent, NoTangent, InplaceableThunk, Thunk, ZeroTangent, AbstractZero, AbstractThunk
@@ -26,6 +26,7 @@ include("differentials/notimplemented.jl")
 
 include("differential_arithmetic.jl")
 include("accumulation.jl")
+include("projection.jl")
 
 include("config.jl")
 include("rules.jl")

src/differentials/composite.jl

@@ -129,6 +129,7 @@ backing(x::NamedTuple) = x
 backing(x::Dict) = x
 backing(x::Tangent) = getfield(x, :backing)
 
+# For generic structs
 function backing(x::T)::NamedTuple where T
     # note: all computation outside the if @generated happens at runtime.
     # so the first 4 lines of the branchs look the same, but can not be moved out.

src/projection.jl

@@ -0,0 +1,144 @@
+"""
+    ProjectTo(x::T)
+
+Returns a `ProjectTo{T,...}` functor able to project a differential `dx` onto the same tangent space as `x`.
+This functor encloses over what ever is needed to be able to be able to do that projection.
+For example, when projecting `dx=ZeroTangent()` on an array `P=Array{T, N}`, the size of `x`
+is not available from `P`, so it is stored in the functor.
+
+    (::ProjectTo{T})(dx)
+
+Projects the differential `dx` on the onto the tangent space used to create the `ProjectTo`.
+"""
+struct ProjectTo{P,D<:NamedTuple}
+    info::D
+end
+ProjectTo{P}(info::D) where {P,D<:NamedTuple} = ProjectTo{P,D}(info)
+ProjectTo{P}(; kwargs...) where {P} = ProjectTo{P}(NamedTuple(kwargs))
+
+backing(project::ProjectTo) = getfield(project, :info)
+Base.getproperty(p::ProjectTo, name::Symbol) = getproperty(backing(p), name)
+Base.propertynames(p::ProjectTo) = propertynames(backing(p))
+
+function Base.show(io::IO, project::ProjectTo{T}) where {T}
+    print(io, "ProjectTo{")
+    show(io, T)
+    print(io, "}")
+    if isempty(backing(project))
+        print(io, "()")
+    else
+        show(io, backing(project))
+    end
+end
+
+# fallback (structs)
+function ProjectTo(x::T) where {T}
+    # Generic fallback for structs, recursively make `ProjectTo`s all their fields
+    fields_nt::NamedTuple = backing(x)
+    return ProjectTo{T}(map(ProjectTo, fields_nt))
+end
+function (project::ProjectTo{T})(dx::Tangent) where {T}
+    sub_projects = backing(project)
+    sub_dxs = backing(canonicalize(dx))
+    _call(f, x) = f(x)
+    return construct(T, map(_call, sub_projects, sub_dxs))
+end
+
+# should not work for Tuples and NamedTuples, as not valid tangent types
+function ProjectTo(x::T) where {T<:Union{<:Tuple,NamedTuple}}
+    return throw(
+        ArgumentError("The `x` in `ProjectTo(x)` must be a valid differential, not $x")
+    )
+end
+
+# Generic
+(project::ProjectTo)(dx::AbstractThunk) = project(unthunk(dx))
+(::ProjectTo{T})(dx::T) where {T} = dx  # not always true, but we can special case for when it isn't
+(::ProjectTo{T})(dx::AbstractZero) where {T} = zero(T)
+
+# Number
+ProjectTo(::T) where {T<:Number} = ProjectTo{T}()
+(::ProjectTo{T})(dx::Number) where {T<:Number} = convert(T, dx)
+(::ProjectTo{T})(dx::Number) where {T<:Real} = convert(T, real(dx))
+
+# Arrays 
+ProjectTo(xs::T) where {T<:Array} = ProjectTo{T}(; elements=map(ProjectTo, xs))
+function (project::ProjectTo{T})(dx::Array) where {T<:Array}
+    _call(f, x) = f(x)
+    return T(map(_call, project.elements, dx))
+end
+function (project::ProjectTo{T})(dx::AbstractZero) where {T<:Array}
+    return T(map(proj -> proj(dx), project.elements))
+end
+(project::ProjectTo{<:Array})(dx::AbstractArray) = project(collect(dx))
+
+# Arrays{<:Number}: optimized case so we don't need a projector per element
+function ProjectTo(x::T) where {E<:Number,T<:Array{E}}
+    return ProjectTo{T}(; element=ProjectTo(zero(E)), size=size(x))
+end
+(project::ProjectTo{<:Array{T}})(dx::Array) where {T<:Number} = project.element.(dx)
+function (project::ProjectTo{<:Array{T}})(dx::AbstractZero) where {T<:Number}
+    return zeros(T, project.size)
+end
+function (project::ProjectTo{<:Array{T}})(dx::Tangent{<:SubArray}) where {T<:Number}
+    return project(dx.parent)
+end
+
+# Diagonal
+ProjectTo(x::T) where {T<:Diagonal} = ProjectTo{T}(; diag=ProjectTo(diag(x)))
+(project::ProjectTo{T})(dx::AbstractMatrix) where {T<:Diagonal} = T(project.diag(diag(dx)))
+(project::ProjectTo{T})(dx::AbstractZero) where {T<:Diagonal} = T(project.diag(dx))
+
+# :data, :uplo fields
+for SymHerm in (:Symmetric, :Hermitian)
+    @eval begin
+        function ProjectTo(x::T) where {T<:$SymHerm}
+            return ProjectTo{T}(; uplo=Symbol(x.uplo), parent=ProjectTo(parent(x)))
+        end
+        function (project::ProjectTo{<:$SymHerm})(dx::AbstractMatrix)
+            return $SymHerm(project.parent(dx), project.uplo)
+        end
+        function (project::ProjectTo{<:$SymHerm})(dx::AbstractZero)
+            return $SymHerm(project.parent(dx), project.uplo)
+        end
+        function (project::ProjectTo{<:$SymHerm})(dx::Tangent)
+            return $SymHerm(project.parent(dx.data), project.uplo)
+        end
+    end
+end
+
+# :data field
+for UL in (:UpperTriangular, :LowerTriangular)
+    @eval begin
+        ProjectTo(x::T) where {T<:$UL} = ProjectTo{T}(; parent=ProjectTo(parent(x)))
+        (project::ProjectTo{<:$UL})(dx::AbstractMatrix) = $UL(project.parent(dx))
+        (project::ProjectTo{<:$UL})(dx::AbstractZero) = $UL(project.parent(dx))
+        (project::ProjectTo{<:$UL})(dx::Tangent) = $UL(project.parent(dx.data))
+    end
+end
+
+# Transpose
+ProjectTo(x::T) where {T<:Transpose} = ProjectTo{T}(; parent=ProjectTo(parent(x)))
+function (project::ProjectTo{<:Transpose})(dx::AbstractMatrix)
+    return transpose(project.parent(transpose(dx)))
+end
+(project::ProjectTo{<:Transpose})(dx::AbstractZero) = transpose(project.parent(dx))
+
+# Adjoint
+ProjectTo(x::T) where {T<:Adjoint} = ProjectTo{T}(; parent=ProjectTo(parent(x)))
+(project::ProjectTo{<:Adjoint})(dx::AbstractMatrix) = adjoint(project.parent(adjoint(dx)))
+(project::ProjectTo{<:Adjoint})(dx::AbstractZero) = adjoint(project.parent(dx))
+
+# PermutedDimsArray
+ProjectTo(x::P) where {P<:PermutedDimsArray} = ProjectTo{P}(; parent=ProjectTo(parent(x)))
+function (project::ProjectTo{<:PermutedDimsArray{T,N,perm,iperm,AA}})(
+    dx::AbstractArray
+) where {T,N,perm,iperm,AA}
+    return PermutedDimsArray{T,N,perm,iperm,AA}(permutedims(project.parent(dx), perm))
+end
+function (project::ProjectTo{P})(dx::AbstractZero) where {P<:PermutedDimsArray}
+    return P(project.parent(dx))
+end
+
+# SubArray
+ProjectTo(x::T) where {T<:SubArray} = ProjectTo(copy(x))  # don't project on to a view, but onto matching copy