Add support for eigvals, svdvals and diag, diagm.

ext/TensorKitChainRulesCoreExt.jl

@@ -224,6 +224,20 @@ function ChainRulesCore.rrule(::typeof(TensorKit.tsvd!), t::AbstractTensorMap;
     return (U′, Σ′, V′, ϵ), tsvd!_pullback
 end
 
+function ChainRulesCore.rrule(::typeof(LinearAlgebra.svdvals!), t::AbstractTensorMap)
+    U, S, V = tsvd(t)
+    s = diag(S)
+    project_t = ProjectTo(t)
+
+    function svdvals_pullback(Δs′)
+        Δs = unthunk(Δs′)
+        ΔS = diagm(codomain(S), domain(S), Δs)
+        return NoTangent(), project_t(U * ΔS * V)
+    end
+
+    return s, svdvals_pullback
+end
+
 function ChainRulesCore.rrule(::typeof(TensorKit.eig!), t::AbstractTensorMap; kwargs...)
     D, V = eig(t; kwargs...)
 
@@ -266,6 +280,21 @@ function ChainRulesCore.rrule(::typeof(TensorKit.eigh!), t::AbstractTensorMap; k
     return (D, V), eigh!_pullback
 end
 
+function ChainRulesCore.rrule(::typeof(LinearAlgebra.eigvals!), t::AbstractTensorMap;
+                              sortby=nothing, kwargs...)
+    @assert sortby === nothing "only `sortby=nothing` is supported"
+    (D, _), eig_pullback = rrule(TensorKit.eig!, t; kwargs...)
+    d = diag(D)
+    project_t = ProjectTo(t)
+    function eigvals_pullback(Δd′)
+        Δd = unthunk(Δd′)
+        ΔD = diagm(codomain(D), domain(D), Δd)
+        return NoTangent(), project_t(eig_pullback((ΔD, ZeroTangent()))[2])
+    end
+
+    return d, eigvals_pullback
+end
+
 function ChainRulesCore.rrule(::typeof(leftorth!), t::AbstractTensorMap; alg=QRpos())
     alg isa TensorKit.QR || alg isa TensorKit.QRpos ||
         error("only `alg=QR()` and `alg=QRpos()` are supported")

src/tensors/factorizations.jl

@@ -36,6 +36,11 @@ function tsvd(t::AbstractTensorMap, (p₁, p₂)::Index2Tuple; kwargs...)
     return tsvd!(permute(t, (p₁, p₂); copy=true); kwargs...)
 end
 
+LinearAlgebra.svdvals(t::AbstractTensorMap) = LinearAlgebra.svdvals!(copy(t))
+function LinearAlgebra.svdvals!(t::AbstractTensorMap)
+    return SectorDict(c => LinearAlgebra.svdvals!(b) for (c, b) in blocks(t))
+end
+
 """
     leftorth(t::AbstractTensorMap, (leftind, rightind)::Index2Tuple;
                 alg::OrthogonalFactorizationAlgorithm = QRpos()) -> Q, R
@@ -168,6 +173,14 @@ function LinearAlgebra.eigen(t::AbstractTensorMap, (p₁, p₂)::Index2Tuple;
     return eigen!(permute(t, (p₁, p₂); copy=true); kwargs...)
 end
 
+function LinearAlgebra.eigvals(t::AbstractTensorMap; kwargs...)
+    return LinearAlgebra.eigvals!(copy(t); kwargs...)
+end
+function LinearAlgebra.eigvals!(t::AbstractTensorMap; kwargs...)
+    return SectorDict(c => complex(LinearAlgebra.eigvals!(b; kwargs...))
+                      for (c, b) in blocks(t))
+end
+
 """
     eig(t::AbstractTensor, (leftind, rightind)::Index2Tuple; kwargs...) -> D, V
 

src/tensors/linalg.jl

@@ -153,6 +153,19 @@ function isometry(::Type{A},
     return t
 end
 
+# Diagonal tensors
+# ----------------
+# TODO: consider adding a specialised DiagonalTensorMap type
+function LinearAlgebra.diag(t::AbstractTensorMap)
+    return SectorDict(c => LinearAlgebra.diag(b) for (c, b) in blocks(t))
+end
+function LinearAlgebra.diagm(codom::VectorSpace, dom::VectorSpace, v::SectorDict)
+    return TensorMap(SectorDict(c => LinearAlgebra.diagm(blockdim(codom, c),
+                                                         blockdim(dom, c), b)
+                                for (c, b) in v), codom ← dom)
+end
+LinearAlgebra.isdiag(t::AbstractTensorMap) = all(LinearAlgebra.isdiag, values(blocks(t)))
+
 # In-place methods
 #------------------
 # Wrapping the blocks in a StridedView enables multithreading if JULIA_NUM_THREADS > 1

test/ad.jl

@@ -46,6 +46,27 @@ function FiniteDifferences.to_vec(t::AbstractTensorMap)
 end
 FiniteDifferences.to_vec(t::TensorKit.AdjointTensorMap) = to_vec(copy(t))
 
+# make sure that norms are computed correctly:
+function FiniteDifferences.to_vec(t::TensorKit.SectorDict)
+    T = scalartype(valtype(t))
+    vec = mapreduce(vcat, t; init=T[]) do (c, b)
+        return reshape(b, :) .* sqrt(dim(c))
+    end
+    vec_real = T <: Real ? vec : collect(reinterpret(real(T), vec))
+
+    function from_vec(x_real)
+        x = T <: Real ? x_real : reinterpret(T, x_real)
+        ctr = 0
+        return TensorKit.SectorDict(c => (n = length(b);
+                                          b′ = reshape(view(x, ctr .+ (1:n)), size(b)) ./
+                                               sqrt(dim(c));
+                                          ctr += n;
+                                          b′)
+                                    for (c, b) in t)
+    end
+    return vec_real, from_vec
+end
+
 function _randomize!(a::TensorMap)
     for b in values(blocks(a))
         copyto!(b, randn(size(b)))
@@ -68,12 +89,18 @@ end
 function ChainRulesCore.rrule(::typeof(TensorKit.tsvd), args...; kwargs...)
     return ChainRulesCore.rrule(tsvd!, args...; kwargs...)
 end
+function ChainRulesCore.rrule(::typeof(LinearAlgebra.svdvals), args...; kwargs...)
+    return ChainRulesCore.rrule(svdvals!, args...; kwargs...)
+end
 function ChainRulesCore.rrule(::typeof(TensorKit.eig), args...; kwargs...)
     return ChainRulesCore.rrule(eig!, args...; kwargs...)
 end
 function ChainRulesCore.rrule(::typeof(TensorKit.eigh), args...; kwargs...)
     return ChainRulesCore.rrule(eigh!, args...; kwargs...)
 end
+function ChainRulesCore.rrule(::typeof(LinearAlgebra.eigvals), args...; kwargs...)
+    return ChainRulesCore.rrule(eigvals!, args...; kwargs...)
+end
 function ChainRulesCore.rrule(::typeof(TensorKit.leftorth), args...; kwargs...)
     return ChainRulesCore.rrule(leftorth!, args...; kwargs...)
 end
@@ -355,5 +382,16 @@ Vlist = ((ℂ^2, (ℂ^3)', ℂ^3, ℂ^2, (ℂ^2)'),
             test_rrule(tsvd, C; atol, output_tangent=(ΔU, ΔS, ΔV, 0.0),
                        fkwargs=(; trunc=truncdim(2 * dim(c))))
         end
+
+        let D = LinearAlgebra.eigvals(C)
+            ΔD = diag(TensorMap(randn, complex(scalartype(C)), space(C)))
+            test_rrule(LinearAlgebra.eigvals, C; atol, output_tangent=ΔD,
+                       fkwargs=(; sortby=nothing))
+        end
+
+        let S = LinearAlgebra.svdvals(C)
+            ΔS = diag(TensorMap(randn, real(scalartype(C)), space(C)))
+            test_rrule(LinearAlgebra.svdvals, C; atol, output_tangent=ΔS)
+        end
     end
 end

test/tensors.jl

@@ -188,6 +188,14 @@ for V in spacelist
             @test Base.promote_typeof(t, tc) == typeof(tc)
             @test Base.promote_typeof(tc, t) == typeof(tc + t)
         end
+        @timedtestset "diag/diagm" begin
+            W = V1 ⊗ V2 ⊗ V3 ← V4 ⊗ V5
+            t = TensorMap(randn, ComplexF64, W)
+            d = LinearAlgebra.diag(t)
+            D = LinearAlgebra.diagm(codomain(t), domain(t), d)
+            @test LinearAlgebra.isdiag(D)
+            @test LinearAlgebra.diag(D) == d
+        end
         @timedtestset "Permutations: test via inner product invariance" begin
             W = V1 ⊗ V2 ⊗ V3 ⊗ V4 ⊗ V5
             t = Tensor(rand, ComplexF64, W)
@@ -408,7 +416,14 @@ for V in spacelist
                         @test UdU ≈ one(UdU)
                         VVd = V * V'
                         @test VVd ≈ one(VVd)
-                        @test U * S * V ≈ permute(t, ((3, 4, 2), (1, 5)))
+                        t2 = permute(t, ((3, 4, 2), (1, 5)))
+                        @test U * S * V ≈ t2
+
+                        s = LinearAlgebra.svdvals(t2)
+                        s′ = LinearAlgebra.diag(S)
+                        for (c, b) in s
+                            @test b ≈ s′[c]
+                        end
                     end
                 end
                 @testset "empty tensor" begin
@@ -458,6 +473,12 @@ for V in spacelist
                     t2 = permute(t, ((1, 3), (2, 4)))
                     @test t2 * V ≈ V * D
 
+                    d = LinearAlgebra.eigvals(t2; sortby=nothing)
+                    d′ = LinearAlgebra.diag(D)
+                    for (c, b) in d
+                        @test b ≈ d′[c]
+                    end
+
                     # Somehow moving these test before the previous one gives rise to errors
                     # with T=Float32 on x86 platforms. Is this an OpenBLAS issue? 
                     VdV = V' * V