Remove unnecessary fusiontree conversions

Author: kshyatt

ext/TensorKitCUDAExt/TensorKitCUDAExt.jl

@@ -18,70 +18,6 @@ using Random
 
 include("cutensormap.jl")
 
-# for ambiguity
-function Base.convert(A::Type{CuArray}, f::TensorKit.FusionTree{I, 0}) where {I}
-    return convert(A, TensorKit.fusiontensor(unit(I), unit(I), unit(I)))[1, 1, :]
-end
-function Base.convert(A::Type{CuArray}, f::TensorKit.FusionTree{I, 1}) where {I}
-    c = f.coupled
-    if f.isdual[1]
-        sqrtdc = TensorKit.sqrtdim(c)
-        Zcbartranspose = sqrtdc * convert(A, TensorKit.fusiontensor(dual(c), c, unit(c)))[:, :, 1, 1]
-        X = conj!(Zcbartranspose) # we want Zcbar^†
-    else
-        X = convert(A, TensorKit.fusiontensor(c, unit(c), c))[:, 1, :, 1, 1]
-    end
-    return X
-end
-# needed because the Int eltype isn't supported by CuTENSOR
-function Base.convert(A::Type{CuArray}, f::TensorKit.FusionTree{I, 2}) where {I}
-    a, b = f.uncoupled
-    isduala, isdualb = f.isdual
-    c = f.coupled
-    μ = (TensorKit.FusionStyle(I) isa TensorKit.GenericFusion) ? f.vertices[1] : 1
-    C = convert(A, TensorKit.fusiontensor(a, b, c))[:, :, :, μ]
-    X = C
-    fX = reinterpret(Float64, X)
-    if isduala
-        Za = convert(A, TensorKit.FusionTree((a,), a, (isduala,), ()))
-        # reinterpret all these as Float64 since cuTENSOR does not support Int64
-        fZa = reinterpret(Float64, Za)
-        @tensor fX[a′, b, c] := fZa[a′, a] * fX[a, b, c]
-    end
-    if isdualb
-        Zb = convert(A, TensorKit.FusionTree((b,), b, (isdualb,), ()))
-        fZb = reinterpret(Float64, Zb)
-        @tensor fX[a, b′, c] := fZb[b′, b] * fX[a, b, c]
-    end
-    return X
-end
-
-function Base.convert(A::Type{CuArray}, f::TensorKit.FusionTree{I, N}) where {I, N}
-    tailout = (f.innerlines[1], TensorKit.TupleTools.tail2(f.uncoupled)...)
-    isdualout = (false, TensorKit.TupleTools.tail2(f.isdual)...)
-    ftail = TensorKit.FusionTree(tailout, f.coupled, isdualout, Base.tail(f.innerlines), Base.tail(f.vertices))
-    Ctail = convert(A, ftail)
-    f₁ = TensorKit.FusionTree(
-        (f.uncoupled[1], f.uncoupled[2]), f.innerlines[1],
-        (f.isdual[1], f.isdual[2]), (), (f.vertices[1],)
-    )
-    C1 = convert(A, f₁)
-    dtail = size(Ctail)
-    d1 = size(C1)
-    X = similar(C1, (d1[1], d1[2], Base.tail(dtail)...))
-    trivialtuple = ntuple(identity, Val(N))
-    # reinterpret all these as Float64 since cuTENSOR does not support Int64
-    fX = reinterpret(Float64, X)
-    fC1 = reinterpret(Float64, C1)
-    fCtail = reinterpret(Float64, Ctail)
-    TensorKit.TensorOperations.tensorcontract!(
-        fX,
-        fC1, ((1, 2), (3,)), false,
-        fCtail, ((1,), Base.tail(trivialtuple)), false,
-        ((trivialtuple..., N + 1), ())
-    )
-    return X
-end
 # TODO
 # add VectorInterface extensions for proper CUDA promotion
 function TensorKit.VectorInterface.promote_add(TA::Type{<:CUDA.StridedCuMatrix{Tx}}, TB::Type{<:CUDA.StridedCuMatrix{Ty}}, α::Tα = TensorKit.VectorInterface.One(), β::Tβ = TensorKit.VectorInterface.One()) where {Tx, Ty, Tα, Tβ}