Start on GPU extensions (again) #320
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| module TensorKitAMDGPUExt | ||
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| using AMDGPU, AMDGPU.rocBLAS, LinearAlgebra | ||
| using AMDGPU: @allowscalar | ||
| import AMDGPU: rand as rocrand, rand! as rocrand!, randn as rocrandn, randn! as rocrandn! | ||
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| using TensorKit | ||
| using TensorKit.Factorizations | ||
| using TensorKit.Strided | ||
| using TensorKit.Factorizations: AbstractAlgorithm | ||
| using TensorKit: SectorDict, tensormaptype, scalar, similarstoragetype, AdjointTensorMap, scalartype | ||
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| using TensorKit.MatrixAlgebraKit | ||
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| using Random | ||
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| include("roctensormap.jl") | ||
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| const ROCDiagonalTensorMap{T, S} = DiagonalTensorMap{T, S, ROCVector{T, AMDGPU.Mem.HIPBuffer}} | ||
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| """ | ||
| ROCDiagonalTensorMap{T}(undef, domain::S) where {T,S<:IndexSpace} | ||
| # expert mode: select storage type `A` | ||
| DiagonalTensorMap{T,S,A}(undef, domain::S) where {T,S<:IndexSpace,A<:DenseVector{T}} | ||
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| Construct a `DiagonalTensorMap` with uninitialized data. | ||
| """ | ||
| function ROCDiagonalTensorMap{T}(::UndefInitializer, V::TensorMapSpace) where {T} | ||
| (numin(V) == numout(V) == 1 && domain(V) == codomain(V)) || | ||
| throw(ArgumentError("DiagonalTensorMap requires a space with equal domain and codomain and 2 indices")) | ||
| return ROCDiagonalTensorMap{T}(undef, domain(V)) | ||
| end | ||
| function ROCDiagonalTensorMap{T}(::UndefInitializer, V::ProductSpace) where {T} | ||
| length(V) == 1 || | ||
| throw(ArgumentError("DiagonalTensorMap requires `numin(d) == numout(d) == 1`")) | ||
| return ROCDiagonalTensorMap{T}(undef, only(V)) | ||
| end | ||
| function ROCDiagonalTensorMap{T}(::UndefInitializer, V::S) where {T, S <: IndexSpace} | ||
| return ROCDiagonalTensorMap{T, S}(undef, V) | ||
| end | ||
| ROCDiagonalTensorMap(::UndefInitializer, V::IndexSpace) = ROCDiagonalTensorMap{Float64}(undef, V) | ||
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| function ROCDiagonalTensorMap(data::ROCVector{T}, V::S) where {T, S} | ||
| return ROCDiagonalTensorMap{T, S}(data, V) | ||
| end | ||
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| function ROCDiagonalTensorMap(data::Vector{T}, V::S) where {T, S} | ||
| return ROCDiagonalTensorMap{T, S}(ROCVector{T}(data), V) | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(svd_full!), t::ROCDiagonalTensorMap, alg::DiagonalAlgorithm) | ||
| V_cod = fuse(codomain(t)) | ||
| V_dom = fuse(domain(t)) | ||
| U = similar(t, codomain(t) ← V_cod) | ||
| S = ROCDiagonalTensorMap{real(scalartype(t))}(undef, V_cod ← V_dom) | ||
| Vᴴ = similar(t, V_dom ← domain(t)) | ||
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| return U, S, Vᴴ | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(svd_vals!), t::ROCTensorMap, alg::AbstractAlgorithm) | ||
| V_cod = infimum(fuse(codomain(t)), fuse(domain(t))) | ||
| return ROCDiagonalTensorMap{real(scalartype(t))}(undef, V_cod) | ||
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There was a problem hiding this comment. [edit] I hadn't seen you already more or less did this for the base implementation, in which case these functions might not be necessary anymore? |
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| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(svd_compact!), t::ROCTensorMap, ::AbstractAlgorithm) | ||
| V_cod = V_dom = infimum(fuse(codomain(t)), fuse(domain(t))) | ||
| U = similar(t, codomain(t) ← V_cod) | ||
| S = ROCDiagonalTensorMap{real(scalartype(t))}(undef, V_cod) | ||
| Vᴴ = similar(t, V_dom ← domain(t)) | ||
| return U, S, Vᴴ | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(eigh_full!), t::ROCTensorMap, ::AbstractAlgorithm) | ||
| V_D = fuse(domain(t)) | ||
| T = real(scalartype(t)) | ||
| D = ROCDiagonalTensorMap{T}(undef, V_D) | ||
| V = similar(t, codomain(t) ← V_D) | ||
| return D, V | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(eig_full!), t::ROCTensorMap, ::AbstractAlgorithm) | ||
| V_D = fuse(domain(t)) | ||
| Tc = complex(scalartype(t)) | ||
| D = ROCDiagonalTensorMap{Tc}(undef, V_D) | ||
| V = similar(t, Tc, codomain(t) ← V_D) | ||
| return D, V | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(eigh_vals!), t::ROCTensorMap, alg::AbstractAlgorithm) | ||
| V_D = fuse(domain(t)) | ||
| T = real(scalartype(t)) | ||
| return D = ROCDiagonalTensorMap{Tc}(undef, V_D) | ||
| end | ||
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| function TensorKit.Factorizations.MAK.initialize_output(::typeof(eig_vals!), t::ROCTensorMap, alg::AbstractAlgorithm) | ||
| V_D = fuse(domain(t)) | ||
| Tc = complex(scalartype(t)) | ||
| return D = ROCDiagonalTensorMap{Tc}(undef, V_D) | ||
| end | ||
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| # TODO | ||
| # add VectorInterface extensions for proper AMDGPU promotion | ||
| function TensorKit.VectorInterface.promote_add(TA::Type{<:AMDGPU.StridedROCMatrix{Tx}}, TB::Type{<:AMDGPU.StridedROCMatrix{Ty}}, α::Tα = TensorKit.VectorInterface.One(), β::Tβ = TensorKit.VectorInterface.One()) where {Tx, Ty, Tα, Tβ} | ||
| return Base.promote_op(add, Tx, Ty, Tα, Tβ) | ||
| end | ||
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| end | ||
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We don't have these generalizations of the constructor for the normal (i.e. non-gpu)
DiagonalTensorMapright? Is this useful?