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Automatic ITensorFormatter run (#1700)
Co-authored-by: mtfishman <7855256+mtfishman@users.noreply.github.com> Co-authored-by: Matt Fishman <mtfishman@users.noreply.github.com> Co-authored-by: mtfishman <mfishman@flatironinstitute.org>
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Lines changed: 1885 additions & 1468 deletions

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‎NDTensors/Project.toml‎

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name = "NDTensors"
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uuid = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf"
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authors = ["Matthew Fishman <mfishman@flatironinstitute.org>"]
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version = "0.4.18"
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version = "0.4.19"
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[deps]
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Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

‎NDTensors/ext/NDTensorsAMDGPUExt/adapt.jl‎

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using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
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using AMDGPU: AMDGPU, ROCArray, ROCVector
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using Adapt: Adapt, adapt
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using Functors: fmap
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using NDTensors.AMDGPUExtensions: AMDGPUExtensions, ROCArrayAdaptor
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using NDTensors.GPUArraysCoreExtensions: storagemode
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using NDTensors.Vendored.TypeParameterAccessors:
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default_type_parameters, set_type_parameters, type_parameters
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using Adapt: Adapt, adapt
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using AMDGPU: AMDGPU, ROCArray, ROCVector
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using Functors: fmap
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using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
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function AMDGPUExtensions.roc(
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xs; storagemode = default_type_parameters(ROCArray, storagemode)

‎NDTensors/ext/NDTensorsAMDGPUExt/copyto.jl‎

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using NDTensors.Expose: Exposed, expose, parent, unexpose
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using LinearAlgebra: LinearAlgebra, Adjoint
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using AMDGPU: ROCArray
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using LinearAlgebra: LinearAlgebra, Adjoint
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using NDTensors.Expose: Exposed, expose, parent, unexpose
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# Same definition as `MtlArray`.
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function Base.copy(src::Exposed{<:ROCArray, <:Base.ReshapedArray})
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function Base.copy(
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src::Exposed{
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<:ROCArray, <:SubArray{<:Any, <:Any, <:Base.ReshapedArray{<:Any, <:Any, <:Adjoint}},
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},
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}
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)
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return copy(@view copy(expose(parent(src)))[parentindices(unexpose(src))...])
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end

‎NDTensors/ext/NDTensorsAMDGPUExt/linearalgebra.jl‎

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using AMDGPU: ROCMatrix
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using Adapt: adapt
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using LinearAlgebra: svd
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using NDTensors.AMDGPUExtensions: roc
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using NDTensors.Expose: Expose, Exposed, expose, ql, ql_positive
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using NDTensors.GPUArraysCoreExtensions: cpu
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using NDTensors.Vendored.TypeParameterAccessors: unwrap_array_type
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using LinearAlgebra: svd
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using Adapt: adapt
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using AMDGPU: ROCMatrix
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function LinearAlgebra.svd(A::Exposed{<:ROCMatrix}; kwargs...)
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U, S, V = svd(cpu(A))

‎NDTensors/ext/NDTensorsAMDGPUExt/mul.jl‎

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@@ -1,14 +1,14 @@
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using NDTensors.Expose: Exposed, expose, parent, unexpose
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using LinearAlgebra: LinearAlgebra, Adjoint, Transpose, mul!
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using AMDGPU: ROCArray
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using LinearAlgebra: LinearAlgebra, Adjoint, Transpose, mul!
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using NDTensors.Expose: Exposed, expose, parent, unexpose
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# This was calling generic matrix multiplication.
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function LinearAlgebra.mul!(
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CM::Exposed{<:ROCArray, <:LinearAlgebra.Transpose},
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AM::Exposed{<:ROCArray},
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BM::Exposed{<:ROCArray},
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α,
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β,
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β
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)
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mul!(transpose(CM), transpose(BM), transpose(AM), α, β)
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return unexpose(CM)
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AM::Exposed{<:ROCArray},
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BM::Exposed{<:ROCArray},
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α,
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β,
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β
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)
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mul!(CM', BM', AM', α, β)
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return unexpose(CM)
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},
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},
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α,
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β,
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β
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)
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mul!(CM, AM, expose(transpose(copy(expose(parent(BM))))), α, β)
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return unexpose(CM)

‎NDTensors/ext/NDTensorsAMDGPUExt/permutedims.jl‎

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using NDTensors.Expose: Exposed, expose, parent, unexpose
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using AMDGPU: ROCArray
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using NDTensors.Expose: Exposed, expose, parent, unexpose
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function Base.permutedims!(
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Edest::Exposed{<:ROCArray, <:Base.ReshapedArray}, Esrc::Exposed{<:ROCArray}, perm
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Edest::Exposed{<:ROCArray, <:Base.ReshapedArray{<:Any, <:Any, <:Adjoint}},
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Esrc::Exposed{<:ROCArray},
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perm,
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f,
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f
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)
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Aperm = reshape(permutedims(Esrc, perm), size(parent(Edest)))
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parent(Edest) .= f.(parent(Edest), Aperm)

‎NDTensors/ext/NDTensorsAMDGPUExt/set_types.jl‎

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using AMDGPU: ROCArray
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using NDTensors.GPUArraysCoreExtensions: storagemode
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using NDTensors.Vendored.TypeParameterAccessors: TypeParameterAccessors, Position
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using NDTensors.Vendored.TypeParameterAccessors: Position, TypeParameterAccessors
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TypeParameterAccessors.position(::Type{<:ROCArray}, ::typeof(storagemode)) = Position(3)

‎NDTensors/ext/NDTensorsCUDAExt/adapt.jl‎

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using Adapt: Adapt
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using CUDA: CUDA, CuArray, CuVector
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using Functors: fmap
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using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
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using NDTensors.CUDAExtensions: CUDAExtensions, CuArrayAdaptor
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using NDTensors.GPUArraysCoreExtensions: storagemode
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using NDTensors.Vendored.TypeParameterAccessors:
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default_type_parameters, set_type_parameters, type_parameters
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using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
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function CUDAExtensions.cu(xs; storagemode = default_type_parameters(CuArray, storagemode))
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return fmap(x -> adapt(CuArrayAdaptor{storagemode}(), x), xs)
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function NDTensors.adapt_storagetype(
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adaptor::CuArrayAdaptor, ::Type{EmptyStorage{ElT, StoreT}}
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) where {ElT, StoreT}
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cutype = set_type_parameters(CuVector, (eltype, storagemode), (ElT, storagemode(adaptor)))
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cutype =
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set_type_parameters(CuVector, (eltype, storagemode), (ElT, storagemode(adaptor)))
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return emptytype(adapt_storagetype(cutype, StoreT))
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end

‎NDTensors/ext/NDTensorsCUDAExt/copyto.jl‎

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using CUDA: CuArray
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using NDTensors.Expose: Exposed, expose, unexpose
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using LinearAlgebra: Adjoint
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using NDTensors.Expose: Exposed, expose, unexpose
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# Same definition as `MtlArray`.
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function Base.copy(src::Exposed{<:CuArray, <:Base.ReshapedArray})
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function Base.copy(
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src::Exposed{
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<:CuArray, <:SubArray{<:Any, <:Any, <:Base.ReshapedArray{<:Any, <:Any, <:Adjoint}},
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},
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}
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)
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return copy(@view copy(expose(parent(src)))[parentindices(unexpose(src))...])
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end

‎NDTensors/ext/NDTensorsCUDAExt/indexing.jl‎

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using CUDA: CuArray
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using GPUArraysCore: @allowscalar
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using NDTensors: NDTensors
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using NDTensors.Expose: Exposed, expose, unexpose
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using NDTensors: NDTensors
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function Base.getindex(E::Exposed{<:CuArray})
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return @allowscalar unexpose(E)[]

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