More Runic formatting (#1675)

Author: mtfishman

.github/ISSUE_TEMPLATE/generate_issue_templates/generate_issue_templates.jl

@@ -7,44 +7,44 @@ package_names = ["ITensors", "NDTensors"]
 package_ordering = Dict(["ITensors" => 1, "NDTensors" => 2])
 
 function bug_report_file(package_name::String)
-  return "$(package_name)_bug_report.md"
+    return "$(package_name)_bug_report.md"
 end
 function feature_request_file(package_name::String)
-  return "$(package_name)_feature_request.md"
+    return "$(package_name)_feature_request.md"
 end
 
 for package_name in package_names
-  @show package_name
+    @show package_name
 
-  order = lpad(package_ordering[package_name], 2, "0")
+    order = lpad(package_ordering[package_name], 2, "0")
 
-  template_bug_report = bug_report_file(template_package_name)
-  new_bug_report = order * "_" * bug_report_file(package_name)
+    template_bug_report = bug_report_file(template_package_name)
+    new_bug_report = order * "_" * bug_report_file(package_name)
 
-  if isfile(new_bug_report)
-    println("File $new_bug_report already exists, skipping")
-  else
-    println("Copying $template_bug_report to $new_bug_report")
-    cp(template_bug_report, new_bug_report)
+    if isfile(new_bug_report)
+        println("File $new_bug_report already exists, skipping")
+    else
+        println("Copying $template_bug_report to $new_bug_report")
+        cp(template_bug_report, new_bug_report)
 
-    println("Replace $template_package_name with $package_name in $new_bug_report")
-    replace_in_file(new_bug_report, template_package_name => package_name)
+        println("Replace $template_package_name with $package_name in $new_bug_report")
+        replace_in_file(new_bug_report, template_package_name => package_name)
 
-    mv(new_bug_report, joinpath("..", new_bug_report); force=true)
-  end
+        mv(new_bug_report, joinpath("..", new_bug_report); force = true)
+    end
 
-  template_feature_request = feature_request_file(template_package_name)
-  new_feature_request = order * "_" * feature_request_file(package_name)
+    template_feature_request = feature_request_file(template_package_name)
+    new_feature_request = order * "_" * feature_request_file(package_name)
 
-  if isfile(new_feature_request)
-    println("File $new_feature_request already exists, skipping")
-  else
-    println("Copying $template_feature_request to $new_feature_request")
-    cp(template_feature_request, new_feature_request)
+    if isfile(new_feature_request)
+        println("File $new_feature_request already exists, skipping")
+    else
+        println("Copying $template_feature_request to $new_feature_request")
+        cp(template_feature_request, new_feature_request)
 
-    println("Replace $template_package_name with $package_name in $new_feature_request")
-    replace_in_file(new_feature_request, template_package_name => package_name)
+        println("Replace $template_package_name with $package_name in $new_feature_request")
+        replace_in_file(new_feature_request, template_package_name => package_name)
 
-    mv(new_feature_request, joinpath("..", new_feature_request); force=true)
-  end
+        mv(new_feature_request, joinpath("..", new_feature_request); force = true)
+    end
 end

NDTensors/Project.toml

@@ -1,7 +1,7 @@
 name = "NDTensors"
 uuid = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf"
 authors = ["Matthew Fishman <[email protected]>"]
-version = "0.4.12"
+version = "0.4.13"
 
 [deps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

NDTensors/ext/NDTensorsAMDGPUExt/adapt.jl

@@ -2,28 +2,28 @@ using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
 using NDTensors.AMDGPUExtensions: AMDGPUExtensions, ROCArrayAdaptor
 using NDTensors.GPUArraysCoreExtensions: storagemode
 using NDTensors.TypeParameterAccessors:
-  default_type_parameters, set_type_parameters, type_parameters
+    default_type_parameters, set_type_parameters, type_parameters
 using Adapt: Adapt, adapt
 using AMDGPU: AMDGPU, ROCArray, ROCVector
 using Functors: fmap
 
 function AMDGPUExtensions.roc(
-  xs; storagemode=default_type_parameters(ROCArray, storagemode)
-)
-  return fmap(x -> adapt(ROCArrayAdaptor{storagemode}(), x), xs)
+        xs; storagemode = default_type_parameters(ROCArray, storagemode)
+    )
+    return fmap(x -> adapt(ROCArrayAdaptor{storagemode}(), x), xs)
 end
 
 function Adapt.adapt_storage(adaptor::ROCArrayAdaptor, xs::AbstractArray)
-  new_parameters = (type_parameters(xs, (eltype, ndims))..., storagemode(adaptor))
-  roctype = set_type_parameters(ROCArray, (eltype, ndims, storagemode), new_parameters)
-  return isbits(xs) ? xs : adapt(roctype, xs)
+    new_parameters = (type_parameters(xs, (eltype, ndims))..., storagemode(adaptor))
+    roctype = set_type_parameters(ROCArray, (eltype, ndims, storagemode), new_parameters)
+    return isbits(xs) ? xs : adapt(roctype, xs)
 end
 
 function NDTensors.adapt_storagetype(
-  adaptor::ROCArrayAdaptor, ::Type{EmptyStorage{ElT,StoreT}}
-) where {ElT,StoreT}
-  roctype = set_type_parameters(
-    ROCVector, (eltype, storagemode), (ElT, storagemode(adaptor))
-  )
-  return emptytype(adapt_storagetype(roctype, StoreT))
+        adaptor::ROCArrayAdaptor, ::Type{EmptyStorage{ElT, StoreT}}
+    ) where {ElT, StoreT}
+    roctype = set_type_parameters(
+        ROCVector, (eltype, storagemode), (ElT, storagemode(adaptor))
+    )
+    return emptytype(adapt_storagetype(roctype, StoreT))
 end

NDTensors/ext/NDTensorsAMDGPUExt/append.jl

@@ -4,5 +4,5 @@ using NDTensors.Expose: Exposed, unexpose
 
 ## Warning this append function uses scalar indexing and is therefore extremely slow
 function Base.append!(Ecollection::Exposed{<:ROCArray}, collections...)
-  return @allowscalar append!(unexpose(Ecollection), collections...)
+    return @allowscalar append!(unexpose(Ecollection), collections...)
 end

NDTensors/ext/NDTensorsAMDGPUExt/copyto.jl

@@ -3,33 +3,33 @@ using LinearAlgebra: LinearAlgebra, Adjoint
 using AMDGPU: ROCArray
 
 # Same definition as `MtlArray`.
-function Base.copy(src::Exposed{<:ROCArray,<:Base.ReshapedArray})
-  return reshape(copy(parent(src)), size(unexpose(src)))
+function Base.copy(src::Exposed{<:ROCArray, <:Base.ReshapedArray})
+    return reshape(copy(parent(src)), size(unexpose(src)))
 end
 
 function Base.copy(
-  src::Exposed{
-    <:ROCArray,<:SubArray{<:Any,<:Any,<:Base.ReshapedArray{<:Any,<:Any,<:Adjoint}}
-  },
-)
-  return copy(@view copy(expose(parent(src)))[parentindices(unexpose(src))...])
+        src::Exposed{
+            <:ROCArray, <:SubArray{<:Any, <:Any, <:Base.ReshapedArray{<:Any, <:Any, <:Adjoint}},
+        },
+    )
+    return copy(@view copy(expose(parent(src)))[parentindices(unexpose(src))...])
 end
 
-function Base.copyto!(dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray,<:SubArray})
-  copyto!(dest, expose(copy(src)))
-  return unexpose(dest)
+function Base.copyto!(dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray, <:SubArray})
+    copyto!(dest, expose(copy(src)))
+    return unexpose(dest)
 end
 
 function Base.copyto!(
-  dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray,<:Base.ReshapedArray}
-)
-  copyto!(dest, expose(parent(src)))
-  return unexpose(dest)
+        dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray, <:Base.ReshapedArray}
+    )
+    copyto!(dest, expose(parent(src)))
+    return unexpose(dest)
 end
 
 function Base.copyto!(
-  dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray,<:LinearAlgebra.Transpose}
-)
-  copyto!(expose(transpose(dest)), expose(parent(src)))
-  return unexpose(dest)
+        dest::Exposed{<:ROCArray}, src::Exposed{<:ROCArray, <:LinearAlgebra.Transpose}
+    )
+    copyto!(expose(transpose(dest)), expose(parent(src)))
+    return unexpose(dest)
 end

NDTensors/ext/NDTensorsAMDGPUExt/indexing.jl

@@ -4,20 +4,20 @@ using NDTensors.Expose: Exposed, expose, parent, unexpose
 using NDTensors.GPUArraysCoreExtensions: cpu
 
 function Base.getindex(E::Exposed{<:ROCArray})
-  return @allowscalar unexpose(E)[]
+    return @allowscalar unexpose(E)[]
 end
 
 function Base.setindex!(E::Exposed{<:ROCArray}, x::Number)
-  @allowscalar unexpose(E)[] = x
-  return unexpose(E)
+    @allowscalar unexpose(E)[] = x
+    return unexpose(E)
 end
 
-function Base.getindex(E::Exposed{<:ROCArray,<:Adjoint}, i, j)
-  return (expose(parent(E))[j, i])'
+function Base.getindex(E::Exposed{<:ROCArray, <:Adjoint}, i, j)
+    return (expose(parent(E))[j, i])'
 end
 
-Base.any(f, E::Exposed{<:ROCArray,<:NDTensors.Tensor}) = any(f, data(unexpose(E)))
+Base.any(f, E::Exposed{<:ROCArray, <:NDTensors.Tensor}) = any(f, data(unexpose(E)))
 
 function Base.print_array(io::IO, E::Exposed{<:ROCArray})
-  return Base.print_array(io, expose(cpu(E)))
+    return Base.print_array(io, expose(cpu(E)))
 end

NDTensors/ext/NDTensorsAMDGPUExt/linearalgebra.jl

@@ -7,16 +7,16 @@ using Adapt: adapt
 using AMDGPU: ROCMatrix
 
 function LinearAlgebra.svd(A::Exposed{<:ROCMatrix}; kwargs...)
-  U, S, V = svd(cpu(A))
-  return roc.((U, S, V))
+    U, S, V = svd(cpu(A))
+    return roc.((U, S, V))
 end
 
 ## TODO currently AMDGPU doesn't have ql so make a ql function
 function Expose.ql(A::Exposed{<:ROCMatrix})
-  Q, L = ql(expose(cpu(A)))
-  return adapt(unwrap_array_type(A), Matrix(Q)), adapt(unwrap_array_type(A), L)
+    Q, L = ql(expose(cpu(A)))
+    return adapt(unwrap_array_type(A), Matrix(Q)), adapt(unwrap_array_type(A), L)
 end
 function Expose.ql_positive(A::Exposed{<:ROCMatrix})
-  Q, L = ql_positive(expose(cpu(A)))
-  return adapt(unwrap_array_type(A), Matrix(Q)), adapt(unwrap_array_type(A), L)
+    Q, L = ql_positive(expose(cpu(A)))
+    return adapt(unwrap_array_type(A), Matrix(Q)), adapt(unwrap_array_type(A), L)
 end

NDTensors/ext/NDTensorsAMDGPUExt/mul.jl

@@ -4,42 +4,42 @@ using AMDGPU: ROCArray
 
 # This was calling generic matrix multiplication.
 function LinearAlgebra.mul!(
-  CM::Exposed{<:ROCArray,<:LinearAlgebra.Transpose},
-  AM::Exposed{<:ROCArray},
-  BM::Exposed{<:ROCArray},
-  α,
-  β,
-)
-  mul!(transpose(CM), transpose(BM), transpose(AM), α, β)
-  return unexpose(CM)
+        CM::Exposed{<:ROCArray, <:LinearAlgebra.Transpose},
+        AM::Exposed{<:ROCArray},
+        BM::Exposed{<:ROCArray},
+        α,
+        β,
+    )
+    mul!(transpose(CM), transpose(BM), transpose(AM), α, β)
+    return unexpose(CM)
 end
 
 # This was calling generic matrix multiplication.
 function LinearAlgebra.mul!(
-  CM::Exposed{<:ROCArray,<:LinearAlgebra.Adjoint},
-  AM::Exposed{<:ROCArray},
-  BM::Exposed{<:ROCArray},
-  α,
-  β,
-)
-  mul!(CM', BM', AM', α, β)
-  return unexpose(CM)
+        CM::Exposed{<:ROCArray, <:LinearAlgebra.Adjoint},
+        AM::Exposed{<:ROCArray},
+        BM::Exposed{<:ROCArray},
+        α,
+        β,
+    )
+    mul!(CM', BM', AM', α, β)
+    return unexpose(CM)
 end
 
 # Fix issue in AMDGPU.jl where it cannot distinguish
 # Transpose{Reshape{Adjoint{ROCArray}}} as a ROCArray and calls generic matmul
 function LinearAlgebra.mul!(
-  CM::Exposed{<:ROCArray},
-  AM::Exposed{<:ROCArray},
-  BM::Exposed{
-    <:ROCArray,
-    <:LinearAlgebra.Transpose{
-      <:Any,<:Base.ReshapedArray{<:Any,<:Any,<:LinearAlgebra.Adjoint}
-    },
-  },
-  α,
-  β,
-)
-  mul!(CM, AM, expose(transpose(copy(expose(parent(BM))))), α, β)
-  return unexpose(CM)
+        CM::Exposed{<:ROCArray},
+        AM::Exposed{<:ROCArray},
+        BM::Exposed{
+            <:ROCArray,
+            <:LinearAlgebra.Transpose{
+                <:Any, <:Base.ReshapedArray{<:Any, <:Any, <:LinearAlgebra.Adjoint},
+            },
+        },
+        α,
+        β,
+    )
+    mul!(CM, AM, expose(transpose(copy(expose(parent(BM))))), α, β)
+    return unexpose(CM)
 end

NDTensors/ext/NDTensorsAMDGPUExt/permutedims.jl

@@ -2,22 +2,22 @@ using NDTensors.Expose: Exposed, expose, parent, unexpose
 using AMDGPU: ROCArray
 
 function Base.permutedims!(
-  Edest::Exposed{<:ROCArray,<:Base.ReshapedArray}, Esrc::Exposed{<:ROCArray}, perm
-)
-  Aperm = permutedims(Esrc, perm)
-  copyto!(expose(parent(Edest)), expose(Aperm))
-  return unexpose(Edest)
+        Edest::Exposed{<:ROCArray, <:Base.ReshapedArray}, Esrc::Exposed{<:ROCArray}, perm
+    )
+    Aperm = permutedims(Esrc, perm)
+    copyto!(expose(parent(Edest)), expose(Aperm))
+    return unexpose(Edest)
 end
 
 # There is an issue in AMDGPU where if Edest is a reshaped{<:Adjoint}
 # .= can fail. So instead force Esrc into the shape of parent(Edest)
 function Base.permutedims!(
-  Edest::Exposed{<:ROCArray,<:Base.ReshapedArray{<:Any,<:Any,<:Adjoint}},
-  Esrc::Exposed{<:ROCArray},
-  perm,
-  f,
-)
-  Aperm = reshape(permutedims(Esrc, perm), size(parent(Edest)))
-  parent(Edest) .= f.(parent(Edest), Aperm)
-  return unexpose(Edest)
+        Edest::Exposed{<:ROCArray, <:Base.ReshapedArray{<:Any, <:Any, <:Adjoint}},
+        Esrc::Exposed{<:ROCArray},
+        perm,
+        f,
+    )
+    Aperm = reshape(permutedims(Esrc, perm), size(parent(Edest)))
+    parent(Edest) .= f.(parent(Edest), Aperm)
+    return unexpose(Edest)
 end

NDTensors/ext/NDTensorsCUDAExt/adapt.jl

@@ -5,22 +5,22 @@ using NDTensors: NDTensors, EmptyStorage, adapt_storagetype, emptytype
 using NDTensors.CUDAExtensions: CUDAExtensions, CuArrayAdaptor
 using NDTensors.GPUArraysCoreExtensions: storagemode
 using NDTensors.TypeParameterAccessors:
-  default_type_parameters, set_type_parameters, type_parameters
+    default_type_parameters, set_type_parameters, type_parameters
 
-function CUDAExtensions.cu(xs; storagemode=default_type_parameters(CuArray, storagemode))
-  return fmap(x -> adapt(CuArrayAdaptor{storagemode}(), x), xs)
+function CUDAExtensions.cu(xs; storagemode = default_type_parameters(CuArray, storagemode))
+    return fmap(x -> adapt(CuArrayAdaptor{storagemode}(), x), xs)
 end
 
 ## Could do this generically
 function Adapt.adapt_storage(adaptor::CuArrayAdaptor, xs::AbstractArray)
-  params = (type_parameters(xs, (eltype, ndims))..., storagemode(adaptor))
-  cutype = set_type_parameters(CuArray, (eltype, ndims, storagemode), params)
-  return isbits(xs) ? xs : adapt(cutype, xs)
+    params = (type_parameters(xs, (eltype, ndims))..., storagemode(adaptor))
+    cutype = set_type_parameters(CuArray, (eltype, ndims, storagemode), params)
+    return isbits(xs) ? xs : adapt(cutype, xs)
 end
 
 function NDTensors.adapt_storagetype(
-  adaptor::CuArrayAdaptor, ::Type{EmptyStorage{ElT,StoreT}}
-) where {ElT,StoreT}
-  cutype = set_type_parameters(CuVector, (eltype, storagemode), (ElT, storagemode(adaptor)))
-  return emptytype(adapt_storagetype(cutype, StoreT))
+        adaptor::CuArrayAdaptor, ::Type{EmptyStorage{ElT, StoreT}}
+    ) where {ElT, StoreT}
+    cutype = set_type_parameters(CuVector, (eltype, storagemode), (ElT, storagemode(adaptor)))
+    return emptytype(adapt_storagetype(cutype, StoreT))
 end