Formatter

Author: lkdvos

ext/TensorOperationscuTENSORExt.jl

@@ -38,7 +38,6 @@ const CuStridedView = StridedViewsCUDAExt.CuStridedView
 const SUPPORTED_CUARRAYS = Union{AnyCuArray,CuStridedView}
 const cuTENSORBackend = TO.Backend{:cuTENSOR}
 
-
 function TO.tensorscalar(C::SUPPORTED_CUARRAYS)
     return ndims(C) == 0 ? tensorscalar(collect(C)) : throw(DimensionMismatch())
 end
@@ -60,7 +59,8 @@ end
 
 # making sure that if no backend is specified, the cuTENSOR backend is used:
 
-function TO.tensoradd!(C::SUPPORTED_CUARRAYS, pC::Index2Tuple, A::SUPPORTED_CUARRAYS, conjA::Symbol,
+function TO.tensoradd!(C::SUPPORTED_CUARRAYS, pC::Index2Tuple, A::SUPPORTED_CUARRAYS,
+                       conjA::Symbol,
                        α::Number, β::Number)
     return tensoradd!(C, pC, A, conjA, α, β, cuTENSORBackend())
 end
@@ -171,7 +171,7 @@ function TO.tensorcontract!(C::CuArray, pC::Index2Tuple,
     Ainds, Binds, Cinds = collect.(TO.contract_labels(pA, pB, pC))
     opA = tensorop(A, conjA)
     opB = tensorop(B, conjB)
-    
+
     # dispatch to cuTENSOR
     return cuTENSOR.contract!(α,
                               A, Ainds, opA,
@@ -206,38 +206,38 @@ function plan_trace(@nospecialize(A::AbstractArray), Ainds::cuTENSOR.ModeType,
     !cuTENSOR.is_unary(opA) && throw(ArgumentError("opA must be a unary op!"))
     !cuTENSOR.is_unary(opC) && throw(ArgumentError("opC must be a unary op!"))
     !cuTENSOR.is_binary(opReduce) && throw(ArgumentError("opReduce must be a binary op!"))
-    
+
     # TODO: check if this can be avoided, available in caller
     # TODO: cuTENSOR will allocate sizes and strides anyways, could use that here
     _, cindA1, cindA2 = TO.trace_indices(tuple(Ainds...), tuple(Cinds...))
-    
+
     # add strides of cindA2 to strides of cindA1 -> selects diagonal
     stA = strides(A)
     for (i, j) in zip(cindA1, cindA2)
         stA = Base.setindex(stA, stA[i] + stA[j], i)
     end
     szA = TT.deleteat(size(A), cindA2)
     stA′ = TT.deleteat(stA, cindA2)
-    
+
     descA = cuTENSOR.CuTensorDescriptor(A; size=szA, strides=stA′)
     descC = cuTENSOR.CuTensorDescriptor(C)
-    
+
     modeA = collect(Cint, deleteat!(Ainds, cindA2))
     modeC = collect(Cint, Cinds)
-    
+
     actual_compute_type = if compute_type === nothing
         cuTENSOR.reduction_compute_types[(eltype(A), eltype(C))]
     else
         compute_type
     end
-    
+
     desc = Ref{cuTENSOR.cutensorOperationDescriptor_t}()
     cuTENSOR.cutensorCreateReduction(cuTENSOR.handle(),
-                            desc,
-                            descA, modeA, opA,
-                            descC, modeC, opC,
-                            descC, modeC, opReduce,
-                            actual_compute_type)
+                                     desc,
+                                     descA, modeA, opA,
+                                     descC, modeC, opC,
+                                     descC, modeC, opReduce,
+                                     actual_compute_type)
 
     plan_pref = Ref{cuTENSOR.cutensorPlanPreference_t}()
     cuTENSOR.cutensorCreatePlanPreference(cuTENSOR.handle(), plan_pref, algo, jit)

test/cutensor.jl

@@ -303,4 +303,4 @@ if cuTENSOR.has_cutensor()
             @test copy(C) ≈ Ccopy
         end
     end
-end
+end