Apply JuliaFormatter to fix formatting CI

Fixes formatting issues throughout the codebase to comply with SciML style guide:
- Documentation formatting in docs/
- Extension modules formatting
- Core source code formatting
- Test files formatting

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: ChrisRackauckas

docs/src/tutorials/gpu.md

@@ -2,20 +2,20 @@
 
 LinearSolve.jl provides two ways to GPU accelerate linear solves:
 
-* Offloading: offloading takes a CPU-based problem and automatically transforms it into a
-  GPU-based problem in the background, and returns the solution on CPU. Thus using
-  offloading requires no change on the part of the user other than to choose an offloading
-  solver.
-* Array type interface: the array type interface requires that the user defines the
-  `LinearProblem` using an `AbstractGPUArray` type and chooses an appropriate solver
-  (or uses the default solver). The solution will then be returned as a GPU array type.
+  - Offloading: offloading takes a CPU-based problem and automatically transforms it into a
+    GPU-based problem in the background, and returns the solution on CPU. Thus using
+    offloading requires no change on the part of the user other than to choose an offloading
+    solver.
+  - Array type interface: the array type interface requires that the user defines the
+    `LinearProblem` using an `AbstractGPUArray` type and chooses an appropriate solver
+    (or uses the default solver). The solution will then be returned as a GPU array type.
 
 The offloading approach has the advantage of being simpler and requiring no change to
 existing CPU code, while having the disadvantage of having more overhead. In the following
 sections we will demonstrate how to use each of the approaches.
 
 !!! warn
-
+    
     GPUs are not always faster! Your matrices need to be sufficiently large in order for
     GPU accelerations to actually be faster. For offloading it's around 1,000 x 1,000 matrices
     and for Array type interface it's around 100 x 100. For sparse matrices, it is highly
@@ -74,7 +74,7 @@ to return it to the CPU. This setup does no automated memory transfers and will
 move things to CPU on command.
 
 !!! warn
-
+    
     Many GPU functionalities, such as `CUDA.cu`, have a built-in preference for `Float32`.
     Generally it is much faster to use 32-bit floating point operations on GPU than 64-bit
     operations, and thus this is generally the right choice if going to such platforms.
@@ -118,7 +118,7 @@ sol = LS.solve(prob, LS.LUFactorization())
 ```
 
 !!! note
-
+    
     For now, CUDSS only supports CuSparseMatrixCSR type matrices.
 
 Note that `KrylovJL` methods also work with sparse GPU arrays:
@@ -130,7 +130,8 @@ sol = LS.solve(prob, LS.KrylovJL_GMRES())
 Note that CUSPARSE also has some GPU-based preconditioners, such as a built-in `ilu`. However:
 
 ```julia
-sol = LS.solve(prob, LS.KrylovJL_GMRES(precs = (A, p) -> (CUDA.CUSPARSE.ilu02!(A, 'O'), LA.I)))
+sol = LS.solve(
+    prob, LS.KrylovJL_GMRES(precs = (A, p) -> (CUDA.CUSPARSE.ilu02!(A, 'O'), LA.I)))
 ```
 
 However, right now CUSPARSE is missing the right `ldiv!` implementation for this to work

docs/src/tutorials/linear.md

@@ -103,11 +103,11 @@ The Matrix-Free operators are provided by the [SciMLOperators.jl interface](http
 For example, for the matrix `A` defined via:
 
 ```@example linsys1
-A = [-2.0  1  0  0  0
-      1 -2  1  0  0
-      0  1 -2  1  0
-      0  0  1 -2  1
-      0  0  0  1 -2]
+A = [-2.0 1 0 0 0
+     1 -2 1 0 0
+     0 1 -2 1 0
+     0 0 1 -2 1
+     0 0 0 1 -2]
 ```
 
 We can define the `FunctionOperator` that does the `A*v` operations, without using the matrix `A`. This is done by defining
@@ -116,18 +116,18 @@ operator. See the [SciMLOperators.jl documentation](https://docs.sciml.ai/SciMLO
 non-constant operators, operator algebras, and many more features). This is done by:
 
 ```@example linsys1
-function Afunc!(w,v,u,p,t)
+function Afunc!(w, v, u, p, t)
     w[1] = -2v[1] + v[2]
     for i in 2:4
-        w[i] = v[i-1] - 2v[i] + v[i+1]
+        w[i] = v[i - 1] - 2v[i] + v[i + 1]
     end
     w[5] = v[4] - 2v[5]
     nothing
 end
 
-function Afunc!(v,u,p,t)
+function Afunc!(v, u, p, t)
     w = zeros(5)
-    Afunc!(w,v,u,p,t)
+    Afunc!(w, v, u, p, t)
     w
 end
 
@@ -159,6 +159,8 @@ mfopA * sol.u - b
 ```
 
 !!! note
-  Note that not all methods can use a matrix-free operator. For example, `LS.LUFactorization()` requires a matrix. If you use an
-  invalid method, you will get an error. The methods particularly from KrylovJL are the ones preferred for these cases
-  (and are defaulted to).
+    
+
+Note that not all methods can use a matrix-free operator. For example, `LS.LUFactorization()` requires a matrix. If you use an
+invalid method, you will get an error. The methods particularly from KrylovJL are the ones preferred for these cases
+(and are defaulted to).

ext/LinearSolveCUDAExt.jl

@@ -1,14 +1,17 @@
 module LinearSolveCUDAExt
 
 using CUDA
-using LinearSolve: LinearSolve, is_cusparse, defaultalg, cudss_loaded, DefaultLinearSolver, 
-                   DefaultAlgorithmChoice, ALREADY_WARNED_CUDSS, LinearCache, needs_concrete_A,
-                   error_no_cudss_lu, init_cacheval, OperatorAssumptions, CudaOffloadFactorization,
+using LinearSolve: LinearSolve, is_cusparse, defaultalg, cudss_loaded, DefaultLinearSolver,
+                   DefaultAlgorithmChoice, ALREADY_WARNED_CUDSS, LinearCache,
+                   needs_concrete_A,
+                   error_no_cudss_lu, init_cacheval, OperatorAssumptions,
+                   CudaOffloadFactorization,
                    SparspakFactorization, KLUFactorization, UMFPACKFactorization
 using LinearSolve.LinearAlgebra, LinearSolve.SciMLBase, LinearSolve.ArrayInterface
 using SciMLBase: AbstractSciMLOperator
 
-function LinearSolve.is_cusparse(A::Union{CUDA.CUSPARSE.CuSparseMatrixCSR, CUDA.CUSPARSE.CuSparseMatrixCSC})
+function LinearSolve.is_cusparse(A::Union{
+        CUDA.CUSPARSE.CuSparseMatrixCSR, CUDA.CUSPARSE.CuSparseMatrixCSC})
     true
 end
 

ext/LinearSolveEnzymeExt.jl

@@ -1,7 +1,7 @@
 module LinearSolveEnzymeExt
 
-using LinearSolve: LinearSolve, SciMLLinearSolveAlgorithm, init, solve!, LinearProblem, 
-                   LinearCache, AbstractKrylovSubspaceMethod, DefaultLinearSolver, 
+using LinearSolve: LinearSolve, SciMLLinearSolveAlgorithm, init, solve!, LinearProblem,
+                   LinearCache, AbstractKrylovSubspaceMethod, DefaultLinearSolver,
                    defaultalg_adjoint_eval, solve
 using LinearSolve.LinearAlgebra
 using EnzymeCore
@@ -205,9 +205,9 @@ function EnzymeRules.augmented_primal(
 
     cache = (copy(res.u), resvals, cachesolve, dAs, dbs)
 
-    _res = EnzymeRules.needs_primal(config) ? res : nothing  
+    _res = EnzymeRules.needs_primal(config) ? res : nothing
     _dres = EnzymeRules.needs_shadow(config) ? dres : nothing
-    
+
     return EnzymeRules.AugmentedReturn(_res, _dres, cache)
 end
 

ext/LinearSolveForwardDiffExt.jl

@@ -150,26 +150,29 @@ function SciMLBase.init(
         dual_type = get_dual_type(prob.b)
     end
 
-    alg isa LinearSolve.DefaultLinearSolver ? real_alg = LinearSolve.defaultalg(primal_prob.A, primal_prob.b) : real_alg = alg
+    alg isa LinearSolve.DefaultLinearSolver ?
+    real_alg = LinearSolve.defaultalg(primal_prob.A, primal_prob.b) : real_alg = alg
 
     non_partial_cache = init(
         primal_prob, real_alg, assumptions, args...;
         alias = alias, abstol = abstol, reltol = reltol,
         maxiters = maxiters, verbose = verbose, Pl = Pl, Pr = Pr, assumptions = assumptions,
         sensealg = sensealg, u0 = new_u0, kwargs...)
-    return DualLinearCache(non_partial_cache, dual_type, ∂_A, ∂_b, !isnothing(∂_b) ? zero.(∂_b) : ∂_b, A, b, zeros(dual_type, length(b)))
+    return DualLinearCache(non_partial_cache, dual_type, ∂_A, ∂_b,
+        !isnothing(∂_b) ? zero.(∂_b) : ∂_b, A, b, zeros(dual_type, length(b)))
 end
 
 function SciMLBase.solve!(cache::DualLinearCache, args...; kwargs...)
-   solve!(cache, cache.alg, args...; kwargs...)
+    solve!(cache, cache.alg, args...; kwargs...)
 end
 
-function SciMLBase.solve!(cache::DualLinearCache, alg::SciMLLinearSolveAlgorithm, args...; kwargs...)
+function SciMLBase.solve!(
+        cache::DualLinearCache, alg::SciMLLinearSolveAlgorithm, args...; kwargs...)
     sol,
     partials = linearsolve_forwarddiff_solve(
         cache::DualLinearCache, cache.alg, args...; kwargs...)
     dual_sol = linearsolve_dual_solution(sol.u, partials, cache.dual_type)
-    
+
     if cache.dual_u isa AbstractArray
         cache.dual_u[:] = dual_sol
     else
@@ -192,7 +195,6 @@ function Base.setproperty!(dc::DualLinearCache, sym::Symbol, val)
         setproperty!(dc.linear_cache, sym, val)
     end
 
-
     # Update the partials if setting A or b
     if sym === :A
         setfield!(dc, :dual_A, val)
@@ -221,8 +223,6 @@ function Base.getproperty(dc::DualLinearCache, sym::Symbol)
     end
 end
 
-
-
 # Enhanced helper functions for Dual numbers to handle recursion
 get_dual_type(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = typeof(x)
 get_dual_type(x::Dual{T, V, P}) where {T, V <: Dual, P} = typeof(x)
@@ -241,7 +241,6 @@ nodual_value(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = ForwardDiff.va
 nodual_value(x::Dual{T, V, P}) where {T, V <: Dual, P} = x.value  # Keep the inner dual intact
 nodual_value(x::AbstractArray{<:Dual}) = map(nodual_value, x)
 
-
 function partials_to_list(partial_matrix::AbstractVector{T}) where {T}
     p = eachindex(first(partial_matrix))
     [[partial[i] for partial in partial_matrix] for i in p]
@@ -263,6 +262,4 @@ function partials_to_list(partial_matrix)
     return res_list
 end
 
-
 end
-

ext/LinearSolveRecursiveFactorizationExt.jl

@@ -1,6 +1,7 @@
 module LinearSolveRecursiveFactorizationExt
 
-using LinearSolve: LinearSolve, userecursivefactorization, LinearCache, @get_cacheval, RFLUFactorization
+using LinearSolve: LinearSolve, userecursivefactorization, LinearCache, @get_cacheval,
+                   RFLUFactorization
 using LinearSolve.LinearAlgebra, LinearSolve.ArrayInterface, RecursiveFactorization
 using SciMLBase: SciMLBase, ReturnCode
 

ext/LinearSolveSparseArraysExt.jl

@@ -1,12 +1,15 @@
 module LinearSolveSparseArraysExt
 
 using LinearSolve: LinearSolve, BLASELTYPES, pattern_changed, ArrayInterface,
-                    @get_cacheval, CHOLMODFactorization, GenericFactorization, GenericLUFactorization,
-                    KLUFactorization, LUFactorization, NormalCholeskyFactorization, OperatorAssumptions,
-                    QRFactorization, RFLUFactorization, UMFPACKFactorization, solve
+                   @get_cacheval, CHOLMODFactorization, GenericFactorization,
+                   GenericLUFactorization,
+                   KLUFactorization, LUFactorization, NormalCholeskyFactorization,
+                   OperatorAssumptions,
+                   QRFactorization, RFLUFactorization, UMFPACKFactorization, solve
 using ArrayInterface: ArrayInterface
 using LinearAlgebra: LinearAlgebra, I, Hermitian, Symmetric, cholesky, ldiv!, lu, lu!, QR
-using SparseArrays: SparseArrays, AbstractSparseArray, AbstractSparseMatrixCSC, SparseMatrixCSC, 
+using SparseArrays: SparseArrays, AbstractSparseArray, AbstractSparseMatrixCSC,
+                    SparseMatrixCSC,
                     nonzeros, rowvals, getcolptr, sparse, sprand
 using SparseArrays.UMFPACK: UMFPACK_OK
 using Base: /, \, convert
@@ -107,23 +110,25 @@ function LinearSolve.init_cacheval(
         alg::LUFactorization, A::AbstractSparseArray{T, Int64}, b, u,
         Pl, Pr,
         maxiters::Int, abstol, reltol,
-        verbose::Bool, assumptions::OperatorAssumptions) where {T<:BLASELTYPES}
+        verbose::Bool, assumptions::OperatorAssumptions) where {T <: BLASELTYPES}
     if LinearSolve.is_cusparse(A)
         ArrayInterface.lu_instance(A)
     else
-        SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int64}(zero(Int64), zero(Int64), [Int64(1)], Int64[], T[]))
+        SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int64}(
+            zero(Int64), zero(Int64), [Int64(1)], Int64[], T[]))
     end
 end
 
 function LinearSolve.init_cacheval(
         alg::LUFactorization, A::AbstractSparseArray{T, Int32}, b, u,
         Pl, Pr,
         maxiters::Int, abstol, reltol,
-        verbose::Bool, assumptions::OperatorAssumptions) where {T<:BLASELTYPES}
+        verbose::Bool, assumptions::OperatorAssumptions) where {T <: BLASELTYPES}
     if LinearSolve.is_cusparse(A)
         ArrayInterface.lu_instance(A)
     else
-        SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int32}(zero(Int32), zero(Int32), [Int32(1)], Int32[], T[]))
+        SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int32}(
+            zero(Int32), zero(Int32), [Int32(1)], Int32[], T[]))
     end
 end
 
@@ -140,7 +145,6 @@ function LinearSolve.init_cacheval(
         maxiters::Int, abstol,
         reltol,
         verbose::Bool, assumptions::OperatorAssumptions)
-    
     PREALLOCATED_UMFPACK
 end
 
@@ -156,16 +160,18 @@ function LinearSolve.init_cacheval(
         alg::UMFPACKFactorization, A::AbstractSparseArray{T, Int64}, b, u,
         Pl, Pr,
         maxiters::Int, abstol, reltol,
-        verbose::Bool, assumptions::OperatorAssumptions) where {T<:BLASELTYPES}
-    SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int64}(zero(Int64), zero(Int64), [Int64(1)], Int64[], T[]))
+        verbose::Bool, assumptions::OperatorAssumptions) where {T <: BLASELTYPES}
+    SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int64}(
+        zero(Int64), zero(Int64), [Int64(1)], Int64[], T[]))
 end
 
 function LinearSolve.init_cacheval(
         alg::UMFPACKFactorization, A::AbstractSparseArray{T, Int32}, b, u,
         Pl, Pr,
         maxiters::Int, abstol, reltol,
-        verbose::Bool, assumptions::OperatorAssumptions) where {T<:BLASELTYPES}
-    SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int32}(zero(Int32), zero(Int32), [Int32(1)], Int32[], T[]))
+        verbose::Bool, assumptions::OperatorAssumptions) where {T <: BLASELTYPES}
+    SparseArrays.UMFPACK.UmfpackLU(SparseMatrixCSC{T, Int32}(
+        zero(Int32), zero(Int32), [Int32(1)], Int32[], T[]))
 end
 
 function SciMLBase.solve!(
@@ -197,7 +203,8 @@ function SciMLBase.solve!(
     F = LinearSolve.@get_cacheval(cache, :UMFPACKFactorization)
     if F.status == UMFPACK_OK
         y = ldiv!(cache.u, F, cache.b)
-        SciMLBase.build_linear_solution(alg, y, nothing, cache; retcode = ReturnCode.Success)
+        SciMLBase.build_linear_solution(
+            alg, y, nothing, cache; retcode = ReturnCode.Success)
     else
         SciMLBase.build_linear_solution(
             alg, cache.u, nothing, cache; retcode = ReturnCode.Infeasible)
@@ -236,7 +243,8 @@ function LinearSolve.init_cacheval(
         maxiters::Int, abstol,
         reltol,
         verbose::Bool, assumptions::OperatorAssumptions)
-    KLU.KLUFactorization(SparseMatrixCSC{Float64, Int32}(0, 0, [Int32(1)], Int32[], Float64[]))
+    KLU.KLUFactorization(SparseMatrixCSC{Float64, Int32}(
+        0, 0, [Int32(1)], Int32[], Float64[]))
 end
 
 # TODO: guard this against errors
@@ -266,14 +274,15 @@ function SciMLBase.solve!(cache::LinearSolve.LinearCache, alg::KLUFactorization;
     F = LinearSolve.@get_cacheval(cache, :KLUFactorization)
     if F.common.status == KLU.KLU_OK
         y = ldiv!(cache.u, F, cache.b)
-        SciMLBase.build_linear_solution(alg, y, nothing, cache; retcode = ReturnCode.Success)
+        SciMLBase.build_linear_solution(
+            alg, y, nothing, cache; retcode = ReturnCode.Success)
     else
         SciMLBase.build_linear_solution(
             alg, cache.u, nothing, cache; retcode = ReturnCode.Infeasible)
     end
 end
 
-const PREALLOCATED_CHOLMOD = cholesky(sparse(reshape([1.0],1,1)))
+const PREALLOCATED_CHOLMOD = cholesky(sparse(reshape([1.0], 1, 1)))
 
 function LinearSolve.init_cacheval(alg::CHOLMODFactorization,
         A::Union{SparseMatrixCSC{T, Int}, Symmetric{T, SparseMatrixCSC{T, Int}}}, b, u,
@@ -290,7 +299,7 @@ function LinearSolve.init_cacheval(alg::CHOLMODFactorization,
         maxiters::Int, abstol, reltol,
         verbose::Bool, assumptions::OperatorAssumptions) where {T <:
                                                                 BLASELTYPES}
-    cholesky(sparse(reshape([one(T)],1,1)))
+    cholesky(sparse(reshape([one(T)], 1, 1)))
 end
 
 function LinearSolve.init_cacheval(alg::CHOLMODFactorization,
@@ -368,7 +377,8 @@ function LinearSolve.init_cacheval(
     nothing
 end
 
-function LinearSolve.init_cacheval(alg::QRFactorization, A::SparseMatrixCSC{Float64, <:Integer}, b, u, Pl, Pr,
+function LinearSolve.init_cacheval(
+        alg::QRFactorization, A::SparseMatrixCSC{Float64, <:Integer}, b, u, Pl, Pr,
         maxiters::Int, abstol, reltol, verbose::Bool,
         assumptions::OperatorAssumptions)
     ArrayInterface.qr_instance(convert(AbstractMatrix, A), alg.pivot)

ext/LinearSolveSparspakExt.jl

@@ -20,16 +20,16 @@ function LinearSolve.init_cacheval(
         ::SparspakFactorization, A::AbstractSparseMatrixCSC{Tv, Ti}, b, u, Pl, Pr, maxiters::Int, abstol,
         reltol,
         verbose::Bool, assumptions::OperatorAssumptions) where {Tv, Ti}
-    
-    if size(A,1) == size(A,2)
+    if size(A, 1) == size(A, 2)
         A = convert(AbstractMatrix, A)
         if A isa SparseArrays.AbstractSparseArray
             return sparspaklu(
                 SparseMatrixCSC{Tv, Ti}(size(A)..., getcolptr(A), rowvals(A),
                     nonzeros(A)),
                 factorize = false)
         else
-            return sparspaklu(SparseMatrixCSC{Tv, Ti}(zero(Ti), zero(Ti), [one(Ti)], Ti[], eltype(A)[]),
+            return sparspaklu(
+                SparseMatrixCSC{Tv, Ti}(zero(Ti), zero(Ti), [one(Ti)], Ti[], eltype(A)[]),
                 factorize = false)
         end
     else