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Add and fix some aqua tests #1965

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merged 7 commits into from
Jun 16, 2023
Merged

Add and fix some aqua tests #1965

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e307ca8
Add aqua tests
charleskawczynski Jun 14, 2023
322c038
Fix aqua tests
charleskawczynski Jun 14, 2023
7c9e435
More Aqua tests.
maleadt Jun 16, 2023
7d22012
Remove unused typevars.
maleadt Jun 16, 2023
da1f2b3
Formatting.
maleadt Jun 16, 2023
2df324e
Disable piracy test on 1.6.
maleadt Jun 16, 2023
26de26c
Move test.
maleadt Jun 16, 2023
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1 change: 0 additions & 1 deletion Project.toml
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Expand Up @@ -10,7 +10,6 @@ CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"
CUDA_Driver_jll = "4ee394cb-3365-5eb0-8335-949819d2adfc"
CUDA_Runtime_Discovery = "1af6417a-86b4-443c-805f-a4643ffb695f"
CUDA_Runtime_jll = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"
CompilerSupportLibraries_jll = "e66e0078-7015-5450-92f7-15fbd957f2ae"
ExprTools = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
GPUCompiler = "61eb1bfa-7361-4325-ad38-22787b887f55"
Expand Down
68 changes: 34 additions & 34 deletions lib/cusparse/linalg.jl
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Expand Up @@ -16,7 +16,7 @@ function sum_dim1(A::CuSparseMatrixCSR{T}) where {T}
m, n = size(A)
rowsum = CuVector{Float64}(undef, m)
kernel_f = @cuda launch=false kernel(T, rowsum, A)

config = launch_configuration(kernel_f.fun)
threads = min(n, config.threads)
blocks = cld(n, threads)
Expand All @@ -40,7 +40,7 @@ function sum_dim2(A::CuSparseMatrixCSR{T}) where {T}
m, n = size(A)
colsum = CuVector{Float64}(undef, n)
kernel_f = @cuda launch=false kernel(T, colsum, A)

config = launch_configuration(kernel_f.fun)
threads = min(m, config.threads)
blocks = cld(m, threads)
Expand Down Expand Up @@ -97,7 +97,7 @@ function SparseArrays.droptol!(A::CuSparseMatrixCOO, tol::Real)
copyto!(A, B)
end

function Base.reshape(A::CuSparseMatrixCOO, dims::NTuple{N,Int}) where {N}
function Base.reshape(A::CuSparseMatrixCOO, dims::Dims)
nrows, ncols = size(A)
flat_indices = nrows .* (A.colInd .- 1) .+ A.rowInd .- 1
new_col, new_row = div.(flat_indices, dims[1]) .+ 1, rem.(flat_indices, dims[1]) .+ 1
Expand Down Expand Up @@ -125,7 +125,7 @@ function LinearAlgebra.kron(A::CuSparseMatrixCOO{T}, B::CuSparseMatrixCOO{T}) wh
col .+= repeat(B.colInd .- 1, outer = Annz) .+ 1

data .*= repeat(B.nzVal, outer = Annz)

sparse(row, col, data, out_shape..., fmt = :coo)
end

Expand All @@ -150,7 +150,7 @@ function LinearAlgebra.kron(A::CuSparseMatrixCOO{T}, B::Diagonal) where {T}
col .+= CuVector(repeat(0:nB-1, outer = Annz)) .+ 1

data .*= repeat(CUDA.ones(T, nB), outer = Annz)

sparse(row, col, data, out_shape..., fmt = :coo)
end

Expand All @@ -175,62 +175,62 @@ function LinearAlgebra.kron(A::Diagonal, B::CuSparseMatrixCOO{T}) where {T}
col .+= repeat(B.colInd .- 1, outer = Annz) .+ 1

data .*= repeat(B.nzVal, outer = Annz)

sparse(row, col, data, out_shape..., fmt = :coo)
end

for SparseMatrixType in [:CuSparseMatrixCSC, :CuSparseMatrixCSR]
@eval begin
LinearAlgebra.triu(A::$SparseMatrixType{T,M}, k::Integer) where {T,M} =
LinearAlgebra.triu(A::$SparseMatrixType{T}, k::Integer) where {T} =
$SparseMatrixType( triu(CuSparseMatrixCOO(A), k) )
LinearAlgebra.triu(A::Transpose{T,<:$SparseMatrixType}, k::Integer) where {T} =
LinearAlgebra.triu(A::Transpose{T,<:$SparseMatrixType}, k::Integer) where {T} =
$SparseMatrixType( triu(CuSparseMatrixCOO(_sptranspose(parent(A))), k) )
LinearAlgebra.triu(A::Adjoint{T,<:$SparseMatrixType}, k::Integer) where {T} =
LinearAlgebra.triu(A::Adjoint{T,<:$SparseMatrixType}, k::Integer) where {T} =
$SparseMatrixType( triu(CuSparseMatrixCOO(_spadjoint(parent(A))), k) )
LinearAlgebra.tril(A::$SparseMatrixType{T,M}, k::Integer) where {T,M} =

LinearAlgebra.tril(A::$SparseMatrixType{T}, k::Integer) where {T} =
$SparseMatrixType( tril(CuSparseMatrixCOO(A), k) )
LinearAlgebra.tril(A::Transpose{T,<:$SparseMatrixType}, k::Integer) where {T} =
LinearAlgebra.tril(A::Transpose{T,<:$SparseMatrixType}, k::Integer) where {T} =
$SparseMatrixType( tril(CuSparseMatrixCOO(_sptranspose(parent(A))), k) )
LinearAlgebra.tril(A::Adjoint{T,<:$SparseMatrixType}, k::Integer) where {T} =
LinearAlgebra.tril(A::Adjoint{T,<:$SparseMatrixType}, k::Integer) where {T} =
$SparseMatrixType( tril(CuSparseMatrixCOO(_spadjoint(parent(A))), k) )
LinearAlgebra.triu(A::Union{$SparseMatrixType{T,M}, Transpose{T,<:$SparseMatrixType}, Adjoint{T,<:$SparseMatrixType}}) where {T,M} =

LinearAlgebra.triu(A::Union{$SparseMatrixType{T}, Transpose{T,<:$SparseMatrixType}, Adjoint{T,<:$SparseMatrixType}}) where {T} =
$SparseMatrixType( triu(CuSparseMatrixCOO(A), 0) )
LinearAlgebra.tril(A::Union{$SparseMatrixType{T,M}, Transpose{T,<:$SparseMatrixType}, Adjoint{T,<:$SparseMatrixType}}) where {T,M} =
LinearAlgebra.tril(A::Union{$SparseMatrixType{T},Transpose{T,<:$SparseMatrixType}, Adjoint{T,<:$SparseMatrixType}}) where {T} =
$SparseMatrixType( tril(CuSparseMatrixCOO(A), 0) )

LinearAlgebra.kron(A::$SparseMatrixType{T,M}, B::$SparseMatrixType{T,M}) where {T,M} =
LinearAlgebra.kron(A::$SparseMatrixType{T}, B::$SparseMatrixType{T}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(A), CuSparseMatrixCOO(B)) )
LinearAlgebra.kron(A::$SparseMatrixType{T,M}, B::Diagonal) where {T,M} =
LinearAlgebra.kron(A::$SparseMatrixType{T}, B::Diagonal) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(A), B) )
LinearAlgebra.kron(A::Diagonal, B::$SparseMatrixType{T,M}) where {T,M} =
LinearAlgebra.kron(A::Diagonal, B::$SparseMatrixType{T}) where {T} =
$SparseMatrixType( kron(A, CuSparseMatrixCOO(B)) )
LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::$SparseMatrixType{T,M}) where {T,M} =

LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::$SparseMatrixType{T}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_sptranspose(parent(A))), CuSparseMatrixCOO(B)) )
LinearAlgebra.kron(A::$SparseMatrixType{T,M}, B::Transpose{T,<:$SparseMatrixType}) where {T,M} =
LinearAlgebra.kron(A::$SparseMatrixType{T}, B::Transpose{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(A), CuSparseMatrixCOO(_sptranspose(parent(B)))) )
LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::Transpose{T,<:$SparseMatrixType}) where {T} =
LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::Transpose{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_sptranspose(parent(A))), CuSparseMatrixCOO(_sptranspose(parent(B)))) )
LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::Diagonal) where {T} =
LinearAlgebra.kron(A::Transpose{T,<:$SparseMatrixType}, B::Diagonal) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_sptranspose(parent(A))), B) )
LinearAlgebra.kron(A::Diagonal, B::Transpose{T,<:$SparseMatrixType}) where {T} =
LinearAlgebra.kron(A::Diagonal, B::Transpose{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(A, CuSparseMatrixCOO(_sptranspose(parent(B)))) )

LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::$SparseMatrixType{T,M}) where {T,M} =
LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::$SparseMatrixType{T}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_spadjoint(parent(A))), CuSparseMatrixCOO(B)) )
LinearAlgebra.kron(A::$SparseMatrixType{T,M}, B::Adjoint{T,<:$SparseMatrixType}) where {T,M} =
LinearAlgebra.kron(A::$SparseMatrixType{T}, B::Adjoint{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(A), CuSparseMatrixCOO(_spadjoint(parent(B)))) )
LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::Adjoint{T,<:$SparseMatrixType}) where {T} =
LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::Adjoint{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_spadjoint(parent(A))), CuSparseMatrixCOO(_spadjoint(parent(B)))) )
LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::Diagonal) where {T} =
LinearAlgebra.kron(A::Adjoint{T,<:$SparseMatrixType}, B::Diagonal) where {T} =
$SparseMatrixType( kron(CuSparseMatrixCOO(_spadjoint(parent(A))), B) )
LinearAlgebra.kron(A::Diagonal, B::Adjoint{T,<:$SparseMatrixType}) where {T} =
LinearAlgebra.kron(A::Diagonal, B::Adjoint{T,<:$SparseMatrixType}) where {T} =
$SparseMatrixType( kron(A, CuSparseMatrixCOO(_spadjoint(parent(B)))) )


function Base.reshape(A::$SparseMatrixType, dims::NTuple{N,Int}) where {N}
function Base.reshape(A::$SparseMatrixType, dims::Dims)
B = CuSparseMatrixCOO(A)
$SparseMatrixType(reshape(B, dims))
end
Expand All @@ -244,16 +244,16 @@ for SparseMatrixType in [:CuSparseMatrixCSC, :CuSparseMatrixCSR]
function LinearAlgebra.exp(A::$SparseMatrixType; threshold = 1e-7, nonzero_tol = 1e-14)
rows = LinearAlgebra.checksquare(A) # Throws exception if not square
typeA = eltype(A)

mat_norm = norm(A, Inf)
scaling_factor = nextpow(2, mat_norm) # Native routine, faster
A = A ./ scaling_factor
delta = 1

P = $SparseMatrixType(spdiagm(0 => ones(eltype(A), rows)))
next_term = P
n = 1

while delta > threshold
next_term = typeA(1 / n) * A * next_term
droptol!(next_term, nonzero_tol)
Expand Down
34 changes: 25 additions & 9 deletions src/array.jl
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Expand Up @@ -237,9 +237,26 @@ array) or a tuple of the array dimensions. `own` optionally specified whether Ju
take ownership of the memory, calling `cudaFree` when the array is no longer referenced. The
`ctx` argument determines the CUDA context where the data is allocated in.
"""
function Base.unsafe_wrap(::Union{Type{CuArray},Type{CuArray{T}},Type{CuArray{T,N}},Type{CuArray{T,N,B}}},
function Base.unsafe_wrap(::Union{Type{CuArray},Type{CuArray{T}},Type{CuArray{T,N}}},
ptr::CuPtr{T}, dims::NTuple{N,Int};
own::Bool=false, ctx::CuContext=context()) where {T,N}
buf = _unsafe_wrap(T, ptr, dims; own, ctx)
storage = ArrayStorage(buf, own ? 1 : -1)
CuArray{T, length(dims)}(storage, dims)
end
function Base.unsafe_wrap(::Type{CuArray{T,N,B}},
ptr::CuPtr{T}, dims::NTuple{N,Int};
own::Bool=false, ctx::CuContext=context()) where {T,N,B}
buf = _unsafe_wrap(T, ptr, dims; own, ctx)
if typeof(buf) !== B
error("Declared buffer type does not match inferred buffer type.")
end
storage = ArrayStorage(buf, own ? 1 : -1)
CuArray{T, length(dims)}(storage, dims)
end

function _unsafe_wrap(::Type{T}, ptr::CuPtr{T}, dims::NTuple{N,Int};
own::Bool=false, ctx::CuContext=context()) where {T,N}
isbitstype(T) || error("Can only unsafe_wrap a pointer to a bits type")
sz = prod(dims) * sizeof(T)

Expand All @@ -259,16 +276,15 @@ function Base.unsafe_wrap(::Union{Type{CuArray},Type{CuArray{T}},Type{CuArray{T,
catch err
error("Could not identify the buffer type; are you passing a valid CUDA pointer to unsafe_wrap?")
end

if @isdefined(B) && typeof(buf) !== B
error("Declared buffer type does not match inferred buffer type.")
end

storage = ArrayStorage(buf, own ? 1 : -1)
CuArray{T, length(dims)}(storage, dims)
return buf
end

function Base.unsafe_wrap(Atype::Union{Type{CuArray},Type{CuArray{T}},Type{CuArray{T,1}},Type{CuArray{T,1,B}}},
function Base.unsafe_wrap(Atype::Union{Type{CuArray},Type{CuArray{T}},Type{CuArray{T,1}}},
p::CuPtr{T}, dim::Int;
own::Bool=false, ctx::CuContext=context()) where {T}
unsafe_wrap(Atype, p, (dim,); own, ctx)
end
function Base.unsafe_wrap(Atype::Type{CuArray{T,1,B}},
p::CuPtr{T}, dim::Int;
own::Bool=false, ctx::CuContext=context()) where {T,B}
unsafe_wrap(Atype, p, (dim,); own, ctx)
Expand Down
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