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blas.jl
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blas.jl
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using LinearAlgebra
@testset "CUBLAS" begin
using CuArrays.CUBLAS
using CuArrays.CUBLAS: band, bandex
m = 20
n = 35
k = 13
@test_throws ArgumentError CUBLAS.cublasop('V')
@test_throws ArgumentError CUBLAS.cublasfill('V')
@test_throws ArgumentError CUBLAS.cublasdiag('V')
@test_throws ArgumentError CUBLAS.cublasside('V')
# this is an internal function, but only used on some devices so make sure it works
CUBLAS.cublasSetMathMode(CUBLAS.CUBLAS_DEFAULT_MATH)
#################
# level 1 tests #
#################
@testset "Level 1 with element type $T" for T in [Float32, Float64, ComplexF32, ComplexF64]
A = CuArray(rand(T, m))
B = CuArray{T}(undef, m)
CuArrays.CUBLAS.blascopy!(m,A,1,B,1)
@test Array(A) == Array(B)
@test testf(rmul!, rand(T, 6, 9, 3), Ref(rand()))
@test testf(dot, rand(T, m), rand(T, m))
@test testf(*, transpose(rand(T, m)), rand(T, m))
@test testf(*, rand(T, m)', rand(T, m))
@test testf(norm, rand(T, m))
@test testf(BLAS.asum, rand(T, m))
@test testf(BLAS.axpy!, Ref(rand()), rand(T, m), rand(T, m))
if T <: Real
@test testf(argmin, rand(T, m))
@test testf(argmax, rand(T, m))
end
end # level 1 testset
@testset "element type $elty" for elty in [Float32, Float64, ComplexF32, ComplexF64]
alpha = convert(elty,2)
beta = convert(elty,3)
@testset "Level 2" begin
@testset "gemv" begin
@test testf(*, rand(elty, m, n), rand(elty, n))
@test testf(*, transpose(rand(elty, m, n)), rand(elty, m))
@test testf(*, rand(elty, m, n)', rand(elty, m))
end
@testset "banded methods" begin
# bands
ku = 2
kl = 3
# generate banded matrix
A = rand(elty,m,n)
A = bandex(A,kl,ku)
# get packed format
Ab = band(A,kl,ku)
d_Ab = CuArray(Ab)
x = rand(elty,n)
d_x = CuArray(x)
@testset "gbmv!" begin
# test y = alpha*A*x + beta*y
y = rand(elty,m)
d_y = CuArray(y)
CuArrays.CUBLAS.gbmv!('N',m,kl,ku,alpha,d_Ab,d_x,beta,d_y)
BLAS.gbmv!('N',m,kl,ku,alpha,Ab,x,beta,y)
h_y = Array(d_y)
@test y ≈ h_y
# test y = alpha*transpose(A)*x + beta*y
x = rand(elty,n)
d_x = CuArray(x)
y = rand(elty,m)
d_y = CuArray(y)
CuArrays.CUBLAS.gbmv!('T',m,kl,ku,alpha,d_Ab,d_y,beta,d_x)
BLAS.gbmv!('T',m,kl,ku,alpha,Ab,y,beta,x)
h_x = Array(d_x)
@test x ≈ h_x
# test y = alpha*A'*x + beta*y
x = rand(elty,n)
d_x = CuArray(x)
y = rand(elty,m)
d_y = CuArray(y)
CuArrays.CUBLAS.gbmv!('C',m,kl,ku,alpha,d_Ab,d_y,beta,d_x)
BLAS.gbmv!('C',m,kl,ku,alpha,Ab,y,beta,x)
h_x = Array(d_x)
@test x ≈ h_x
end
x = rand(elty,n)
d_x = CuArray(x)
@testset "gbmv" begin
# test y = alpha*A*x
d_y = CuArrays.CUBLAS.gbmv('N',m,kl,ku,alpha,d_Ab,d_x)
y = zeros(elty,m)
y = BLAS.gbmv('N',m,kl,ku,alpha,Ab,x)
h_y = Array(d_y)
@test y ≈ h_y
end
A = rand(elty,m,m)
A = A + A'
nbands = 3
@test m >= 1+nbands
A = bandex(A,nbands,nbands)
# convert to 'upper' banded storage format
AB = band(A,0,nbands)
# construct x
x = rand(elty,m)
d_AB = CuArray(AB)
d_x = CuArray(x)
if elty <: Real
@testset "sbmv!" begin
y = rand(elty,m)
d_y = CuArray(y)
# sbmv!
CuArrays.CUBLAS.sbmv!('U',nbands,alpha,d_AB,d_x,beta,d_y)
y = alpha*(A*x) + beta*y
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "sbmv" begin
d_y = CuArrays.CUBLAS.sbmv('U',nbands,d_AB,d_x)
y = A*x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
else
@testset "hbmv!" begin
y = rand(elty,m)
d_y = CuArray(y)
# hbmv!
CuArrays.CUBLAS.hbmv!('U',nbands,alpha,d_AB,d_x,beta,d_y)
y = alpha*(A*x) + beta*y
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "hbmv" begin
d_y = CuArrays.CUBLAS.hbmv('U',nbands,d_AB,d_x)
y = A*x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
end
# generate triangular matrix
A = rand(elty,m,m)
# restrict to 3 bands
nbands = 3
@test m >= 1+nbands
A = bandex(A,0,nbands)
# convert to 'upper' banded storage format
AB = band(A,0,nbands)
d_AB = CuArray(AB)
@testset "tbmv!" begin
y = rand(elty, m)
# move to host
d_y = CuArray(y)
# tbmv!
CuArrays.CUBLAS.tbmv!('U','N','N',nbands,d_AB,d_y)
y = A*y
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "tbmv" begin
# tbmv
d_y = CuArrays.CUBLAS.tbmv('U','N','N',nbands,d_AB,d_x)
y = A*x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "tbsv!" begin
d_y = copy(d_x)
#tbsv!
CuArrays.CUBLAS.tbsv!('U','N','N',nbands,d_AB,d_y)
y = A\x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "tbsv" begin
d_y = CuArrays.CUBLAS.tbsv('U','N','N',nbands,d_AB,d_x)
y = A\x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
end
A = rand(elty,m,n)
dA = CuArray(A)
sA = rand(elty,m,m)
sA = sA + transpose(sA)
dsA = CuArray(sA)
hA = rand(elty,m,m)
hA = hA + hA'
dhA = CuArray(hA)
x = rand(elty,m)
dx = CuArray(x)
@testset "symv!" begin
# generate vectors
y = rand(elty,m)
# copy to device
dy = CuArray(y)
# execute on host
BLAS.symv!('U',alpha,sA,x,beta,y)
# execute on device
CuArrays.CUBLAS.symv!('U',alpha,dsA,dx,beta,dy)
# compare results
hy = Array(dy)
@test y ≈ hy
end
@testset "symv" begin
y = BLAS.symv('U',sA,x)
# execute on device
dy = CuArrays.CUBLAS.symv('U',dsA,dx)
# compare results
hy = Array(dy)
@test y ≈ hy
end
if elty <: Complex
@testset "hemv!" begin
y = rand(elty,m)
dy = CuArray(y)
# execute on host
BLAS.hemv!('U',alpha,hA,x,beta,y)
# execute on device
CuArrays.CUBLAS.hemv!('U',alpha,dhA,dx,beta,dy)
# compare results
hy = Array(dy)
@test y ≈ hy
end
@testset "hemv" begin
y = BLAS.hemv('U',hA,x)
# execute on device
dy = CuArrays.CUBLAS.hemv('U',dhA,dx)
# compare results
hy = Array(dy)
@test y ≈ hy
end
end
A = triu(sA)
dA = CuArray(A)
@testset "trmv!" begin
d_y = copy(dx)
# execute trmv!
CuArrays.CUBLAS.trmv!('U','N','N',dA,d_y)
y = A*x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "trmv" begin
d_y = CuArrays.CUBLAS.trmv('U','N','N',dA,dx)
y = A*x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "trsv!" begin
d_y = copy(dx)
# execute trsv!
CuArrays.CUBLAS.trsv!('U','N','N',dA,d_y)
y = A\x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
@testset "trsv" begin
d_y = CuArrays.CUBLAS.trsv('U','N','N',dA,dx)
y = A\x
# compare
h_y = Array(d_y)
@test y ≈ h_y
end
A = rand(elty,m,m)
x = rand(elty,m)
y = rand(elty,m)
dA = CuArray(A)
dx = CuArray(x)
dy = CuArray(y)
@testset "ger!" begin
# perform rank one update
dB = copy(dA)
CuArrays.CUBLAS.ger!(alpha,dx,dy,dB)
B = (alpha*x)*y' + A
# move to host and compare
hB = Array(dB)
@test B ≈ hB
end
@testset "syr!" begin
dB = copy(dsA)
CuArrays.CUBLAS.syr!('U',alpha,dx,dB)
B = (alpha*x)*transpose(x) + sA
# move to host and compare upper triangles
hB = Array(dB)
B = triu(B)
hB = triu(hB)
@test B ≈ hB
end
if elty <: Complex
@testset "her!" begin
dB = copy(dhA)
# perform rank one update
CuArrays.CUBLAS.her!('U',alpha,dx,dB)
B = (alpha*x)*x' + hA
# move to host and compare upper triangles
hB = Array(dB)
B = triu(B)
hB = triu(hB)
@test B ≈ hB
end
@testset "her2!" begin
dB = copy(dhA)
CuArrays.CUBLAS.her2!('U',alpha,dx,dy,dB)
B = (alpha*x)*y' + y*(alpha*x)' + hA
# move to host and compare upper triangles
hB = Array(dB)
B = triu(B)
hB = triu(hB)
@test B ≈ hB
end
end
end
@testset "Level 3" begin
A = rand(elty,m,k)
B = rand(elty,k,n)
C1 = rand(elty,m,n)
C2 = copy(C1)
d_A = CuArray(A)
d_B = CuArray(B)
d_C1 = CuArray(C1)
d_C2 = CuArray(C2)
hA = rand(elty,m,m)
hA = hA + hA'
dhA = CuArray(hA)
sA = rand(elty,m,m)
sA = sA + transpose(sA)
dsA = CuArray(sA)
@testset "gemm!" begin
CuArrays.CUBLAS.gemm!('N','N',alpha,d_A,d_B,beta,d_C1)
mul!(d_C2, d_A, d_B)
h_C1 = Array(d_C1)
h_C2 = Array(d_C2)
C1 = (alpha*A)*B + beta*C1
C2 = A*B
# compare
@test C1 ≈ h_C1
@test C2 ≈ h_C2
end
@testset "gemm" begin
d_C = CuArrays.CUBLAS.gemm('N','N',d_A,d_B)
C = A*B
C2 = d_A * d_B
# compare
h_C = Array(d_C)
h_C2 = Array(C2)
@test C ≈ h_C
@test C ≈ h_C2
end
@testset "xt_gemm!" begin
CuArrays.CUBLAS.xt_gemm!('N','N',alpha,d_A,d_B,beta,d_C1)
mul!(d_C2, d_A, d_B)
h_C1 = Array(d_C1)
h_C2 = Array(d_C2)
C1 = (alpha*A)*B + beta*C1
C2 = A*B
# compare
@test C1 ≈ h_C1
@test C2 ≈ h_C2
end
@testset "xt_gemm" begin
d_C = CuArrays.CUBLAS.xt_gemm('N','N',d_A,d_B)
C = A*B
C2 = d_A * d_B
# compare
h_C = Array(d_C)
h_C2 = Array(C2)
@test C ≈ h_C
@test C ≈ h_C2
end
# generate matrices
bA = [rand(elty,m,k) for i in 1:10]
bB = [rand(elty,k,n) for i in 1:10]
bC = [rand(elty,m,n) for i in 1:10]
# move to device
bd_A = CuArray{elty, 2}[]
bd_B = CuArray{elty, 2}[]
bd_C = CuArray{elty, 2}[]
for i in 1:length(bA)
push!(bd_A,CuArray(bA[i]))
push!(bd_B,CuArray(bB[i]))
push!(bd_C,CuArray(bC[i]))
end
@testset "gemm_batched!" begin
# C = (alpha*A)*B + beta*C
CuArrays.CUBLAS.gemm_batched!('N','N',alpha,bd_A,bd_B,beta,bd_C)
for i in 1:length(bd_C)
bC[i] = (alpha*bA[i])*bB[i] + beta*bC[i]
h_C = Array(bd_C[i])
#compare
@test bC[i] ≈ h_C
end
end
@testset "gemm_batched" begin
bd_C = CuArrays.CUBLAS.gemm_batched('N','N',bd_A,bd_B)
for i in 1:length(bA)
bC = bA[i]*bB[i]
h_C = Array(bd_C[i])
@test bC ≈ h_C
end
end
nbatch = 10
bA = rand(elty, m, k, nbatch)
bB = rand(elty, k, n, nbatch)
bC = rand(elty, m, n, nbatch)
# move to device
bd_A = CuArray{elty, 3}(bA)
bd_B = CuArray{elty, 3}(bB)
bd_C = CuArray{elty, 3}(bC)
@testset "gemm_strided_batched!" begin
CuArrays.CUBLAS.gemm_strided_batched!('N', 'N', alpha, bd_A, bd_B, beta, bd_C)
for i in 1:nbatch
bC[:, :, i] = (alpha * bA[:, :, i]) * bB[:, :, i] + beta * bC[:, :, i]
end
h_C = Array(bd_C)
@test bC ≈ h_C
end
@testset "gemm_strided_batched" begin
bd_C = CuArrays.CUBLAS.gemm_strided_batched('N', 'N', bd_A, bd_B)
for i in 1:nbatch
bC[:, :, i] = bA[:, :, i] * bB[:, :, i]
end
h_C = Array(bd_C)
@test bC ≈ h_C
# generate matrices
bA = rand(elty, k, m, nbatch)
bB = rand(elty, k, n, nbatch)
bC = zeros(elty, m, n, nbatch)
# move to device
bd_A = CuArray{elty, 3}(bA)
bd_B = CuArray{elty, 3}(bB)
bd_C = CuArrays.CUBLAS.gemm_strided_batched('T', 'N', bd_A, bd_B)
for i in 1:nbatch
bC[:, :, i] = transpose(bA[:, :, i]) * bB[:, :, i]
end
h_C = Array(bd_C)
@test bC ≈ h_C
end
B = rand(elty,m,n)
C = rand(elty,m,n)
d_B = CuArray(B)
d_C = CuArray(C)
@testset "symm!" begin
CuArrays.CUBLAS.symm!('L','U',alpha,dsA,d_B,beta,d_C)
C = (alpha*sA)*B + beta*C
# compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "symm" begin
d_C = CuArrays.CUBLAS.symm('L','U',dsA,d_B)
C = sA*B
# compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "xt_symm!" begin
CuArrays.CUBLAS.xt_symm!('L','U',alpha,dsA,d_B,beta,d_C)
C = (alpha*sA)*B + beta*C
# compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "xt_symm" begin
d_C = CuArrays.CUBLAS.xt_symm('L','U',dsA,d_B)
C = sA*B
# compare
h_C = Array(d_C)
@test C ≈ h_C
end
A = triu(rand(elty, m, m))
B = rand(elty,m,n)
C = zeros(elty,m,n)
dA = CuArray(A)
dB = CuArray(B)
dC = CuArray(C)
@testset "trmm!" begin
C = alpha*A*B
CuArrays.CUBLAS.trmm!('L','U','N','N',alpha,dA,dB,dC)
# move to host and compare
h_C = Array(dC)
@test C ≈ h_C
end
@testset "trmm" begin
C = alpha*A*B
d_C = CuArrays.CUBLAS.trmm('L','U','N','N',alpha,dA,dB)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "xt_trmm!" begin
C = alpha*A*B
CuArrays.CUBLAS.xt_trmm!('L','U','N','N',alpha,dA,dB,dC)
# move to host and compare
h_C = Array(dC)
@test C ≈ h_C
end
@testset "xt_trmm" begin
C = alpha*A*B
d_C = CuArrays.CUBLAS.xt_trmm('L','U','N','N',alpha,dA,dB)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "trsm!" begin
C = alpha*(A\B)
dC = copy(dB)
CuArrays.CUBLAS.trsm!('L','U','N','N',alpha,dA,dC)
# move to host and compare
h_C = Array(dC)
@test C ≈ h_C
end
@testset "xt_trsm!" begin
C = alpha*(A\B)
dC = copy(dB)
CuArrays.CUBLAS.xt_trsm!('L','U','N','N',alpha,dA,dC)
# move to host and compare
h_C = Array(dC)
@test C ≈ h_C
end
@testset "trsm" begin
Br = rand(elty,m,n)
Bl = rand(elty,n,m)
d_Br = CuArray(Br)
d_Bl = CuArray(Bl)
# compute
@testset "adjtype=$adjtype, uplotype=$uplotype" for
adjtype in (identity, adjoint, transpose),
uplotype in (UpperTriangular, UnitUpperTriangular, LowerTriangular, UnitLowerTriangular)
@test adjtype(uplotype(A))\Br ≈ Array(adjtype(uplotype(dA))\d_Br)
@test Bl/adjtype(uplotype(A)) ≈ Array(d_Bl/adjtype(uplotype(dA)))
end
# Check also that scaling parameter works
@test BLAS.trsm('L','U','N','N',alpha,A,Br) ≈ Array(CuArrays.CUBLAS.trsm('L','U','N','N',alpha,dA,d_Br))
end
@testset "trsm_batched!" begin
bA = [rand(elty,m,m) for i in 1:10]
map!((x) -> triu(x), bA, bA)
bB = [rand(elty,m,n) for i in 1:10]
# move to device
bd_A = CuArray{elty, 2}[]
bd_B = CuArray{elty, 2}[]
for i in 1:length(bA)
push!(bd_A,CuArray(bA[i]))
push!(bd_B,CuArray(bB[i]))
end
# compute
CuArrays.CUBLAS.trsm_batched!('L','U','N','N',alpha,bd_A,bd_B)
# move to host and compare
for i in 1:length(bd_B)
bC = alpha*(bA[i]\bB[i])
h_C = Array(bd_B[i])
#compare
@test bC ≈ h_C
end
end
@testset "trsm_batched" begin
# generate parameter alpha = rand(elty)
# generate matrices
bA = [rand(elty,m,m) for i in 1:10]
map!((x) -> triu(x), bA, bA)
bB = [rand(elty,m,n) for i in 1:10]
# move to device
bd_A = CuArray{elty, 2}[]
bd_B = CuArray{elty, 2}[]
for i in 1:length(bA)
push!(bd_A,CuArray(bA[i]))
push!(bd_B,CuArray(bB[i]))
end
# compute
bd_C = CuArrays.CUBLAS.trsm_batched('L','U','N','N',alpha,bd_A,bd_B)
# move to host and compare
for i in 1:length(bd_C)
bC = alpha*(bA[i]\bB[i])
h_C = Array(bd_C[i])
@test bC ≈ h_C
end
end
B = rand(elty,m,n)
C = rand(elty,m,n)
d_B = CuArray(B)
d_C = CuArray(C)
if elty <: Complex
@testset "hemm!" begin
# compute
C = alpha*(hA*B) + beta*C
CuArrays.CUBLAS.hemm!('L','L',alpha,dhA,d_B,beta,d_C)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "hemm" begin
C = alpha*(hA*B)
d_C = CuArrays.CUBLAS.hemm('L','U',alpha,dhA,d_B)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "xt_hemm!" begin
# compute
C = alpha*(hA*B) + beta*C
CuArrays.CUBLAS.xt_hemm!('L','L',alpha,dhA,d_B,beta,d_C)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
@testset "xt_hemm" begin
C = alpha*(hA*B)
d_C = CuArrays.CUBLAS.xt_hemm('L','U',alpha,dhA,d_B)
# move to host and compare
h_C = Array(d_C)
@test C ≈ h_C
end
end
A = rand(elty,m,n)
d_A = CuArray(A)
@testset "geam!" begin
# compute
D = alpha*A + beta*B
CuArrays.CUBLAS.geam!('N','N',alpha,d_A,beta,d_B,d_C)
# move to host and compare
h_C = Array(d_C)
@test D ≈ h_C
#test in place versions too
d_C = CuArray(C)
D = alpha*C + beta*B
CuArrays.CUBLAS.geam!('N','N',alpha,d_C,beta,d_B,d_C)
# move to host and compare
h_C = Array(d_C)
@test D ≈ h_C
d_C = CuArray(C)
D = alpha*A + beta*C
CuArrays.CUBLAS.geam!('N','N',alpha,d_A,beta,d_C,d_C)
# move to host and compare
h_C = Array(d_C)
@test D ≈ h_C
#test setting C to zero
CuArrays.CUBLAS.geam!('N','N',zero(elty),d_A,zero(elty),d_B,d_C)
h_C = Array(d_C)
@test h_C ≈ zeros(elty,m,n)
# bounds checking
@test_throws DimensionMismatch CuArrays.CUBLAS.geam!('N','T',alpha,d_A,beta,d_B,d_C)
@test_throws DimensionMismatch CuArrays.CUBLAS.geam!('T','T',alpha,d_A,beta,d_B,d_C)
@test_throws DimensionMismatch CuArrays.CUBLAS.geam!('T','N',alpha,d_A,beta,d_B,d_C)
end
@testset "geam" begin
D = alpha*A + beta*B
d_C = CuArrays.CUBLAS.geam('N','N',alpha,d_A,beta,d_B)
# move to host and compare
h_C = Array(d_C)
@test D ≈ h_C
end
A = rand(elty,m,k)
d_A = CuArray(A)
@testset "syrk!" begin
# generate matrices
d_C = CuArray(sA)
# C = (alpha*A)*transpose(A) + beta*C
CuArrays.CUBLAS.syrk!('U','N',alpha,d_A,beta,d_C)
C = (alpha*A)*transpose(A) + beta*sA
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "xt_syrk!" begin
# generate matrices
d_C = CuArray(sA)
# C = (alpha*A)*transpose(A) + beta*C
CuArrays.CUBLAS.xt_syrk!('U','N',alpha,d_A,beta,d_C)
C = (alpha*A)*transpose(A) + beta*sA
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "syrk" begin
# C = A*transpose(A)
d_C = CuArrays.CUBLAS.syrk('U','N',d_A)
C = A*transpose(A)
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "xt_syrk" begin
# C = A*transpose(A)
d_C = CuArrays.CUBLAS.xt_syrk('U','N',d_A)
C = A*transpose(A)
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
if elty <: Complex
@testset "herk!" begin
d_C = CuArray(dhA)
CuArrays.CUBLAS.herk!('U','N',alpha,d_A,beta,d_C)
C = alpha*(A*A') + beta*C
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "herk" begin
d_C = CuArrays.CUBLAS.herk('U','N',d_A)
C = A*A'
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "xt_herk!" begin
d_C = CuArray(dhA)
CuArrays.CUBLAS.xt_herk!('U','N',alpha,d_A,beta,d_C)
C = alpha*(A*A') + beta*C
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
@testset "xt_herk" begin
d_C = CuArrays.CUBLAS.xt_herk('U','N',d_A)
C = A*A'
C = triu(C)
# move to host and compare
h_C = Array(d_C)
h_C = triu(C)
@test C ≈ h_C
end
end
A = rand(elty,m,k)
B = rand(elty,m,k)
C = rand(elty,m,m)
C = C + transpose(C)
# move to device
d_A = CuArray(A)
d_B = CuArray(B)
d_C = CuArray(C)
@testset "syr2k!" begin
# compute
C = alpha*(A*transpose(B) + B*transpose(A)) + beta*C
CuArrays.CUBLAS.syr2k!('U','N',alpha,d_A,d_B,beta,d_C)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
@testset "syr2k" begin
C = alpha*(A*transpose(B) + B*transpose(A))
d_C = CuArrays.CUBLAS.syr2k('U','N',alpha,d_A,d_B)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
if elty <: Complex
@testset "her2k!" begin
elty1 = elty
elty2 = real(elty)
# generate parameters
α = rand(elty1)
β = rand(elty2)
C = C + C'
d_C = CuArray(C)
C = α*(A*B') + conj(α)*(B*A') + β*C
CuArrays.CUBLAS.her2k!('U','N',α,d_A,d_B,β,d_C)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
@testset "her2k" begin
C = A*B' + B*A'
d_C = CuArrays.CUBLAS.her2k('U','N',d_A,d_B)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
@testset "xt_her2k!" begin
elty1 = elty
elty2 = real(elty)
# generate parameters
α = rand(elty1)
β = rand(elty2)
C = C + C'
d_C = CuArray(C)
C = α*(A*B') + conj(α)*(B*A') + β*C
CuArrays.CUBLAS.xt_her2k!('U','N',α,d_A,d_B,β,d_C)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
@testset "her2k" begin
C = A*B' + B*A'
d_C = CuArrays.CUBLAS.her2k('U','N',d_A,d_B)
# move back to host and compare
C = triu(C)
h_C = Array(d_C)
h_C = triu(h_C)
@test C ≈ h_C
end
end
end
@testset "extensions" begin
@testset "getrf_batched!" begin
Random.seed!(1)
local k
# generate matrices
A = [rand(elty,m,m) for i in 1:10]
# move to device
d_A = CuArray{elty, 2}[]
for i in 1:length(A)
push!(d_A,CuArray(A[i]))
end
pivot, info = CuArrays.CUBLAS.getrf_batched!(d_A, false)
h_info = Array(info)
for As in 1:length(d_A)
C = lu!(copy(A[As]), Val(false)) # lu(A[As],pivot=false)
h_A = Array(d_A[As])
#reconstruct L,U
dL = Matrix(one(elty)*I, m, m)
dU = zeros(elty,(m,m))
k = h_info[As]
if( k >= 0 )
dL += tril(h_A,-k-1)
dU += triu(h_A,k)
end
#compare
@test C.L ≈ dL rtol=1e-2
@test C.U ≈ dU rtol=1e-2
end
for i in 1:length(A)
d_A[ i ] = CuArray(A[i])
end
pivot, info = CuArrays.CUBLAS.getrf_batched!(d_A, true)
h_info = Array(info)
h_pivot = Array(pivot)
for As in 1:length(d_A)
C = lu(A[As])
h_A = Array(d_A[As])
#reconstruct L,U
dL = Matrix(one(elty)*I, m, m)
dU = zeros(elty,(m,m))
k = h_info[As]
if( k >= 0 )
dL += tril(h_A,-k-1)
dU += triu(h_A,k)
end
#compare pivots
@test length(setdiff(h_pivot[:,As],C.p)) == 0
#make device pivot matrix
P = Matrix(1.0*I, m, m)
for row in 1:m
temp = copy(P[row,:])
P[row,:] = P[h_pivot[row,As],:]
P[h_pivot[row,As],:] = temp
end
@test inv(P)*dL*dU ≈ inv(C.P) * C.L * C.U
end
end
@testset "getrf_batched" begin
local k
# generate matrices
A = [rand(elty,m,m) for i in 1:10]
# move to device
d_A = CuArray{elty, 2}[]
for i in 1:length(A)
push!(d_A,CuArray(A[i]))
end
pivot, info, d_B = CuArrays.CUBLAS.getrf_batched(d_A, false)
h_info = Array(info)
for Bs in 1:length(d_B)
C = lu!(copy(A[Bs]),Val(false)) # lu(A[Bs],pivot=false)
h_B = Array(d_B[Bs])
#reconstruct L,U
dL = Matrix(one(elty)*I, m, m)
dU = zeros(elty,(m,m))
k = h_info[Bs]
if( h_info[Bs] >= 0 )
dU += triu(h_B,k)
dL += tril(h_B,-k-1)
end
#compare
@test C.L ≈ dL rtol=1e-2
@test C.U ≈ dU rtol=1e-2
end
end
@testset "getri_batched" begin
# generate matrices
A = [rand(elty,m,m) for i in 1:10]
# move to device
d_A = CuArray{elty, 2}[]
for i in 1:length(A)
push!(d_A,CuArray(A[i]))
end
pivot, info = CuArrays.CUBLAS.getrf_batched!(d_A, true)
h_info = Array(info)
for Cs in 1:length(h_info)
@test h_info[Cs] == 0
end
pivot, info, d_C = CuArrays.CUBLAS.getri_batched(d_A, pivot)
h_info = Array(info)
for Cs in 1:length(d_C)
C = inv(A[Cs])
h_C = Array(d_C[Cs])
@test h_info[Cs] == 0
@test C ≈ h_C rtol=1e-2
end
end
@testset "matinv_batched" begin
# generate matrices
A = [rand(elty,m,m) for i in 1:10]
# move to device
d_A = CuArray{elty, 2}[]
for i in 1:length(A)
push!(d_A,CuArray(A[i]))
end
info, d_C = CuArrays.CUBLAS.matinv_batched(d_A)
for Cs in 1:length(d_C)
C = inv(A[Cs])
h_C = Array(d_C[Cs])
@test C ≈ h_C
end
end
@testset "geqrf_batched!" begin
# generate matrices
A = [rand(elty,m,n) for i in 1:10]
# move to device
d_A = CuArray{elty, 2}[]
for i in 1:length(A)
push!(d_A,CuArray(A[i]))
end
tau, d_A = CuArrays.CUBLAS.geqrf_batched!(d_A)
for As in 1:length(d_A)
C = qr(A[As])
h_A = Array(d_A[As])
h_tau = Array(tau[As])