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Issue #2533: Preserving the buffer type in linear algebra #2534

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Issue #2533: Preserving the buffer type in linear algebra #2534

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d877b22
An attempt to fix buffer type linear algebra issue
kmp5VT Oct 28, 2024
524514e
Use similar over CuArray
kmp5VT Oct 29, 2024
9822cd6
Merge branch 'master' into kmp5/debug/issue_2533
kmp5VT Oct 29, 2024
c8f1834
Remove memory_type calls
kmp5VT Oct 30, 2024
702bc57
Merge branch 'kmp5/debug/issue_2533' of github.com:kmp5VT/CUDA.jl int…
kmp5VT Oct 30, 2024
5a8abbe
Merge branch 'master' into kmp5/debug/issue_2533
kmp5VT Oct 30, 2024
3c2b072
revert changes
kmp5VT Oct 30, 2024
6a497c5
Merge branch 'kmp5/debug/issue_2533' of github.com:kmp5VT/CUDA.jl int…
kmp5VT Oct 30, 2024
99f920b
Merge branch 'master' into kmp5/debug/issue_2533
kmp5VT Nov 1, 2024
cdb0d24
Merge branch 'master' into kmp5/debug/issue_2533
kmp5VT Nov 13, 2024
3894639
Add changes back to gebrd!
kmp5VT Nov 13, 2024
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45 changes: 22 additions & 23 deletions lib/cusolver/dense.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -169,7 +169,7 @@ for (bname, fname,elty) in ((:cusolverDnSgeqrf_bufferSize, :cusolverDnSgeqrf, :F

function geqrf!(A::StridedCuMatrix{$elty})
m, n = size(A)
tau = CuArray{$elty}(undef, min(m, n))
tau = similar(A, $elty, min(m,n))
geqrf!(A, tau)
end
end
Expand Down Expand Up @@ -361,10 +361,10 @@ for (bname, fname, elty, relty) in ((:cusolverDnSgebrd_bufferSize, :cusolverDnSg
end

k = min(m, n)
D = CuArray{$relty}(undef, k)
E = CUDA.zeros($relty, k)
TAUQ = CuArray{$elty}(undef, k)
TAUP = CuArray{$elty}(undef, k)
D = similar(A, $relty, k)
E = fill!(similar(A, $relty, k), zero($relty))
TAUQ = similar(A, $elty, k)
TAUP = similar(A, $elty, k)

with_workspace(dh.workspace_gpu, bufferSize) do buffer
$fname(dh, m, n, A, lda, D, E, TAUQ, TAUP,
Expand Down Expand Up @@ -392,22 +392,22 @@ for (bname, fname, elty, relty) in ((:cusolverDnSgesvd_bufferSize, :cusolverDnSg
lda = max(1, stride(A, 2))

U = if jobu === 'A'
CuArray{$elty}(undef, m, m)
similar(A, $elty, (m, m))
elseif jobu == 'S' || jobu === 'O'
CuArray{$elty}(undef, m, min(m, n))
similar(A, $elty, (m, min(m, n)))
elseif jobu === 'N'
CU_NULL
else
error("jobu must be one of 'A', 'S', 'O', or 'N'")
end
ldu = U == CU_NULL ? 1 : max(1, stride(U, 2))

S = CuArray{$relty}(undef, min(m, n))
S = similar(A, $relty, min(m, n))

Vt = if jobvt === 'A'
CuArray{$elty}(undef, n, n)
similar(A, $elty, (n, n))
elseif jobvt === 'S' || jobvt === 'O'
CuArray{$elty}(undef, min(m, n), n)
similar(A, $elty, (min(m, n), n))
elseif jobvt === 'N'
CU_NULL
else
Expand Down Expand Up @@ -449,22 +449,21 @@ for (bname, fname, elty, relty) in ((:cusolverDnSgesvdj_bufferSize, :cusolverDnS
max_sweeps::Int=100)
m,n = size(A)
lda = max(1, stride(A, 2))

# Warning! For some reason, the solver needs to access U and V even
# when only the values are requested
U = if jobz === 'V' && econ == 1 && m > n
CuArray{$elty}(undef, m, n)
similar(A, $elty, (m ,n))
else
CuArray{$elty}(undef, m, m)
similar(A, $elty, (m, m))
end
ldu = max(1, stride(U, 2))

S = CuArray{$relty}(undef, min(m, n))
S = similar(A, $relty, min(m,n))

V = if jobz === 'V' && econ == 1 && m < n
CuArray{$elty}(undef, n, m)
similar(A, $elty, (n, m))
else
CuArray{$elty}(undef, n, n)
similar(A, $elty, (n, n))
end
ldv = max(1, stride(V, 2))

Expand Down Expand Up @@ -510,13 +509,13 @@ for (bname, fname, elty, relty) in ((:cusolverDnSgesvdjBatched_bufferSize, :cuso
throw(ArgumentError("CUSOLVER's gesvdjBatched currently requires m <=32 and n <= 32"))
end
lda = max(1, stride(A, 2))

U = CuArray{$elty}(undef, m, m, batchSize)
U = similar(A, $elty, (m, m, batchSize))
ldu = max(1, stride(U, 2))

S = CuArray{$relty}(undef, min(m, n), batchSize)
S = similar(A, $relty, (min(m, n), batchSize))

V = CuArray{$elty}(undef, n, n, batchSize)
V = similar(A, $elty, n, n, batchSize)
ldv = max(1, stride(V, 2))

params = Ref{gesvdjInfo_t}(C_NULL)
Expand Down Expand Up @@ -569,14 +568,14 @@ for (bname, fname, elty, relty) in ((:cusolverDnSgesvdaStridedBatched_bufferSize
lda = max(1, stride(A, 2))
strideA = stride(A, 3)

U = CuArray{$elty}(undef, m, rank, batchSize)
U = similar(A, $elty, (m, rank, batchSize))
ldu = max(1, stride(U, 2))
strideU = stride(U, 3)

S = CuArray{$relty}(undef, rank, batchSize)
S = similar(A, $relty, (rank, batchSize))
strideS = stride(S, 2)

V = CuArray{$elty}(undef, n, rank, batchSize)
V = similar(A, $elty, (n, rank, batchSize))
ldv = max(1, stride(V, 2))
strideV = stride(V, 3)

Expand Down