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add support for CUDA.jl v5 #283

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Dec 1, 2023
Merged

add support for CUDA.jl v5 #283

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2 changes: 1 addition & 1 deletion lib/MadNLPGPU/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"

[compat]
CUDA = "~4"
CUDA = "4, 5"
CUSOLVERRF = "0.2"
KernelAbstractions = "0.9"
MadNLP = "0.7"
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9 changes: 2 additions & 7 deletions lib/MadNLPGPU/test/densekkt_gpu.jl
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Expand Up @@ -3,7 +3,6 @@ using CUDA
using MadNLPTests

function _compare_gpu_with_cpu(KKTSystem, n, m, ind_fixed)

for (T,tol,atol) in [
(Float32,1e-3,1e-1),
(Float64,1e-8,1e-6)
Expand All @@ -16,13 +15,9 @@ function _compare_gpu_with_cpu(KKTSystem, n, m, ind_fixed)
:tol=>tol
)

# Host evaluator
nlph = MadNLPTests.DenseDummyQP(zeros(T,n); m=m, fixed_variables=ind_fixed)

# Some weird issue: there's some non-deterministic behavior in generating the model for the first call
# Not sure where this error is originating, but seems to be resolved in v1.10
# Here, we call this twice to avoid this error
nlpd = MadNLPTests.DenseDummyQP(CUDA.zeros(T,n); m=m, fixed_variables=CuArray(ind_fixed))

# Device evaluator
nlpd = MadNLPTests.DenseDummyQP(CUDA.zeros(T,n); m=m, fixed_variables=CuArray(ind_fixed))

# Solve on CPU
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16 changes: 9 additions & 7 deletions lib/MadNLPTests/src/Instances/dummy_qp.jl
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Expand Up @@ -25,7 +25,7 @@ function NLPModels.hess_structure!(qp::DenseDummyQP, I::AbstractVector{T}, J::Ab
copyto!(J, qp.hcols)
end

function NLPModels.obj(qp::DenseDummyQP, x::AbstractVector)
function NLPModels.obj(qp::DenseDummyQP{T}, x::AbstractVector{T}) where T
mul!(qp.buffer, qp.P, x)
return 0.5 * dot(x, qp.buffer) + dot(qp.q, x)
end
Expand Down Expand Up @@ -84,9 +84,15 @@ function DenseDummyQP(
end

Random.seed!(1)
# Generate random values.
# N.B.: we need to allocate the matrix P_ right after the vector
# q_ if we want to obtain a deterministic behavior: the seed is not working
# if we call the function `randn` after allocating vectors on the device.
q_ = randn(n)
P_ = randn(n, n)

y0 = fill!(similar(x0, m), zero(T))
q = copyto!(similar(x0, n), randn(n))
q = copyto!(similar(x0, n), q_)
buffer = similar(x0, n)

# Bound constraints
Expand All @@ -100,7 +106,7 @@ function DenseDummyQP(
xl[fixed_variables] .= @view(xu[fixed_variables])

# Build QP problem 0.5 * x' * P * x + q' * x
P = copyto!(similar(x0, n , n), randn(n,n))
P = copyto!(similar(x0, n , n), P_)
P = P*P' # P is symmetric
P += T(100.0) * I

Expand All @@ -109,10 +115,6 @@ function DenseDummyQP(
A = fill!(similar(x0, m, n), zero(T))
A[1:m+1:m^2] .= one(T)
A[m+1:m+1:m^2+m] .=-one(T)
# for j in 1:m
# A[j, j] = one(T)
# A[j, j+1] = -one(T)
# end

nnzh = div(n * (n + 1), 2)
hrows = copyto!(similar(x0, Int, nnzh), [i for i in 1:n for j in 1:i])
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