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SciML / ExponentialUtilities.jl

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
https://docs.sciml.ai/ExponentialUtilities/stable/
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exponential!() broken for CuSparseMatrixCSC #181

Open mcarmesin opened 1 week ago

mcarmesin commented 1 week ago

Describe the bug 🐞

Calculating the matrix exponential of a CUDA sparse matrix fails with trowing the exception ArgumentError: Attempt to use a freed reference. or Error: Scalar Indexing is disallowed when selecting ExpMethodGeneric()

Expected behavior

Calculation of the matrix exponential like exp(A)

Minimal Reproducible Example 👇

using ExponentialUtilities
using CUDA
using SparseArrays

A = cu(sprand(50,50,.01))

exponential!(A)
exponential!(A, ExpMethodGeneric())

Error & Stacktrace ⚠️

ERROR: ArgumentError: Attempt to use a freed reference.
Stacktrace:
[1] getindex
    @ ~/.julia/packages/GPUArrays/qt4ax/src/host/abstractarray.jl:70 [inlined]
  [2] unsafe_convert
    @ ~/.julia/packages/CUDA/2kjXI/src/array.jl:434 [inlined]
  [3] unsafe_convert(::Type{CuPtr{Nothing}}, a::CuArray{Int32, 1, CUDA.DeviceMemory})
    @ CUDA ~/.julia/packages/CUDA/2kjXI/src/pointer.jl:62
  [4] macro expansion
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:211 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/libcusparse.jl:5274 [inlined]
  [6] #728
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:35 [inlined]
  [7] retry_reclaim(f::CUDA.CUSPARSE.var"#728#729"{…}, retry_if::CUDA.CUSPARSE.var"#retry_if#1")
    @ CUDA ~/.julia/packages/CUDA/2kjXI/src/memory.jl:434
  [8] check
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/libcusparse.jl:24 [inlined]
  [9] cusparseCreateCsr
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:34 [inlined]
 [10] CUDA.CUSPARSE.CuSparseMatrixDescriptor(A::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, IndexBase::Char; transposed::Bool)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/helpers.jl:189
 [11] CuSparseMatrixDescriptor
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/helpers.jl:179 [inlined]
 [12] gemm!(transa::Char, transb::Char, alpha::Bool, A::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, B::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, beta::Bool, C::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, index::Char, algo::CUDA.CUSPARSE.cusparseSpGEMMAlg_t)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:412
 [13] gemm!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:490 [inlined]
 [14] gemm!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:487 [inlined]
 [15] generic_matmatmul!(C::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, tA::Char, tB::Char, A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, B::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, alpha::Bool, beta::Bool)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/interfaces.jl:167
 [16] generic_matmatmul!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/interfaces.jl:157 [inlined]
 [17] mul!
    @ /lustre/home/software/bwhpc/common/math/julia/1.10.5/share/julia/stdlib/v1.10/LinearAlgebra/src/matmul.jl:263 [inlined]
 [18] mul!
    @ /lustre/home/software/bwhpc/common/math/julia/1.10.5/share/julia/stdlib/v1.10/LinearAlgebra/src/matmul.jl:237 [inlined]
 [19] exp_gen!(cache::Vector{…}, A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, ::Val{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generated/exp_4.jl:21
 [20] exponential!(A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, method::ExpMethodHigham2005, _cache::Tuple{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:101
 [21] exponential!
    @ ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:83 [inlined]
 [22] exponential!(A::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp.jl:15
 [23] top-level scope
    @ ~/CN/test_cu_sparse_exponentiation.jl:7
Some type information was truncated. Use `show(err)` to see complete types.[1] getindex
    @ ~/.julia/packages/GPUArrays/qt4ax/src/host/abstractarray.jl:70 [inlined]
  [2] unsafe_convert
    @ ~/.julia/packages/CUDA/2kjXI/src/array.jl:434 [inlined]
  [3] unsafe_convert(::Type{CuPtr{Nothing}}, a::CuArray{Int32, 1, CUDA.DeviceMemory})
    @ CUDA ~/.julia/packages/CUDA/2kjXI/src/pointer.jl:62
  [4] macro expansion
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:211 [inlined]
  [5] macro expansion
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/libcusparse.jl:5274 [inlined]
  [6] #728
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:35 [inlined]
  [7] retry_reclaim(f::CUDA.CUSPARSE.var"#728#729"{…}, retry_if::CUDA.CUSPARSE.var"#retry_if#1")
    @ CUDA ~/.julia/packages/CUDA/2kjXI/src/memory.jl:434
  [8] check
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/libcusparse.jl:24 [inlined]
  [9] cusparseCreateCsr
    @ ~/.julia/packages/CUDA/2kjXI/lib/utils/call.jl:34 [inlined]
 [10] CUDA.CUSPARSE.CuSparseMatrixDescriptor(A::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, IndexBase::Char; transposed::Bool)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/helpers.jl:189
 [11] CuSparseMatrixDescriptor
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/helpers.jl:179 [inlined]
 [12] gemm!(transa::Char, transb::Char, alpha::Bool, A::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, B::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, beta::Bool, C::CUDA.CUSPARSE.CuSparseMatrixCSR{…}, index::Char, algo::CUDA.CUSPARSE.cusparseSpGEMMAlg_t)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:412
 [13] gemm!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:490 [inlined]
 [14] gemm!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/generic.jl:487 [inlined]
 [15] generic_matmatmul!(C::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, tA::Char, tB::Char, A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, B::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, alpha::Bool, beta::Bool)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/interfaces.jl:167
 [16] generic_matmatmul!
    @ ~/.julia/packages/CUDA/2kjXI/lib/cusparse/interfaces.jl:157 [inlined]
 [17] mul!
    @ /lustre/home/software/bwhpc/common/math/julia/1.10.5/share/julia/stdlib/v1.10/LinearAlgebra/src/matmul.jl:263 [inlined]
 [18] mul!
    @ /lustre/home/software/bwhpc/common/math/julia/1.10.5/share/julia/stdlib/v1.10/LinearAlgebra/src/matmul.jl:237 [inlined]
 [19] exp_gen!(cache::Vector{…}, A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, ::Val{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generated/exp_4.jl:21
 [20] exponential!(A::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, method::ExpMethodHigham2005, _cache::Tuple{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:101
 [21] exponential!
    @ ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:83 [inlined]
 [22] exponential!(A::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp.jl:15
 [23] top-level scope
    @ ~/CN/test_cu_sparse_exponentiation.jl:7
Some type information was truncated. Use `show(err)` to see complete types.
ERROR: Scalar indexing is disallowed.
Invocation of getindex resulted in scalar indexing of a GPU array.
This is typically caused by calling an iterating implementation of a method.
Such implementations *do not* execute on the GPU, but very slowly on the CPU,
and therefore should be avoided.

If you want to allow scalar iteration, use `allowscalar` or `@allowscalar`
to enable scalar iteration globally or for the operations in question.
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:35
  [2] errorscalar(op::String)
    @ GPUArraysCore ~/.julia/packages/GPUArraysCore/GMsgk/src/GPUArraysCore.jl:155
  [3] _assertscalar(op::String, behavior::GPUArraysCore.ScalarIndexing)
    @ GPUArraysCore ~/.julia/packages/GPUArraysCore/GMsgk/src/GPUArraysCore.jl:128
  [4] assertscalar(op::String)
    @ GPUArraysCore ~/.julia/packages/GPUArraysCore/GMsgk/src/GPUArraysCore.jl:116
  [5] getindex(A::CuArray{Int32, 1, CUDA.DeviceMemory}, I::Int64)
    @ GPUArrays ~/.julia/packages/GPUArrays/qt4ax/src/host/indexing.jl:50
  [6] getindex(A::CUDA.CUSPARSE.CuSparseMatrixCSC{Float64, Int32}, i0::Int64, i1::Int64)
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/2kjXI/lib/cusparse/array.jl:387
  [7] _getindex
    @ ./abstractarray.jl:1341 [inlined]
  [8] getindex
    @ ./abstractarray.jl:1291 [inlined]
  [9] exp_generic_core!(y1::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, y2::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, y3::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, x::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, ::Val{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generic.jl:192
 [10] exp_generic!(y1::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, y2::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, y3::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, x::CUDA.CUSPARSE.CuSparseMatrixCSC{…}, s::Int64, ::Val{…})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generic.jl:170
 [11] exp_generic_mutable
    @ ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generic.jl:166 [inlined]
 [12] exponential!(x::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, method::ExpMethodGeneric{Val{13}()}, cache::Nothing)
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generic.jl:136
 [13] exponential!(x::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, method::ExpMethodGeneric{Val{13}()})
    @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generic.jl:128
 [14] top-level scope
    @ ~/CN/test_cu_sparse_exponentiation.jl:9
Some type information was truncated. Use `show(err)` to see complete types.

Environment (please complete the following information):

 [47edcb42] ADTypes v1.9.0
  [79e6a3ab] Adapt v4.1.1
  [7d9fca2a] Arpack v0.5.4
  [6e4b80f9] BenchmarkTools v1.5.0
  [052768ef] CUDA v5.5.2
  [071ae1c0] DiffEqGPU v3.4.1
⌃ [0c46a032] DifferentialEquations v7.13.0
  [5b8099bc] DomainSets v0.7.14
  [d4d017d3] ExponentialUtilities v1.26.1
⌃ [f6369f11] ForwardDiff v0.10.37
⌅ [46192b85] GPUArraysCore v0.1.6
  [40713840] IncompleteLU v0.2.1
  [7a12625a] LinearMaps v3.11.3
  [7ed4a6bd] LinearSolve v2.36.2
  [94925ecb] MethodOfLines v0.11.6
⌃ [961ee093] ModelingToolkit v9.49.0
⌃ [1dea7af3] OrdinaryDiffEq v6.80.1
  [94395366] ParallelStencil v0.13.6
  [f0f68f2c] PlotlyJS v0.18.15
  [91a5bcdd] Plots v1.40.8
  [33c8b6b6] ProgressLogging v0.1.4
  [295af30f] Revise v3.6.2
⌃ [0bca4576] SciMLBase v2.58.1
  [c0aeaf25] SciMLOperators v0.3.12
  [05bca326] SimpleDiffEq v1.11.1
  [ce78b400] SimpleUnPack v1.1.0
  [9f842d2f] SparseConnectivityTracer v0.6.8
⌃ [0c5d862f] Symbolics v6.17.0
  [2f01184e] SparseArrays v1.10.0
[47edcb42] ADTypes v1.9.0
  [621f4979] AbstractFFTs v1.5.0
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  [7d9f7c33] Accessors v0.1.38
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  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
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⌃ [4fba245c] ArrayInterface v7.16.0
  [4c555306] ArrayLayouts v1.10.4
  [bf4720bc] AssetRegistry v0.1.0
  [a9b6321e] Atomix v0.1.0
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  [aae01518] BandedMatrices v1.7.5
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  [e2ed5e7c] Bijections v0.1.9
  [d1d4a3ce] BitFlags v0.1.9
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [ad839575] Blink v0.12.9
  [8e7c35d0] BlockArrays v1.1.1
⌃ [764a87c0] BoundaryValueDiffEq v5.9.1
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
  [00ebfdb7] CSTParser v3.4.3
  [052768ef] CUDA v5.5.2
  [1af6417a] CUDA_Runtime_Discovery v0.3.5
⌅ [d35fcfd7] CellArrays v0.2.2
  [d360d2e6] ChainRulesCore v1.25.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [da1fd8a2] CodeTracking v1.3.6
  [944b1d66] CodecZlib v0.7.6
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⌅ [3da002f7] ColorTypes v0.11.5
⌅ [c3611d14] ColorVectorSpace v0.10.0
⌅ [5ae59095] Colors v0.12.11
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  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.16.0
  [b152e2b5] CompositeTypes v0.1.4
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [f0e56b4a] ConcurrentUtilities v2.4.2
  [187b0558] ConstructionBase v1.5.8
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  [a8cc5b0e] Crayons v4.1.1
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⌅ [459566f4] DiffEqCallbacks v3.8.0
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  [163ba53b] DiffResults v1.1.0
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⌃ [0c46a032] DifferentialEquations v7.13.0
⌃ [a0c0ee7d] DifferentiationInterface v0.6.18
  [8d63f2c5] DispatchDoctor v0.4.17
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⌃ [31c24e10] Distributions v0.25.112
  [ffbed154] DocStringExtensions v0.9.3
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⌃ [06fc5a27] DynamicQuantities v1.2.0
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  [f151be2c] EnzymeCore v0.8.5
  [460bff9d] ExceptionUnwrapping v0.1.10
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⌅ [8913a72c] NonlinearSolve v3.15.1
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  [bac558e1] OrderedCollections v1.6.3
⌃ [1dea7af3] OrdinaryDiffEq v6.80.1
⌃ [a7812802] PDEBase v0.1.15
  [90014a1f] PDMats v0.11.31
  [65ce6f38] PackageExtensionCompat v1.0.2
  [94395366] ParallelStencil v0.13.6
  [d96e819e] Parameters v0.12.3
  [69de0a69] Parsers v2.8.1
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  [b98c9c47] Pipe v1.3.0
  [ccf2f8ad] PlotThemes v3.3.0
⌃ [995b91a9] PlotUtils v1.4.2
  [a03496cd] PlotlyBase v0.8.19
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Julia Version 1.10.5
Commit 6f3fdf7b362 (2024-08-27 14:19 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 96 × Intel(R) Xeon(R) Gold 6252 CPU @ 2.10GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, cascadelake)
Threads: 48 default, 0 interactive, 24 GC (on 96 virtual cores)
ChrisRackauckas commented 1 week ago

I'm not 100% sure what the behavior you'd want here is. The matrix exponential of a sparse matrix is dense unless the matrix is diagonal. Diagonal GPU matrices would use Diagonal{CuArray}, so that wouldn't be this type. Which means that the result here is almost certainly dense. Should this just dispatch to exp(CuArray(A))? It seems having the user actually have to request that isn't the worst thing as a somewhat safety measure, since generally asking for a densifying operation on a sparse matrix is a bad idea.

What the user would almost certainly want to do instead here is exp(At)*v operations, which can be done sparsely. My understanding is that as long as A*v is supported in the array type, which it is here, then it should just work. So then erroring and having people use expv and phiv is the better option.

mcarmesin commented 1 week ago

I agree that densifying would be not a good solution for various reasons. Probably it would be best, to have exponential!() undefined for sparse matrices, like the base exp!(). Btw. exponential!() is also broken for SparseMatrixCSC():

ERROR: pointer_from_objref cannot be used on immutable objects
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] pointer_from_objref
   @ ./pointer.jl:269 [inlined]
 [3] ldiv_for_generated!(C::SparseMatrixCSC{…}, A::SparseMatrixCSC{…}, B::SparseMatrixCSC{…})
   @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:32
 [4] exp_gen!(cache::Vector{SparseMatrixCSC{Float64, Int64}}, A::SparseMatrixCSC{Float64, Int64}, ::Val{4})
   @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_generated/exp_4.jl:63
 [5] exponential!(A::SparseMatrixCSC{Float64, Int64}, method::ExpMethodHigham2005, _cache::Tuple{Vector{…}, SparseVector{…}})
   @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:101
 [6] exponential!(A::SparseMatrixCSC{Float64, Int64}, method::ExpMethodHigham2005)
   @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp_noalloc.jl:83
 [7] exponential!(A::SparseMatrixCSC{Float64, Int64})
   @ ExponentialUtilities ~/.julia/packages/ExponentialUtilities/xLH9y/src/exp.jl:15
 [8] top-level scope
   @ ~/CN/test_cu_sparse_exponentiation.jl:10
ChrisRackauckas commented 1 week ago

Btw. exponential!() is also broken for SparseMatrixCSC():

Yeah we should probably just throw an error on <: AbstractSparseMatrix saying

exp(A) on a sparse matrix is generally dense. This operation is not allowed with exponential. If you wished to compute exp(At)*v, see expv. Otherwise to override this error, densify the matrix before calling, i.e. exponential(Matrix(A))