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Adding sparse adjoint results in kernel error #1591

Closed jamblejoe closed 2 years ago

jamblejoe commented 2 years ago

Describe the bug

Adding a sparse matrix adjoint to a dense or sparse matrix results in kernel errors.

To reproduce

The Minimal Working Example (MWE) for this bug:

A = cu(sprand(10,10,0.1))
A+A'

results in 

ERROR: a exception was thrown during kernel execution.
       Run Julia on debug level 2 for device stack traces.
ERROR: KernelException: exception thrown during kernel execution on device NVIDIA GeForce RTX 2080 SUPER
Stacktrace:
 [1] check_exceptions()
   @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/exceptions.jl:34
 [2] nonblocking_synchronize
   @ ~/.julia/packages/CUDA/DfvRa/lib/cudadrv/context.jl:331 [inlined]
 [3] device_synchronize()
   @ CUDA ~/.julia/packages/CUDA/DfvRa/lib/cudadrv/context.jl:319
 [4] top-level scope
   @ ~/.julia/packages/CUDA/DfvRa/src/initialization.jl:54

caused by: InvalidIRError: compiling kernel #sparse_to_dense_broadcast_kernel(Type{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}, typeof(+), CuDeviceMatrix{Float32, 1}, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}}) resulted in invalid LLVM IR
Reason: unsupported call to an unknown function (call to jl_f_getfield)
Stacktrace:
 [1] getindex
   @ ./tuple.jl:29
 [2] #2341
   @ ~/.julia/packages/CUDA/DfvRa/lib/cusparse/broadcast.jl:451
 [3] ntuple
   @ ./ntuple.jl:49
 [4] sparse_to_dense_broadcast_kernel
   @ ~/.julia/packages/CUDA/DfvRa/lib/cusparse/broadcast.jl:450
Hint: catch this exception as `err` and call `code_typed(err; interactive = true)` to introspect the erronous code with Cthulhu.jl
Stacktrace:
  [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(CUDA.CUSPARSE.sparse_to_dense_broadcast_kernel), Tuple{Type{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}, typeof(+), CuDeviceMatrix{Float32, 1}, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}}}}}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/07qaN/src/validation.jl:141
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/07qaN/src/driver.jl:418 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/4yHI4/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/07qaN/src/driver.jl:416 [inlined]
  [5] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/07qaN/src/utils.jl:68
  [6] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:354
  [7] #224
    @ ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:347 [inlined]
  [8] JuliaContext(f::CUDA.var"#224#225"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{typeof(CUDA.CUSPARSE.sparse_to_dense_broadcast_kernel), Tuple{Type{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}, typeof(+), CuDeviceMatrix{Float32, 1}, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}}}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/07qaN/src/driver.jl:76
  [9] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:346
 [10] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/07qaN/src/cache.jl:90
 [11] cufunction(f::typeof(CUDA.CUSPARSE.sparse_to_dense_broadcast_kernel), tt::Type{Tuple{Type{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}, typeof(+), CuDeviceMatrix{Float32, 1}, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}}}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:299
 [12] cufunction(f::typeof(CUDA.CUSPARSE.sparse_to_dense_broadcast_kernel), tt::Type{Tuple{Type{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}, typeof(+), CuDeviceMatrix{Float32, 1}, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseDeviceMatrixCSC{Float32, Int32, 1}}}})
    @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:292
 [13] macro expansion
    @ ~/.julia/packages/CUDA/DfvRa/src/compiler/execution.jl:102 [inlined]
 [14] copy(bc::Base.Broadcast.Broadcasted{CUDA.CUSPARSE.CuSparseMatStyle, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}, typeof(+), Tuple{CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}}}})
    @ CUDA.CUSPARSE ~/.julia/packages/CUDA/DfvRa/lib/cusparse/broadcast.jl:594
 [15] materialize
    @ ./broadcast.jl:860 [inlined]
 [16] broadcast_preserving_zero_d
    @ ./broadcast.jl:849 [inlined]
 [17] +(A::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}, B::LinearAlgebra.Adjoint{Float32, CUDA.CUSPARSE.CuSparseMatrixCSC{Float32, Int32}})
    @ Base ./arraymath.jl:8
 [18] top-level scope
    @ REPL[75]:1
 [19] top-level scope
    @ ~/.julia/packages/CUDA/DfvRa/src/initialization.jl:52

julia>

or 

B=CUDA.rand(10,10)
B+A'

in

ERROR: KernelException: exception thrown during kernel execution on device NVIDIA GeForce RTX 2080 SUPER
Stacktrace:
 [1] check_exceptions()
   @ CUDA ~/.julia/packages/CUDA/DfvRa/src/compiler/exceptions.jl:34
 [2] nonblocking_synchronize
   @ ~/.julia/packages/CUDA/DfvRa/lib/cudadrv/context.jl:331 [inlined]
 [3] device_synchronize()
   @ CUDA ~/.julia/packages/CUDA/DfvRa/lib/cudadrv/context.jl:319
 [4] top-level scope
   @ ~/.julia/packages/CUDA/DfvRa/src/initialization.jl:54
Manifest.toml

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["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96" uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" version = "0.9.10+4" [[deps.Xorg_libXtst_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll", "Xorg_libXi_jll"] git-tree-sha1 = "0c0a60851f44add2a64069ddf213e941c30ed93c" uuid = "b6f176f1-7aea-5357-ad67-1d3e565ea1c6" version = "1.2.3+4" [[deps.Xorg_libpthread_stubs_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb" uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" version = "0.1.0+3" [[deps.Xorg_libxcb_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6" uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" version = "1.13.0+3" [[deps.Xorg_libxkbfile_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2" uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a" version = "1.1.0+4" [[deps.Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" [[deps.Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" [[deps.Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.12+3" [[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.2+0" [[deps.adwaita_icon_theme_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "hicolor_icon_theme_jll"] git-tree-sha1 = "37c9a36ccb876e02876c8a654f1b2e8c1b443a78" uuid = "b437f822-2cd6-5e08-a15c-8bac984d38ee" version = "3.33.92+5" [[deps.at_spi2_atk_jll]] deps = ["ATK_jll", "Artifacts", "JLLWrappers", "Libdl", "Pkg", "XML2_jll", "Xorg_libX11_jll", "at_spi2_core_jll"] git-tree-sha1 = "f16ae690aca4761f33d2cb338ee9899e541f5eae" uuid = "de012916-1e3f-58c2-8f29-df3ef51d412d" version = "2.34.1+4" [[deps.at_spi2_core_jll]] deps = ["Artifacts", "Dbus_jll", "Glib_jll", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXtst_jll"] git-tree-sha1 = "d2d540cd145f2b2933614649c029d222fe125188" uuid = "0fc3237b-ac94-5853-b45c-d43d59a06200" version = "2.34.0+4" [[deps.gdk_pixbuf_jll]] deps = ["Artifacts", "Glib_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pkg", "Xorg_libX11_jll", "libpng_jll"] git-tree-sha1 = "e9190f9fb03f9c3b15b9fb0c380b0d57a3c8ea39" uuid = "da03df04-f53b-5353-a52f-6a8b0620ced0" version = "2.42.8+0" [[deps.hicolor_icon_theme_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "b458a6f6fc2b1a8ca74ed63852e4eaf43fb9f5ea" uuid = "059c91fe-1bad-52ad-bddd-f7b78713c282" version = "0.17.0+3" [[deps.iso_codes_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "51559b9071db7e363047a34f658d495843ccd35c" uuid = "bf975903-5238-5d20-8243-bc370bc1e7e5" version = "4.11.0+0" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.1.1+0" [[deps.libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.48.0+0" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" version = "17.4.0+0" [[deps.xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" version = "1.4.1+0" ```

Expected behavior

No kernel error when adding adjoints of sparse matrices.

Version info

Details on Julia:

julia> versioninfo()
Julia Version 1.8.0
Commit 5544a0fab76 (2022-08-17 13:38 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 48 × AMD Ryzen Threadripper 3960X 24-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, znver2)
  Threads: 1 on 48 virtual cores

Details on CUDA:

julia> CUDA.versioninfo()
CUDA toolkit 11.7, artifact installation
NVIDIA driver 512.59.0, for CUDA 11.6
CUDA driver 11.6

Libraries:
- CUBLAS: 11.10.1
- CURAND: 10.2.10
- CUFFT: 10.7.2
- CUSOLVER: 11.3.5
- CUSPARSE: 11.7.3
- CUPTI: 17.0.0
- NVML: 11.0.0+510.68.1
- CUDNN: 8.30.2 (for CUDA 11.5.0)
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)

Toolchain:
- Julia: 1.8.0
- LLVM: 13.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0, 7.1, 7.2
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86

1 device:
  0: NVIDIA GeForce RTX 2080 SUPER (sm_75, 6.504 GiB / 8.000 GiB available)
albertomercurio commented 2 years ago

For the case of a sparse CSR matrix, I solved by implementing this function by myself

using CUDA
CUDA.allowscalar(false)
import LinearAlgebra

function LinearAlgebra.adjoint(A::CUSPARSE.CuSparseMatrixCSR)
    cscA = CUSPARSE.CuSparseMatrixCSC(conj(A))
    CUSPARSE.CuSparseMatrixCSR(cscA.colPtr, cscA.rowVal, cscA.nzVal, size(cscA))
end

a = sprand(ComplexF32, 1000, 1000, 0.01)
a = CUSPARSE.CuSparseMatrixCSR(a)
a'

1000×1000 CUDA.CUSPARSE.CuSparseMatrixCSR{ComplexF32, Int32} with 987 stored entries:
⢦⢷⣷⣿⡷⣷⣟⡶⣿⡾⣿⡾
⡼⡯⣣⣽⡷⣯⢿⣙⢷⣾⢿⣿
⣹⢯⠾⣟⣻⣿⣾⣽⣻⣱⣿⣿
⣨⡿⣟⣿⣿⢿⣽⢟⡷⣵⣾⣳
⣿⣇⣥⡷⡯⣻⢽⣻⣯⣿⢳⣳
⢺⣾⢿⠽⢟⣿⡿⡸⡿⣾⣾⡿
jamblejoe commented 2 years ago

@albertomercurio thank you for the workaround!

In the future it should be supported by the CUDA.jl, as it is in e.g. LinearAlgebra.

maleadt commented 2 years ago

Will be fixed by https://github.com/JuliaGPU/CUDA.jl/pull/1603