KernelError: passing and using non-bitstype argument

jquetzalcoatl commented 4 years ago

Hi! I'm not sure if this issue is, in essence, the same as this one: https://github.com/SciML/DiffEqFlux.jl/issues/378

I previously opened an issue at CUDA.jl https://github.com/JuliaGPU/CUDA.jl/issues/372 but it seems the issue is being generated at some point in DiffEqFlux.

The problem is I'm not broadcasting properly, as was pointed out by @maleadt (Thank you).

In what follows, I'll explain what I'm doing and show the stack trace. Thank you.

I'm training a matrix of parameters p by using a MSE.

The values of the elements in pinit are in (0.0,1.0]

The error is raised with: DiffEqFlux.sciml_train(loss, pinit, ADAM(0.1), maxiters=10) where: function loss(p) tmp_prob = remake(prob, p=p) tmp_sol = solve(tmp_prob,ROCK2(), save_everystep=false,save_start=false) ϵ = CuArray(reshape(tmp_sol[:,:,end],:)) sum(abs2, ϵ) end (the function loss(p) does not generate any error).

The stack trace I'm getting is:

`GPU compilation of kernel broadcast(CuArrays.CuKernelContext, CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global}, Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}) failed
KernelError: passing and using non-bitstype argument

Argument 4 to your kernel function is of type Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}, which is not isbits:
  .args is of type Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}} which is not isbits.
    .1 is of type Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}} which is not isbits.
      .x is of type Array{Float32,1} which is not isbits.

check_invocation(::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDAnative.CUDACompilerParams}, ::LLVM.Function) at validation.jl:68
macro expansion at driver.jl:184 [inlined]
macro expansion at TimerOutput.jl:206 [inlined]
codegen(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDAnative.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, strict::Bool) at driver.jl:183
codegen at driver.jl:60 [inlined]
compile(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDAnative.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, strict::Bool) at driver.jl:36
compile at driver.jl:32 [inlined]
_cufunction(::GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at execution.jl:308
_cufunction at execution.jl:302 [inlined]
#77 at cache.jl:21 [inlined]
get!(::GPUCompiler.var"#77#78"{Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}},typeof(CUDAnative._cufunction),GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}}, ::Dict{UInt64,Any}, ::UInt64) at dict.jl:450
macro expansion at lock.jl:183 [inlined]
check_cache(::typeof(CUDAnative._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at cache.jl:19
(::GPUCompiler.var"#check_cache##kw")(::NamedTuple{(),Tuple{}}, ::typeof(GPUCompiler.check_cache), ::Function, ::GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}, ::UInt64) at cache.jl:11
+ at int.jl:86 [inlined]
hash_64_64 at hashing.jl:35 [inlined]
hash_uint64 at hashing.jl:62 [inlined]
hx at float.jl:568 [inlined]
hash at float.jl:571 [inlined]
cached_compilation(::typeof(CUDAnative._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at cache.jl:0
cached_compilation(::Function, ::GPUCompiler.FunctionSpec{GPUArrays.var"#20#21",Tuple{CuArrays.CuKernelContext,CUDAnative.CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}}, ::UInt64) at cache.jl:33
cufunction(::Function, ::Type{T} where T; name::String, kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at execution.jl:296
cufunction at execution.jl:291 [inlined]
macro expansion at execution.jl:108 [inlined]
gpu_call(::CuArrays.CuArrayBackend, ::Function, ::Tuple{CuArray{Float32,1,CuArray{Float32,1,Nothing}},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{Float32,1},Tuple{Bool},Tuple{Int64}}}}}, ::Int64; name::String) at gpuarrays.jl:32
gpu_call at gpuarrays.jl:23 [inlined]
#gpu_call#1 at execution.jl:61 [inlined]
gpu_call at execution.jl:4... `

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"Random", "Requires", "SpecialFunctions", "Statistics", "Test"] git-tree-sha1 = "d8173d93a7b4e74d7ab898874f2a4ca04f0cae1a" uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" version = "0.2.8" [[TranscodingStreams]] deps = ["Random", "Test"] git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" version = "0.9.5" [[TreeViews]] deps = ["Test"] git-tree-sha1 = "8d0d7a3fe2f30d6a7f833a5f19f7c7a5b396eae6" uuid = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7" version = "0.3.0" [[URIParser]] deps = ["Unicode"] git-tree-sha1 = "53a9f49546b8d2dd2e688d216421d050c9a31d0d" uuid = "30578b45-9adc-5946-b283-645ec420af67" version = "0.4.1" [[UUIDs]] deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [[UnPack]] git-tree-sha1 = "d4bfa022cd30df012700cf380af2141961bb3bfb" uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed" version = "1.0.1" [[Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" [[Unitful]] deps = ["ConstructionBase", "LinearAlgebra", "Random"] git-tree-sha1 = "a061dada333813818aa7454f93c63a5cab6ea981" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" version = "1.3.0" [[VectorizationBase]] deps = ["CpuId", "Libdl", "LinearAlgebra"] git-tree-sha1 = "09a4d4896aac75199b332eb26a8d0a9e4655e34a" uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f" version = "0.12.30" [[VersionParsing]] git-tree-sha1 = "80229be1f670524750d905f8fc8148e5a8c4537f" uuid = "81def892-9a0e-5fdd-b105-ffc91e053289" version = "1.2.0" [[VertexSafeGraphs]] deps = ["LightGraphs"] git-tree-sha1 = "b9b450c99a3ca1cc1c6836f560d8d887bcbe356e" uuid = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f" version = "0.1.2" [[WebIO]] deps = ["AssetRegistry", "Base64", "Distributed", "FunctionalCollections", "JSON", "Logging", "Observables", "Pkg", "Random", "Requires", "Sockets", "UUIDs", "WebSockets", "Widgets"] git-tree-sha1 = "add6b526464ab77e9decef9d2dafa46c54af6617" uuid = "0f1e0344-ec1d-5b48-a673-e5cf874b6c29" version = "0.8.14" [[WebSockets]] deps = ["Base64", "Dates", "Distributed", "HTTP", "Logging", "Random", "Sockets", "Test"] git-tree-sha1 = "13f763d38c7a05688938808b49cb29b18b60c8c8" uuid = "104b5d7c-a370-577a-8038-80a2059c5097" version = "1.5.2" [[Widgets]] deps = ["Colors", "Dates", "Observables", "OrderedCollections"] git-tree-sha1 = "fc0feda91b3fef7fe6948ee09bb628f882b49ca4" uuid = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62" version = "0.6.2" [[WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] git-tree-sha1 = "28ffe06d28b1ba8fdb2f36ec7bb079fac81bac0d" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" version = "0.5.2" [[ZMQ]] deps = ["FileWatching", "Sockets", "ZeroMQ_jll"] git-tree-sha1 = "fc68e8a3719166950a0f3e390a14c7302c48f8de" uuid = "c2297ded-f4af-51ae-bb23-16f91089e4e1" version = "1.2.1" [[ZeroMQ_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "733352667c60ce39dfd3017db9b798b288c87417" uuid = "8f1865be-045e-5c20-9c9f-bfbfb0764568" version = "4.3.2+4" [[ZipFile]] deps = ["Libdl", "Printf", "Zlib_jll"] git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f" uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" version = "0.9.2" [[Zlib_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "d5bba6485811931e4b8958e2d7ca3738273ac468" uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.11+15" [[Zstd_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "b25b0fb10176c42e9a5a20e1f40d570ac0288d4e" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.4.5+0" [[Zygote]] deps = ["AbstractFFTs", "ArrayLayouts", "ChainRules", "FillArrays", "ForwardDiff", "Future", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "Random", "Requires", "Statistics", "ZygoteRules"] git-tree-sha1 = "2e2c82549fb0414df10469082fd001e2ede8547c" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" version = "0.4.22" [[ZygoteRules]] deps = ["MacroTools"] git-tree-sha1 = "b3b4882cc9accf6731a08cc39543fbc6b669dca8" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.0" [[libass_jll]] deps = ["Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "f02d0db58888592e98c5f4953cef620ce9274eee" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.14.0+3" [[libfdk_aac_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "e17b4513993b4413d31cffd1b36a63625ebbc3d3" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "0.1.6+3" [[libpng_jll]] deps = ["Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "30518225ee32d63d034756bb5d02d782bffaf0e5" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.37+4" [[libvorbis_jll]] deps = ["Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "8014e1c1033009edcfe820ec25877a9f1862ba4c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.6+5" [[x264_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "e496625b900df1b02ab0e02fad316b77446616ef" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2020.7.14+1" [[x265_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "ac7d44fa1639a780d0ae79ca1a5a7f4181131825" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.0.0+2" ```

Version info

Details on Julia:

# please post the output of:
Julia Version 1.5.0
Commit 96786e22cc (2020-08-01 23:44 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
  JULIA_LOAD_PATH = :/u/jtoledom/modules/

# please post the output of:
Using Juliav1.5

ChrisRackauckas commented 4 years ago

What's the code like? How come it's trying to broadcast a CPU array? Without seeing the example I can't really help.

jquetzalcoatl commented 4 years ago

Here's what I'm doing (actually, I'm pretty much adapting your tutorial in http://www.stochasticlifestyle.com/solving-systems-stochastic-pdes-using-gpus-julia/ and merging it with your (latest?) tutorial on SciML for JuliaCon 20):

function f3(du,u,p,t)
  BPath = p
  A = u
  dA = du
  mul!(gMyA,gMy,A)
  mul!(gAMx,A,gMx)
  @. gDA = Dd*(gMyA + gAMx)
  @. dA = gDA + α1*BPatch - r1*A*BPatch
end

α₁ = get_input([100,100]; uncen=[50,50]) # This is a CuArray{Float32,2} of 0's and 1's (mostly zeroes)
gMx, gMy = gen_op(size(α₁,1))        # This generates the discrete laplacian as in CR's tutorial
r1 = 2.0
Dd = 2.0
α2 = 0.29
r2 = 0.1

gMyA = CuArray(Float32.(zeros(size(Mx))))
gAMx = CuArray(Float32.(zeros(size(Mx))))
gDA = CuArray(Float32.(zeros(size(Mx))))

b_patch = CuArray(Float32.(rand(Distributions.Binomial(1,0.9),size(α₁,1),size(α₁,1)))) .* 0.999 .+ 0.001
p = b_patch     # parameters

u0 = CuArray(Float32.(zeros(size(α₁,1),size(α₁,1)) .+ 0.00001)) # Initial condition

prob = ODEProblem(f3,u0,(0.0,5.0), p)

@time sol2 = solve(prob,ROCK2(),progress=true,save_everystep=false,save_start=true)

function loss(p)
    tmp_prob = remake(prob, p=p)
    tmp_sol = solve(tmp_prob,ROCK2(), save_everystep=false,save_start=false)
    ϵ = CuArray(reshape(tmp_sol[:,:,end],:))
    sum(abs2, ϵ)
end

pinit = CuArray(Float32.(rand(Distributions.Binomial(1,0.9),size(α₁,1),size(α₁,1)) .* 0.999 .+ 0.001))
loss(pinit)
res = DiffEqFlux.sciml_train(loss, pinit, ADAM(0.1), maxiters=10)

where:

function get_input(s;uncen = [0,0], path = "/u/jtoledom/PDE/Img/10_manual1_Sket.tif")
    img = load(path)
    center = collect(half_int.(size(img))) + uncen
    if s == [0,0]
        new_s = minimum(size(α₁))
        img_crop = img[1:new_s,1:new_s]
    else
        Δ = half_int.(s)
        img_crop = img[center[1]-Δ[1]:center[1]+Δ[1]-1, center[2]-Δ[2]:center[2]+Δ[2]-1]
    end
    return CuArray(Float32.(img_crop))
end

function gen_op(s)
    N=s
    Mx = Tridiagonal([1.0 for i in 1:N-1],[-2.0 for i in 1:N],[1.0 for i in 1:N-1])
    My = copy(Mx)
    Mx[2,1] = 2.0
    Mx[end-1,end] = 2.0
    My[1,2] = 2.0
    My[end,end-1] = 2.0
    return CuArray(Float32.(Mx)), CuArray(Float32.(My))
end

Thank you!

ChrisRackauckas commented 4 years ago

using OrdinaryDiffEq, CUDA, DiffEqFlux, LinearAlgebra, Flux, DiffEqSensitivity, Distributions

function get_input(s;uncen = [0,0], path = "/u/jtoledom/PDE/Img/10_manual1_Sket.tif")
    center = uncen
    if s == [0,0]
        new_s = minimum(size(α₁))
        img_crop = img[1:new_s,1:new_s]
    else
        Δ = Int.(s)
        img_crop = ones(length(center[1]-Δ[1]:center[1]+Δ[1]-1), length(center[2]-Δ[2]:center[2]+Δ[2]-1))
    end
    return CuArray(Float32.(img_crop))
end

function gen_op(s)
    N=s
    Mx = Tridiagonal([1.0 for i in 1:N-1],[-2.0 for i in 1:N],[1.0 for i in 1:N-1])
    My = copy(Mx)
    Mx[2,1] = 2.0
    Mx[end-1,end] = 2.0
    My[1,2] = 2.0
    My[end,end-1] = 2.0
    return CuArray(Float32.(Mx)), CuArray(Float32.(My))
end

function f3(u,p,t)
  BPath = p
  A = u
  gMyA =  gMy * A
  gAMx = A * gMx
  gDA =  @. Dd*(gMyA + gAMx)
  dA = @. gDA + α1*BPatch - r1*A*BPatch
  return dA
end

α₁ = get_input([100,100]; uncen=[50,50]) # This is a CuArray{Float32,2} of 0's and 1's (mostly zeroes)
gMx, gMy = gen_op(size(α₁,1))        # This generates the discrete laplacian as in CR's tutorial
r1 = 2.0
Dd = 2.0
α1 = 0.29
α2 = 0.29
r2 = 0.1
BPatch = 0.1

gMyA = CuArray(Float32.(zeros(size(gMx))))
gAMx = CuArray(Float32.(zeros(size(gMx))))
gDA = CuArray(Float32.(zeros(size(gMx))))

b_patch = CuArray(Float32.(rand(Distributions.Binomial(1,0.9),size(α₁,1),size(α₁,1)))) .* 0.999 .+ 0.001
p = b_patch     # parameters

u0 = CuArray(Float32.(zeros(size(α₁,1),size(α₁,1)) .+ 0.00001)) # Initial condition

prob = ODEProblem{false}(f3,u0,(0.0,5.0), p)

@time sol2 = solve(prob,ROCK2(),progress=true,save_everystep=false,save_start=true)

CUDA.allowscalar(false)

function loss(p)
    tmp_prob = remake(prob, p=p)
    tmp_sol = solve(tmp_prob,ROCK2(), save_everystep=false,save_start=false, sensealg = InterpolatingAdjoint(autojacvec=ZygoteVJP()))
    ϵ = tmp_sol[end]
    sum(abs2, ϵ)
end

pinit = CuArray(Float32.(rand(Distributions.Binomial(1,0.9),size(α₁,1),size(α₁,1)) .* 0.999 .+ 0.001))
loss(pinit)
res = DiffEqFlux.sciml_train(loss, pinit, ADAM(0.1), maxiters=10)

works with https://github.com/SciML/DiffEqSensitivity.jl/pull/326 . I didn't have the image so I just made up some parameter values, but that should put you on the right track. Let me know if you have any questions.

jquetzalcoatl commented 4 years ago

Great! Thanks!

One question, though: Are you using Julia v1.5 w/ CUDA.jl? Because I thought currently it wasn't supported

ChrisRackauckas commented 4 years ago

Yes and it is. What made you gather that it's not supported?

jquetzalcoatl commented 4 years ago

That's a really good question which I'll try my best in answering

So when I do a pkg> add CUDA, I get CUDA v0.1.0 and then:

julia> using CUDA
[ Info: Precompiling CUDA [052768ef-5323-5732-b1bb-66c8b64840ba]
ERROR: LoadError: LoadError: LoadError: UndefVarError: AddrSpacePtr not defined
Stacktrace:
 [1] getproperty(::Module, ::Symbol) at ./Base.jl:26
 [2] top-level scope at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/device/cuda/wmma.jl:52
 [3] include(::Function, ::Module, ::String) at ./Base.jl:380
 [4] include at ./Base.jl:368 [inlined]
 [5] include(::String) at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/CUDA.jl:1
 [6] top-level scope at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/device/cuda.jl:15
 [7] include(::Function, ::Module, ::String) at ./Base.jl:380
 [8] include at ./Base.jl:368 [inlined]
 [9] include(::String) at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/CUDA.jl:1
 [10] top-level scope at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/CUDA.jl:39
 [11] include(::Function, ::Module, ::String) at ./Base.jl:380
 [12] include(::Module, ::String) at ./Base.jl:368
 [13] top-level scope at none:2
 [14] eval at ./boot.jl:331 [inlined]
 [15] eval(::Expr) at ./client.jl:467
 [16] top-level scope at ./none:3
in expression starting at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/device/cuda/wmma.jl:52
in expression starting at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/device/cuda.jl:14
in expression starting at /u/jtoledom/.julia/packages/CUDA/5t6R9/src/CUDA.jl:39
ERROR: Failed to precompile CUDA [052768ef-5323-5732-b1bb-66c8b64840ba] to /u/jtoledom/.julia/compiled/v1.5/CUDA/oWw5k_gfi8z.ji.
Stacktrace:
 [1] error(::String) at ./error.jl:33
 [2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1290
 [3] _require(::Base.PkgId) at ./loading.jl:1030
 [4] require(::Base.PkgId) at ./loading.jl:928
 [5] require(::Module, ::Symbol) at ./loading.jl:923

And I googled the error and found this thread

Thanks again!

ChrisRackauckas commented 4 years ago

What version of CUDA.jl do you have? >1.0? Is there a package in your tomls that's holding it back?

adinhobl commented 4 years ago

I was just looking for a solution to this too:

julia> using CUDA
[ Info: Precompiling CUDA [052768ef-5323-5732-b1bb-66c8b64840ba]
ERROR: LoadError: LoadError: LoadError: UndefVarError: AddrSpacePtr not defined
Stacktrace:
 [1] getproperty(::Module, ::Symbol) at ./Base.jl:26
 [2] top-level scope at /home/andrew/.julia/packages/CUDA/5t6R9/src/device/cuda/wmma.jl:52
 [3] include(::Function, ::Module, ::String) at ./Base.jl:380
 [4] include at ./Base.jl:368 [inlined]
 [5] include(::String) at /home/andrew/.julia/packages/CUDA/5t6R9/src/CUDA.jl:1
 [6] top-level scope at /home/andrew/.julia/packages/CUDA/5t6R9/src/device/cuda.jl:15
 [7] include(::Function, ::Module, ::String) at ./Base.jl:380
 [8] include at ./Base.jl:368 [inlined]
 [9] include(::String) at /home/andrew/.julia/packages/CUDA/5t6R9/src/CUDA.jl:1
 [10] top-level scope at /home/andrew/.julia/packages/CUDA/5t6R9/src/CUDA.jl:39
 [11] include(::Function, ::Module, ::String) at ./Base.jl:380
 [12] include(::Module, ::String) at ./Base.jl:368
 [13] top-level scope at none:2
 [14] eval at ./boot.jl:331 [inlined]
 [15] eval(::Expr) at ./client.jl:467
 [16] top-level scope at ./none:3
in expression starting at /home/andrew/.julia/packages/CUDA/5t6R9/src/device/cuda/wmma.jl:52
in expression starting at /home/andrew/.julia/packages/CUDA/5t6R9/src/device/cuda.jl:14
in expression starting at /home/andrew/.julia/packages/CUDA/5t6R9/src/CUDA.jl:39
ERROR: Failed to precompile CUDA [052768ef-5323-5732-b1bb-66c8b64840ba] to /home/andrew/.julia/compiled/v1.5/CUDA/oWw5k_jPOtW.ji.
Stacktrace:
 [1] error(::String) at ./error.jl:33
 [2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1290
 [3] _require(::Base.PkgId) at ./loading.jl:1030
 [4] require(::Base.PkgId) at ./loading.jl:928
 [5] require(::Module, ::Symbol) at ./loading.jl:923
 [6] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1088

And

(@v1.5) pkg> st
Status `~/.julia/environments/v1.5/Project.toml`
  [052768ef] CUDA v0.1.0
  [be33ccc6] CUDAnative v3.0.4
  [3a865a2d] CuArrays v2.2.2
  [587475ba] Flux v0.10.4
  [7073ff75] IJulia v1.21.3
  [916415d5] Images v0.22.4
  [dbeba491] Metalhead v0.5.1
  [e88e6eb3] Zygote v0.4.22

ChrisRackauckas commented 4 years ago

What happens if you remove CuArrays?

ChrisRackauckas commented 4 years ago

CuArrays and CUDAnative are probably putting an upper bound on Adapt.jl, and so you might need to remove them to get the latest Flux, Zygote, and CUDA versions.

adinhobl commented 4 years ago

It looks like the same error.

(@v1.5) pkg> remove CuArrays
Updating `~/.julia/environments/v1.5/Project.toml`
  [3a865a2d] - CuArrays v2.2.2
No Changes to `~/.julia/environments/v1.5/Manifest.toml`

julia> using CUDA
[ Info: Precompiling CUDA [052768ef-5323-5732-b1bb-66c8b64840ba]
ERROR: LoadError: LoadError: LoadError: UndefVarError: AddrSpacePtr not defined
Stacktrace:
 [1] getproperty(::Module, ::Symbol) at ./Base.jl:26
 [2] top-level scope at /home/andrew/.julia/packages/CUDA/5t6R9/src/device/cuda/wmma.jl:52
...

It looks like CUDAnative fails the same way.

ChrisRackauckas commented 4 years ago

Make sure you ]up afterwards to try and get the newer versions now that the bounds are lifted.

adinhobl commented 4 years ago

(@v1.5) pkg> remove CUDAnative
Updating `~/.julia/environments/v1.5/Project.toml`
  [be33ccc6] - CUDAnative v3.0.4
No Changes to `~/.julia/environments/v1.5/Manifest.toml`

(@v1.5) pkg> st
Status `~/.julia/environments/v1.5/Project.toml`
  [052768ef] CUDA v0.1.0
  [587475ba] Flux v0.10.4
  [7073ff75] IJulia v1.21.3
  [916415d5] Images v0.22.4
  [dbeba491] Metalhead v0.5.1
  [e88e6eb3] Zygote v0.4.22

(@v1.5) pkg> up
   Updating registry at `~/.julia/registries/General`
   Updating git-repo `https://github.com/JuliaRegistries/General.git`
  Installed GPUArrays ─ v5.1.0
Updating `~/.julia/environments/v1.5/Project.toml`
  [052768ef] ↑ CUDA v0.1.0 ⇒ v1.2.1
  [587475ba] ↑ Flux v0.10.4 ⇒ v0.11.1
  [e88e6eb3] ↑ Zygote v0.4.22 ⇒ v0.5.4
Updating `~/.julia/environments/v1.5/Manifest.toml`
  [79e6a3ab] ↑ Adapt v1.1.0 ⇒ v2.0.2
  [fa961155] ↑ CEnum v0.2.0 ⇒ v0.4.1
  [052768ef] ↑ CUDA v0.1.0 ⇒ v1.2.1
  [3895d2a7] - CUDAapi v4.0.0
  [c5f51814] - CUDAdrv v6.3.0
  [be33ccc6] - CUDAnative v3.0.4
  [082447d4] ↑ ChainRules v0.6.5 ⇒ v0.7.13
  [d360d2e6] ↑ ChainRulesCore v0.8.1 ⇒ v0.9.5
  [da1fd8a2] - CodeTracking v1.0.0
  [adafc99b] + CpuId v0.2.2
  [f68482b8] - Cthulhu v1.2.0
  [3a865a2d] - CuArrays v2.2.2
  [ffbed154] + DocStringExtensions v0.8.2
  [587475ba] ↑ Flux v0.10.4 ⇒ v0.11.1
  [1eca21be] - FoldingTrees v1.0.1
  [d9f16b24] + Functors v0.1.0
  [0c68f7d7] ↑ GPUArrays v3.4.1 ⇒ v5.1.0
  [61eb1bfa] ↑ GPUCompiler v0.3.0 ⇒ v0.5.5
  [929cbde3] ↑ LLVM v1.7.0 ⇒ v2.0.0
  [bdcacae8] + LoopVectorization v0.8.24
  [872c559c] ↑ NNlib v0.6.6 ⇒ v0.7.4
  [21efa798] + SIMDPirates v0.8.24
  [476501e8] + SLEEFPirates v0.5.5
  [3d5dd08c] + VectorizationBase v0.12.32
  [e88e6eb3] ↑ Zygote v0.4.22 ⇒ v0.5.4

(@v1.5) pkg> add CuArrays
  Resolving package versions...
Updating `~/.julia/environments/v1.5/Project.toml`
  [052768ef] ↓ CUDA v1.2.1 ⇒ v0.1.0
  [3a865a2d] + CuArrays v2.2.2
  [587475ba] ↓ Flux v0.11.1 ⇒ v0.10.4
  [e88e6eb3] ↓ Zygote v0.5.4 ⇒ v0.4.22
Updating `~/.julia/environments/v1.5/Manifest.toml`
  [79e6a3ab] ↓ Adapt v2.0.2 ⇒ v1.1.0
  [fa961155] ↓ CEnum v0.4.1 ⇒ v0.2.0
  [052768ef] ↓ CUDA v1.2.1 ⇒ v0.1.0
  [3895d2a7] + CUDAapi v4.0.0
  [c5f51814] + CUDAdrv v6.3.0
  [be33ccc6] + CUDAnative v3.0.4
  [082447d4] ↓ ChainRules v0.7.13 ⇒ v0.6.5
  [d360d2e6] ↓ ChainRulesCore v0.9.5 ⇒ v0.8.1
  [da1fd8a2] + CodeTracking v1.0.0
  [adafc99b] - CpuId v0.2.2
  [f68482b8] + Cthulhu v1.2.0
  [3a865a2d] + CuArrays v2.2.2
  [ffbed154] - DocStringExtensions v0.8.2
  [587475ba] ↓ Flux v0.11.1 ⇒ v0.10.4
  [1eca21be] + FoldingTrees v1.0.1
  [d9f16b24] - Functors v0.1.0
  [0c68f7d7] ↓ GPUArrays v5.1.0 ⇒ v3.4.1
  [61eb1bfa] ↓ GPUCompiler v0.5.5 ⇒ v0.3.0
  [929cbde3] ↓ LLVM v2.0.0 ⇒ v1.7.0
  [bdcacae8] - LoopVectorization v0.8.24
  [872c559c] ↓ NNlib v0.7.4 ⇒ v0.6.6
  [21efa798] - SIMDPirates v0.8.24
  [476501e8] - SLEEFPirates v0.5.5
  [3d5dd08c] - VectorizationBase v0.12.32
  [e88e6eb3] ↓ Zygote v0.5.4 ⇒ v0.4.22

I think you're right... Let me see if I can run the rest of this with no problem.

jquetzalcoatl commented 4 years ago

Yeah, I ended up removing all my Pkg's and doing a clean ] add.

So I ran the code you posted earlier but I'm still an error slightly different though:

GPU compilation of kernel broadcast_kernel(CUDA.CuKernelContext, CuDeviceArray{Float32,1,CUDA.AS.Global}, Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}}, Int64) failed
KernelError: passing and using non-bitstype argument

Argument 4 to your kernel function is of type Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}}, which is not isbits:
  .args is of type Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}} which is not isbits.
    .1 is of type Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}} which is not isbits.
      .x is of type Array{CuArray{Float32,2},1} which is not isbits.

check_invocation(::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}, ::LLVM.Function) at validation.jl:68
macro expansion at driver.jl:240 [inlined]
macro expansion at TimerOutput.jl:206 [inlined]
codegen(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at driver.jl:239
codegen at driver.jl:63 [inlined]
compile(::Symbol, ::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget,CUDA.CUDACompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, strip::Bool, validate::Bool, only_entry::Bool) at driver.jl:39
compile at driver.jl:35 [inlined]
_cufunction(::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,CuDeviceArray{Float32,1,CUDA.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}},Int64}}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at execution.jl:310
_cufunction at execution.jl:304 [inlined]
check_cache(::typeof(CUDA._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,CuDeviceArray{Float32,1,CUDA.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}},Int64}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at cache.jl:24
(::GPUCompiler.var"#check_cache##kw")(::NamedTuple{(),Tuple{}}, ::typeof(GPUCompiler.check_cache), ::Function, ::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,CuDeviceArray{Float32,1,CUDA.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}},Int64}}, ::UInt64) at cache.jl:11
broadcast_kernel at broadcast.jl:60 [inlined]
cached_compilation(::typeof(CUDA._cufunction), ::GPUCompiler.FunctionSpec{GPUArrays.var"#broadcast_kernel#18",Tuple{CUDA.CuKernelContext,CuDeviceArray{Float32,1,CUDA.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}},Int64}}, ::UInt64; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at cache.jl:0
cached_compilation at cache.jl:40 [inlined]
cufunction(::GPUArrays.var"#broadcast_kernel#18", ::Type{Tuple{CUDA.CuKernelContext,CuDeviceArray{Float32,1,CUDA.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(identity),Tuple{Base.Broadcast.Extruded{Array{CuArray{Float32,2},1},Tuple{Bool},Tuple{Int64}}}},Int64}}; name::Nothing, kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}) at execution.jl:298
cufunction at execution.jl:293 [inlined]
#launch_heuristic#816 at gpuarrays.jl:19 [inlined]
launch_heuristic at gpuarrays.jl:17 [inlined]
copyto! at broadcast.jl:66 [inlined]
copyto! at broadcast.jl:886 [inlined]
materialize! at broadcast.jl:848 [inlined]
materialize!(::CuArray{Float32,1}, ::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1},Nothing,typeof(identity),Tuple{Array{CuArray{Float32,2},1}}}) at broadcast.jl:845
(::DiffEqSensitivity.var"#df#134"{Array{CuArray{Float32,2},1},CuArray{Float32,2},Colon})(::CuArray{Float32,1}, ::CuArray{Float32,2}, ::CuArray{Float32,2}, ::Float64, ::Int64) at concrete_solve.jl:102
(::DiffEqSensitivity.var"#95#97"{DiffEqSensitivity.var"#df#134"{Array{CuArray{Float32,2},1},CuArray{Float32,2},Colon},InterpolatingAdjoint{0,true,Val{:central},ZygoteVJP,Bool},Base.OneTo{Int64},UnitRange{Int64},UniformScaling{Bool},Bool,Nothing,Nothing,Nothing,Nothing,Bool,CuArray{Float32,1},Array{Float64,1},CuArray{Float32,2},Base.RefValue{Int64},Int64,UniformScaling{Bool}})(::OrdinaryDiffEq.ODEIntegrator{ROCK2{Nothing},true,CuArray{Float32,1},Nothing...

Did you get this error as well?

ChrisRackauckas commented 4 years ago

Note with that the script I posted earlier I mentioned that you need https://github.com/SciML/DiffEqSensitivity.jl/pull/326 because of a bug in the loss function. I'll probably get that patch merged tonight. The workaround is just

function loss(p)
    tmp_prob = remake(prob, p=p)
    tmp_sol = solve(tmp_prob,ROCK2(), save_everystep=false,save_start=false, sensealg = InterpolatingAdjoint(autojacvec=ZygoteVJP()))
    sum(abs2, tmp_sol)
end

jquetzalcoatl commented 4 years ago

Yeah, I took the script you posted earlier and executed it in a new and fresh session and got that stack trace posted two comments above.

Indeed your proposal changes Array{Float32,1} to Array{CuArray{Float32,2},1}

ChrisRackauckas commented 4 years ago

The patch is now released, so that code will work if you have up to date packages.

SciML / DiffEqFlux.jl

KernelError: passing and using non-bitstype argument #390