# "Abstractdiffy" not defined for Nonconvex.jl

**URL:** <https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789>\
**Category:** Optimization (Mathematical)\
**Created:** [December 18, 2023, 8:46pm UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789 "2023-12-18T20:46:08Z")\
**Posts on this page:** 6\
**Page:** 1

<div class="post-metadata">

**Author:** ![bdas123](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bdas123/32/32142_2.png) [@bdas123](https://discourse.julialang.org/u/bdas123)\
**Post date:** [December 18, 2023, 8:46pm UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/1 "2023-12-18T20:46:08Z")

</div>

Hello, here is a simplified version of my code, which uses Nonconvex.jl (, ReverseDiff.jl, AbstractDifferentiation.jl, and CUDA.jl:

```julia
using DelimitedFiles, NPZ, SharedArrays, BenchmarkTools, Statistics, DataFrames

using AbstractDifferentiation, ReverseDiff

backend = AbstractDifferentiation.ReverseDiffBackend()

using Nonconvex

Nonconvex.@load NLopt

using CUDA

x0 = rand(2979)
x0 = CuArray(x0)

obj = Nonconvex.NonconvexCore.CountingFunction(calc_sse)

lower_bounds = zeros(2979)
upper_bounds = zeros(2979).+1000

function calc_sse(x0)
    return sum(x0)
end

function optimization(x0)
    model = Model(obj)
    addvar!(model, lower_bounds, upper_bounds, init=x0)
    ad_model = abstractdiffy(model,backend)
    alg = NLoptAlg(:LD_LBFGS)
    options = NLoptOptions(ftol_rel = 1e-3)
    result = optimize(ad_model, alg, x0, options = options)
    return result
end

@time begin

    result = optimization(x0)
    
end

```

I am getting the error

```julia
UndefVarError: `abstractdiffy` not defined

Stacktrace:
 [1] optimization(x0::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
   @ Main ./In[57]:69
 [2] macro expansion
   @ ./In[60]:3 [inlined]
 [3] top-level scope
   @ ./timing.jl:273 [inlined]
 [4] top-level scope
   @ ./In[60]:0

```

This code worked perfectly when I was not trying to use CUDA.jl

---

<div class="post-metadata">

**Author:** ![bdas123](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bdas123/32/32142_2.png) [@bdas123](https://discourse.julialang.org/u/bdas123)\
**Post date:** [December 18, 2023, 9:30pm UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/2 "2023-12-18T21:30:46Z")

</div>

A single-function evaluation worked as well.

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [December 19, 2023, 1:49am UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/3 "2023-12-19T01:49:21Z")

</div>

What is `import Pkg; Pkg.status()`? You’ve probably installed an older version of the packages.

---

<div class="post-metadata">

**Author:** ![bdas123](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bdas123/32/32142_2.png) [@bdas123](https://discourse.julialang.org/u/bdas123)\
**Post date:** [December 19, 2023, 2:03am UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/4 "2023-12-19T02:03:40Z")

</div>

Yes, you are right.

Google CoLab does not automatically download the latest version of the package, which causes issues.

However, now I’m getting another issue now that the code recognizes `abstractdiffy`

```julia

InvalidIRError: compiling MethodInstance for (::GPUArrays.var"#broadcast_kernel#38")(::CUDA.CuKernelContext, ::CuDeviceVector{Tuple{Vector{Float64}, DifferentiableFlatten.var"#unflatten_to_Real#1"}, 1}, ::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, DifferentiableFlatten.var"#2#4", Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float64, 1}, Tuple{Bool}, Tuple{Int64}}}}, ::Int64) resulted in invalid LLVM IR
Reason: unsupported call through a literal pointer (call to ijl_alloc_array_1d)
Stacktrace:
  [1] Array
    @ ./boot.jl:477
  [2] Array
    @ ./boot.jl:486
  [3] similar
    @ ./abstractarray.jl:884
  [4] similar
    @ ./abstractarray.jl:883
  [5] _array_for
    @ ./array.jl:671
  [6] _array_for
    @ ./array.jl:674
  [7] vect
    @ ./array.jl:126
  [8] flatten
    @ ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:45
  [9] #2
    @ ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:53
 [10] _broadcast_getindex_evalf
    @ ./broadcast.jl:683
 [11] _broadcast_getindex
    @ ./broadcast.jl:656
 [12] getindex
    @ ./broadcast.jl:610
 [13] broadcast_kernel
    @ ~/.julia/packages/GPUArrays/dAUOE/src/host/broadcast.jl:64
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}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/validation.jl:147
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:440 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/RsWnF/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:439 [inlined]
  [5] emit_llvm(job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, only_entry::Bool, validate::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/utils.jl:92
  [6] emit_llvm
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/utils.jl:86 [inlined]
  [7] codegen(output::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:129
  [8] codegen
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:110 [inlined]
  [9] compile(target::Symbol, job::GPUCompiler.CompilerJob; libraries::Bool, toplevel::Bool, optimize::Bool, cleanup::Bool, strip::Bool, validate::Bool, only_entry::Bool)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:106
 [10] compile
    @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:98 [inlined]
 [11] #1075
    @ ~/.julia/packages/CUDA/YIj5X/src/compiler/compilation.jl:247 [inlined]
 [12] JuliaContext(f::CUDA.var"#1075#1077"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:47
 [13] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/YIj5X/src/compiler/compilation.jl:246
 [14] actual_compilation(cache::Dict{Any, CuFunction}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/execution.jl:125
 [15] cached_compilation(cache::Dict{Any, CuFunction}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/execution.jl:103
 [16] macro expansion
    @ ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:382 [inlined]
 [17] macro expansion
    @ ./lock.jl:267 [inlined]
 [18] cufunction(f::GPUArrays.var"#broadcast_kernel#38", tt::Type{Tuple{CUDA.CuKernelContext, CuDeviceVector{Tuple{Vector{Float64}, DifferentiableFlatten.var"#unflatten_to_Real#1"}, 1}, Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Tuple{Base.OneTo{Int64}}, DifferentiableFlatten.var"#2#4", Tuple{Base.Broadcast.Extruded{CuDeviceVector{Float64, 1}, Tuple{Bool}, Tuple{Int64}}}}, Int64}}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:377
 [19] cufunction
    @ ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:374 [inlined]
 [20] macro expansion
    @ ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:104 [inlined]
 [21] #launch_heuristic#1120
    @ ~/.julia/packages/CUDA/YIj5X/src/gpuarrays.jl:17 [inlined]
 [22] launch_heuristic
    @ ~/.julia/packages/CUDA/YIj5X/src/gpuarrays.jl:15 [inlined]
 [23] _copyto!
    @ ~/.julia/packages/GPUArrays/dAUOE/src/host/broadcast.jl:70 [inlined]
 [24] copyto!
    @ ~/.julia/packages/GPUArrays/dAUOE/src/host/broadcast.jl:51 [inlined]
 [25] copy
    @ ~/.julia/packages/GPUArrays/dAUOE/src/host/broadcast.jl:42 [inlined]
 [26] materialize(bc::Base.Broadcast.Broadcasted{CUDA.CuArrayStyle{1}, Nothing, DifferentiableFlatten.var"#2#4", Tuple{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}})
    @ Base.Broadcast ./broadcast.jl:873
 [27] map(::Function, ::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
    @ GPUArrays ~/.julia/packages/GPUArrays/dAUOE/src/host/broadcast.jl:89
 [28] flatten(x::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
    @ DifferentiableFlatten ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:53
 [29] (::DifferentiableFlatten.var"#2#4")(val::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
    @ DifferentiableFlatten ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:53
 [30] iterate
    @ ./generator.jl:47 [inlined]
 [31] _collect
    @ ./array.jl:802 [inlined]
 [32] collect_similar(cont::Vector{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, itr::Base.Generator{Vector{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, DifferentiableFlatten.var"#2#4"})
    @ Base ./array.jl:711
 [33] map(f::Function, A::Vector{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}})
    @ Base ./abstractarray.jl:3263
 [34] flatten(x::Vector{Any})
    @ DifferentiableFlatten ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:53
 [35] #8
    @ ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:70 [inlined]
 [36] map
    @ ./tuple.jl:273 [inlined]
 [37] flatten(x::Tuple{Vector{Any}})
    @ DifferentiableFlatten ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:70
 [38] tovecfunc(f::Function, x::Vector{Any}; flatteny::Bool)
    @ NonconvexCore ~/.julia/packages/NonconvexCore/TFoWG/src/models/vec_model.jl:106
 [39] tovecfunc
    @ ~/.julia/packages/NonconvexCore/TFoWG/src/models/vec_model.jl:105 [inlined]
 [40] abstractdiffy(f::Function, backend::AbstractDifferentiation.ReverseDiffBackend, x::Vector{Any})
    @ NonconvexUtils ~/.julia/packages/NonconvexUtils/i3gzf/src/abstractdiff.jl:24
 [41] abstractdiffy(model::Model{Vector{Any}}, backend::AbstractDifferentiation.ReverseDiffBackend; objective::Bool, ineq_constraints::Bool, eq_constraints::Bool, sd_constraints::Bool)
    @ NonconvexUtils ~/.julia/packages/NonconvexUtils/i3gzf/src/abstractdiff.jl:31
 [42] abstractdiffy(model::Model{Vector{Any}}, backend::AbstractDifferentiation.ReverseDiffBackend)
    @ NonconvexUtils ~/.julia/packages/NonconvexUtils/i3gzf/src/abstractdiff.jl:28
 [43] optimization(x0::CuArray{Float64, 1, CUDA.Mem.DeviceBuffer})
    @ Main ./In[36]:68
 [44] macro expansion
    @ ./In[39]:3 [inlined]
 [45] top-level scope
    @ ./timing.jl:273 [inlined]
 [46] top-level scope
    @ ./In[39]:0

```

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [December 19, 2023, 2:24am UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/5 "2023-12-19T02:24:13Z")

</div>

I dont think NLopt supports GPU stuff

---

<div class="post-metadata">

**Author:** ![bdas123](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bdas123/32/32142_2.png) [@bdas123](https://discourse.julialang.org/u/bdas123)\
**Post date:** [December 19, 2023, 2:32am UTC](https://discourse.julialang.org/t/abstractdiffy-not-defined-for-nonconvex-jl/107789/6 "2023-12-19T02:32:01Z")

</div>

Oh okay, thank you!