Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

Fix from #44 to work with CUDA arrays Does Not Work in Latest Version

Aperta
  • Qualcosa non va?
#93 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
35/100
Tipo di issue
Bug
Chiarezza
Abbastanza chiara
Stato di attività
Ferma
Stack tecnologico
julia
Ambito
hpc

Direzione di ricerca

Riproduci l'MWE con Julia 1.11.3, TaylorDiff v0.3.1 e CUDA v5.6.1, quindi ispeziona derivative.jl di TaylorDiff nell'area di make_seed e derivatives. Confronta il comportamento attuale con PR #44 e determina perché il broadcast di CUDA raggiunge una chiamata dinamica non supportata. Il lavoro è completato quando la derivata direzionale mostrata funziona con input CuArray.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Issue: Regression – Fix from #44 Does Not Work in Latest Version

Description

The fix implemented in PR #44 appears to no longer work in the latest version of TaylorDiff.jl.

This MWE:

v, direction = CuArray([0f0, 0f0]), CuArray([1.0f0, 0.0f0])
derivative(x -> sum(exp.(x)), v, direction, 2) # directional derivative

gives:

ERROR: InvalidIRError: compiling MethodInstance for (::GPUArrays.var"#gpu_broadcast_kernel_linear#38")(::KernelAbstractions.CompilerMetadata{…}, ::CuDeviceVector{…}, ::Base.Broadcast.Broadcasted{…}) resulted in invalid LLVM IR
Reason: unsupported dynamic function invocation (call to make_seed)
Stacktrace:
 [1] _broadcast_getindex_evalf
   @ ./broadcast.jl:678
 [2] _broadcast_getindex
   @ ./broadcast.jl:651
 [3] getindex
   @ ./broadcast.jl:610
 [4] macro expansion
   @ ~/.julia/packages/GPUArrays/Mot2g/src/host/broadcast.jl:54
 [5] gpu_broadcast_kernel_linear
   @ ~/.julia/packages/KernelAbstractions/mD0Rj/src/macros.jl:97
 [6] gpu_broadcast_kernel_linear
   @ ./none:0
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/Nxf8r/src/validation.jl:167
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:382 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/6KVfH/src/TimerOutput.jl:253 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:381 [inlined]
  [5]
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/utils.jl:108
  [6] emit_llvm
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/utils.jl:106 [inlined]
  [7]
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:100
  [8] codegen
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:82 [inlined]
  [9] compile(target::Symbol, job::GPUCompiler.CompilerJob; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:79
 [10] compile
    @ ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:74 [inlined]
 [11] #1147
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/compilation.jl:250 [inlined]
 [12] JuliaContext(f::CUDA.var"#1147#1150"{GPUCompiler.CompilerJob{…}}; kwargs::@Kwargs{})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:34
 [13] JuliaContext(f::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/driver.jl:25
 [14] compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/1kIOw/src/compiler/compilation.jl:249
 [15] actual_compilation(cache::Dict{…}, src::Core.MethodInstance, world::UInt64, cfg::GPUCompiler.CompilerConfig{…}, compiler::typeof(CUDA.compile), linker::typeof(CUDA.link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/execution.jl:237
 [16] cached_compilation(cache::Dict{…}, src::Core.MethodInstance, cfg::GPUCompiler.CompilerConfig{…}, compiler::Function, linker::Function)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/Nxf8r/src/execution.jl:151
 [17] macro expansion
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:380 [inlined]
 [18] macro expansion
    @ ./lock.jl:273 [inlined]
 [19] cufunction(f::GPUArrays.var"#gpu_broadcast_kernel_linear#38", tt::Type{…}; kwargs::@Kwargs{…})
    @ CUDA ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:375
 [20] macro expansion
    @ ~/.julia/packages/CUDA/1kIOw/src/compiler/execution.jl:112 [inlined]
 [21] (::KernelAbstractions.Kernel{…})(::CuArray{…}, ::Vararg{…}; ndrange::Tuple{…}, workgroupsize::Nothing)
    @ CUDA.CUDAKernels ~/.julia/packages/CUDA/1kIOw/src/CUDAKernels.jl:103
 [22] _copyto!
    @ ~/.julia/packages/GPUArrays/Mot2g/src/host/broadcast.jl:71 [inlined]
 [23] copyto!
    @ ~/.julia/packages/GPUArrays/Mot2g/src/host/broadcast.jl:44 [inlined]
 [24] copy
    @ ~/.julia/packages/GPUArrays/Mot2g/src/host/broadcast.jl:29 [inlined]
 [25] materialize
    @ ./broadcast.jl:872 [inlined]
 [26] broadcast(::typeof(TaylorDiff.make_seed), ::CuArray{…}, ::CuArray{…}, ::Int64)
    @ Base.Broadcast ./broadcast.jl:810
 [27] make_seed
    @ ~/.julia/packages/TaylorDiff/hFSFr/src/derivative.jl:5 [inlined]
 [28] derivatives
    @ ~/.julia/packages/TaylorDiff/hFSFr/src/derivative.jl:41 [inlined]
 [29] derivative(f::Function, x::CuArray{Float32, 1, CUDA.DeviceMemory}, l::CuArray{Float32, 1, CUDA.DeviceMemory}, p::Int64)
    @ TaylorDiff ~/.julia/packages/TaylorDiff/hFSFr/src/derivative.jl:17
 [30] top-level scope
    @ REPL[4]:1
Some type information was truncated. Use `show(err)` to see complete types.

Additional Information
Julia Version: 1.11.3
TaylorDiff Version: v0.3.1
Zygote Version: v0.6.75
CUDA Version v5.6.1

Lingua principale
Julia
Stelle
83
Fork
12
Nessuna PR unita negli ultimi 30g

Preparare l'ambiente

Apri in Codespaces

Avvia il container di sviluppo del progetto nel browser, con il tuo account GitHub.

  • Nessun Dockerfile né file Docker Compose
  • Nessun modello di pull request
  • Nessuna guida per i contributori

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di JuliaDiff/TaylorDiff.jl

Tutte le issue di JuliaDiff/TaylorDiff.jl

Issue simili

Altre issue su Julia

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.