Add GPU reverse mode to EnzymeExt #454

wsmoses · 2024-01-24T23:20:59Z

@michel2323 's PR, but opening so we can have a place to discuss.

wsmoses · 2024-01-24T23:21:06Z

wmoses@beast:~/git/Enzyme.jl/KernelAbstractions.jl (enz_rev_gpu) $ ../julia-1.10.0-rc2/bin/julia --project reverse_gpu.jl 
Custom rule GPU
TapeType = @NamedTuple{1, 2, 3::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 4::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 5::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::Core.LLVMPtr{UInt8, 0}, 4::@NamedTuple{1, 2, 3}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64}, 2, 3::Bool, 4::Bool, 5::Bool}, 6, 7, 8, 9, 10::Float64, 11::Float64}
kernels: Error During Test at /home/wmoses/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:31
  Got exception outside of a @test
  GPU compilation of MethodInstance for EnzymeExt.aug_fwd(::KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}}, ::typeof(gpu_square!), ::Val{(false, false, true)}, ::Vector{@NamedTuple{1, 2, 3::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 4::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 5::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::Core.LLVMPtr{UInt8, 0}, 4::@NamedTuple{1, 2, 3}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64}, 2, 3::Bool, 4::Bool, 5::Bool}, 6, 7, 8, 9, 10::Float64, 11::Float64}}, ::Duplicated{CuDeviceVector{Float64, 1}}) failed
  KernelError: passing and using non-bitstype argument
  
  Argument 5 to your kernel function is of type Vector{@NamedTuple{1, 2, 3::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 4::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 5::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::Core.LLVMPtr{UInt8, 0}, 4::@NamedTuple{1, 2, 3}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64}, 2, 3::Bool, 4::Bool, 5::Bool}, 6, 7, 8, 9, 10::Float64, 11::Float64}}, which is not isbits:
  
  
  Stacktrace:
    [1] check_invocation(job::GPUCompiler.CompilerJob)
      @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/validation.jl:92
    [2] macro expansion
      @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:123 [inlined]
    [3] macro expansion
      @ ~/.julia/packages/TimerOutputs/RsWnF/src/TimerOutput.jl:253 [inlined]
    [4] 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:121
    [5] 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
    [6] compile
      @ ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:98 [inlined]
    [7] #1075
      @ ~/.julia/packages/CUDA/YIj5X/src/compiler/compilation.jl:247 [inlined]
    [8] JuliaContext(f::CUDA.var"#1075#1077"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams}})
      @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:47
    [9] compile(job::GPUCompiler.CompilerJob)
      @ CUDA ~/.julia/packages/CUDA/YIj5X/src/compiler/compilation.jl:246
   [10] 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
   [11] 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
   [12] macro expansion
      @ ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:382 [inlined]
   [13] macro expansion
      @ ./lock.jl:267 [inlined]
   [14] cufunction(f::typeof(EnzymeExt.aug_fwd), tt::Type{Tuple{KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}}, typeof(gpu_square!), Val{(false, false, true)}, Vector{@NamedTuple{1, 2, 3::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 4::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::@NamedTuple{1, 2, 3}, 4::Core.LLVMPtr{UInt8, 0}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64, 10, 11::Bool}, 2, 3::Bool, 4::Bool, 5::Bool}, 5::@NamedTuple{1::@NamedTuple{1::UInt32, 2::@NamedTuple{1, 2::@NamedTuple{1, 2::UInt64, 3::Bool, 4::UInt64}, 3, 4::Bool}, 3::Core.LLVMPtr{UInt8, 0}, 4::@NamedTuple{1, 2, 3}, 5::Bool, 6, 7::Bool, 8::UInt64, 9::UInt64}, 2, 3::Bool, 4::Bool, 5::Bool}, 6, 7, 8, 9, 10::Float64, 11::Float64}}, Duplicated{CuDeviceVector{Float64, 1}}}}; kwargs::@Kwargs{always_inline::Bool, maxthreads::Nothing})
      @ CUDA ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:377
   [15] macro expansion
      @ ~/.julia/packages/CUDA/YIj5X/src/compiler/execution.jl:104 [inlined]
   [16] (::KernelAbstractions.Kernel{CUDABackend, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, typeof(EnzymeExt.aug_fwd)})(::Function, ::Vararg{Any}; ndrange::Tuple{Int64}, workgroupsize::Nothing)
      @ CUDA.CUDAKernels ~/.julia/packages/CUDA/YIj5X/src/CUDAKernels.jl:118
   [17] #augmented_primal#12
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/ext/EnzymeExt.jl:163
   [18] augmented_primal
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/ext/EnzymeExt.jl:115 [inlined]
   [19] square_caller
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:14 [inlined]
   [20] square_caller
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:0 [inlined]
   [21] diffejulia_square_caller_3883_inner_1wrap
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:0
   [22] macro expansion
      @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:5306 [inlined]
   [23] enzyme_call
      @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:4984 [inlined]
   [24] CombinedAdjointThunk
      @ Enzyme.Compiler ~/git/Enzyme.jl/src/compiler.jl:4926 [inlined]
   [25] autodiff
      @ Enzyme ~/git/Enzyme.jl/src/Enzyme.jl:215 [inlined]
   [26] autodiff(::ReverseMode{false, FFIABI}, ::Const{typeof(square_caller)}, ::Duplicated{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, ::Const{CUDABackend})
      @ Enzyme ~/git/Enzyme.jl/src/Enzyme.jl:238
   [27] autodiff
      @ ~/git/Enzyme.jl/src/Enzyme.jl:224 [inlined]
   [28] macro expansion
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:40 [inlined]
   [29] macro expansion
      @ ~/git/Enzyme.jl/julia-1.10.0-rc2/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
   [30] enzyme_testsuite(backend::Type{CUDABackend}, ArrayT::Type, supports_reverse::Bool)
      @ Main ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:32
   [31] top-level scope
      @ ~/git/Enzyme.jl/KernelAbstractions.jl/reverse_gpu.jl:64
   [32] include(mod::Module, _path::String)
      @ Base ./Base.jl:495
   [33] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:318
   [34] _start()
      @ Base ./client.jl:552
Test Summary: | Error  Total     Time
kernels       |     1      1  1m38.4s

wsmoses · 2024-01-24T23:21:53Z

ext/EnzymeExt.jl

+        @show TapeType
+
+
+        subtape = Array{TapeType}(undef, size(blocks(iterspace)))


This shouldn't be Array, but it should be the device type specific to the array.

Is there a way to get that @vchuravy @michel2323

KernelAbstractions.allocate

vchuravy · 2024-01-25T02:07:56Z

Project.toml

@@ -6,6 +6,9 @@ version = "0.9.14"
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 Atomix = "a9b6321e-bd34-4604-b9c9-b65b8de01458"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"


Suggested change

CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

michel2323 · 2024-01-25T16:19:08Z

@wsmoses Forward mode doesn't work anymore, which used to work when I started on this. I'm on latest Enzyme#main
out_fwd.log.

(KernelAbstractions) pkg> st
Project KernelAbstractions v0.9.15
Status `~/.julia/dev/KernelAbstractions/Project.toml`
  [79e6a3ab] Adapt v4.0.1
  [a9b6321e] Atomix v0.1.0
  [052768ef] CUDA v5.2.0
  [7da242da] Enzyme v0.11.13 `~/.julia/dev/Enzyme`
  [1914dd2f] MacroTools v0.5.13
  [aea7be01] PrecompileTools v1.2.0
  [ae029012] Requires v1.3.0
  [90137ffa] StaticArrays v1.9.1
  [013be700] UnsafeAtomics v0.2.1
  [d80eeb9a] UnsafeAtomicsLLVM v0.1.3
  [7cc45869] Enzyme_jll v0.0.98+0 `../Enzyme_jll`
  [b77e0a4c] InteractiveUtils
  [37e2e46d] LinearAlgebra
  [2f01184e] SparseArrays v1.10.0
  [cf7118a7] UUIDs

wsmoses · 2024-01-27T04:17:30Z

ext/CUDAEnzymeExt.jl

+        @show TapeType
+
+
+        subtape = Array{TapeType}(undef, size(blocks(iterspace)))


@michel2323 can you have this use the device-specific allocator like @vchuravy mentioned above?

wsmoses · 2024-01-27T04:19:45Z

test/reverse_gpu.jl

+function mul_caller(A, B, backend)
+    kernel = mul!(backend)
+    kernel(A, B, ndrange=size(A))
+    KernelAbstractions.synchronize(backend)


@vchuravy is there a KA way to have the kernel run immediately rather than get sync'd. That way this can be separately merged from adding support for KA async

I am not sure what you mean by that? CUDA etc are all async, so the synchronized is needed in any case. You can leave it out and rely on stream ordering?

Ok in which case we need to add KA sync rules for forward and reverse. Forward is easy, just sync the regular and shadow kernels. Reverse needs to launch the reverse kernel. and reverse kernel execute needs to sync it

michel2323 · 2024-01-29T20:54:26Z

I added the following allocate call:

subtape = allocate(CUDABackend(), TapeType, size(blocks(iterspace)))

Now with [email protected] and the artifact I get:

╰─$ julia --project=. reverse_gpu.jl
kernels: Error During Test at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:28
  Got exception outside of a @test
  AssertionError: value_type(lhs_v) == value_type(rhs_v)
  Stacktrace:
    [1] (::Enzyme.Compiler.var"#getparent#361"{LLVM.Function, LLVM.IntegerType, Int64, Dict{LLVM.PHIInst, LLVM.PHIInst}, Dict{LLVM.PHIInst, LLVM.PHIInst}, LLVM.PHIInst, LLVM.BitCastInst, LLVM.IRBuilder})(v::LLVM.SelectInst, offset::LLVM.ConstantInt, hasload::Bool)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler/optimize.jl:262
    [2] (::Enzyme.Compiler.var"#getparent#361"{LLVM.Function, LLVM.IntegerType, Int64, Dict{LLVM.PHIInst, LLVM.PHIInst}, Dict{LLVM.PHIInst, LLVM.PHIInst}, LLVM.PHIInst, LLVM.BitCastInst, LLVM.IRBuilder})(v::LLVM.BitCastInst, offset::LLVM.ConstantInt, hasload::Bool)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler/optimize.jl:223
    [3] nodecayed_phis!(mod::LLVM.Module)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler/optimize.jl:278
    [4] optimize!
      @ ~/.julia/packages/Enzyme/Dd2LU/src/compiler/optimize.jl:1334 [inlined]
    [5] nested_codegen!(mode::Enzyme.API.CDerivativeMode, mod::LLVM.Module, funcspec::Core.MethodInstance, world::UInt64)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:1416
    [6] enzyme_custom_common_rev(forward::Bool, B::LLVM.IRBuilder, orig::LLVM.CallInst, gutils::Enzyme.Compiler.GradientUtils, normalR::Ptr{Ptr{LLVM.API.LLVMOpaqueValue}}, shadowR::Ptr{Ptr{LLVM.API.LLVMOpaqueValue}}, tape::Nothing)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/rules/customrules.jl:567
    [7] enzyme_custom_augfwd
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/rules/customrules.jl:886 [inlined]
    [8] (::Enzyme.Compiler.var"#212#213")(B::Ptr{LLVM.API.LLVMOpaqueBuilder}, OrigCI::Ptr{LLVM.API.LLVMOpaqueValue}, gutils::Ptr{Nothing}, normalR::Ptr{Ptr{LLVM.API.LLVMOpaqueValue}}, shadowR::Ptr{Ptr{LLVM.API.LLVMOpaqueValue}}, tapeR::Ptr{Ptr{LLVM.API.LLVMOpaqueValue}})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/rules/llvmrules.jl:1139
    [9] EnzymeCreatePrimalAndGradient(logic::Enzyme.Logic, todiff::LLVM.Function, retType::Enzyme.API.CDIFFE_TYPE, constant_args::Vector{Enzyme.API.CDIFFE_TYPE}, TA::Enzyme.TypeAnalysis, returnValue::Bool, dretUsed::Bool, mode::Enzyme.API.CDerivativeMode, width::Int64, additionalArg::Ptr{Nothing}, forceAnonymousTape::Bool, typeInfo::Enzyme.FnTypeInfo, uncacheable_args::Vector{Bool}, augmented::Ptr{Nothing}, atomicAdd::Bool)
      @ Enzyme.API ~/.julia/packages/Enzyme/Dd2LU/src/api.jl:141
   [10] enzyme!(job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams}, mod::LLVM.Module, primalf::LLVM.Function, TT::Type, mode::Enzyme.API.CDerivativeMode, width::Int64, parallel::Bool, actualRetType::Type, wrap::Bool, modifiedBetween::Tuple{Bool, Bool, Bool}, returnPrimal::Bool, jlrules::Vector{String}, expectedTapeType::Type, loweredArgs::Set{Int64}, boxedArgs::Set{Int64})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:3124
   [11] codegen(output::Symbol, job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams}; libraries::Bool, deferred_codegen::Bool, optimize::Bool, toplevel::Bool, strip::Bool, validate::Bool, only_entry::Bool, parent_job::Nothing)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:4756
   [12] codegen
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:4339 [inlined]
   [13] _thunk(job::GPUCompiler.CompilerJob{Enzyme.Compiler.EnzymeTarget, Enzyme.Compiler.EnzymeCompilerParams}, postopt::Bool) (repeats 2 times)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:5351
   [14] cached_compilation
      @ ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:5385 [inlined]
   [15] (::Enzyme.Compiler.var"#506#507"{DataType, DataType, DataType, Enzyme.API.CDerivativeMode, Tuple{Bool, Bool, Bool}, Int64, Bool, Bool, UInt64, DataType})(ctx::LLVM.Context)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:5451
   [16] JuliaContext(f::Enzyme.Compiler.var"#506#507"{DataType, DataType, DataType, Enzyme.API.CDerivativeMode, Tuple{Bool, Bool, Bool}, Int64, Bool, Bool, UInt64, DataType})
      @ GPUCompiler ~/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:47
   [17] #s1056#505
      @ ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:5403 [inlined]
   [18] var"#s1056#505"(FA::Any, A::Any, TT::Any, Mode::Any, ModifiedBetween::Any, width::Any, ReturnPrimal::Any, ShadowInit::Any, World::Any, ABI::Any, ::Any, ::Type, ::Type, ::Type, tt::Any, ::Type, ::Type, ::Type, ::Type, ::Type, ::Any)
      @ Enzyme.Compiler ./none:0
   [19] (::Core.GeneratedFunctionStub)(::UInt64, ::LineNumberNode, ::Any, ::Vararg{Any})
      @ Core ./boot.jl:602
   [20] autodiff
      @ Enzyme ~/.julia/packages/Enzyme/Dd2LU/src/Enzyme.jl:209 [inlined]
   [21] autodiff(::ReverseMode{false, FFIABI}, ::Const{typeof(square_caller)}, ::Duplicated{CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}, ::Const{CUDABackend})
      @ Enzyme ~/.julia/packages/Enzyme/Dd2LU/src/Enzyme.jl:238
   [22] autodiff
      @ ~/.julia/packages/Enzyme/Dd2LU/src/Enzyme.jl:224 [inlined]
   [23] macro expansion
      @ ~/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:37 [inlined]
   [24] macro expansion
      @ ~/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
   [25] enzyme_testsuite(backend::Type{CUDABackend}, ArrayT::Type, supports_reverse::Bool)
      @ Main ~/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:29
   [26] top-level scope
      @ ~/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:64

With the latest Enzyme and Enzyme.jl I get this below in the call to

KernelAbstractions.jl/ext/CUDAEnzymeExt.jl

Line 54 in 3c38fc7

    
           TapeType = EnzymeCore.tape_type(job, ReverseSplitModified(ReverseSplitWithPrimal, ModifiedBetween), FT, Const,  Const{ctxTy}, map(Core.Typeof, args2)...)

.

[32421] signal (11.1): Segmentation fault
in expression starting at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:64
typekeyvalue_hash at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/jltypes.c:1622 [inlined]
lookup_typevalue at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/jltypes.c:1059
jl_inst_arg_tuple_type at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/jltypes.c:2157
jl_f_tuple at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/builtins.c:868 [inlined]
jl_f_tuple at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/builtins.c:863
absint at /home/michel/.julia/dev/Enzyme/src/absint.jl:116
abs_typeof at /home/michel/.julia/dev/Enzyme/src/absint.jl:213
unknown function (ip: 0x7f48e19f5043)
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
check_ir! at /home/michel/.julia/dev/Enzyme/src/compiler/validation.jl:500
check_ir! at /home/michel/.julia/dev/Enzyme/src/compiler/validation.jl:208
check_ir! at /home/michel/.julia/dev/Enzyme/src/compiler/validation.jl:178
check_ir at /home/michel/.julia/dev/Enzyme/src/compiler/validation.jl:157 [inlined]
#codegen#468 at /home/michel/.julia/dev/Enzyme/src/compiler.jl:4382
codegen at /home/michel/.julia/dev/Enzyme/src/compiler.jl:4346 [inlined]
#48 at /home/michel/.julia/dev/Enzyme/src/Enzyme.jl:672
JuliaContext at /home/michel/.julia/packages/GPUCompiler/U36Ed/src/driver.jl:47
tape_type at /home/michel/.julia/dev/Enzyme/src/Enzyme.jl:671 [inlined]
#augmented_primal#4 at /home/michel/.julia/dev/KernelAbstractions/ext/CUDAEnzymeExt.jl:57
augmented_primal at /home/michel/.julia/dev/KernelAbstractions/ext/CUDAEnzymeExt.jl:14 [inlined]
square_caller at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:13 [inlined]
square_caller at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:0 [inlined]
diffejulia_square_caller_3884_inner_1wrap at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:0
macro expansion at /home/michel/.julia/dev/Enzyme/src/compiler.jl:5306 [inlined]
enzyme_call at /home/michel/.julia/dev/Enzyme/src/compiler.jl:4984 [inlined]
CombinedAdjointThunk at /home/michel/.julia/dev/Enzyme/src/compiler.jl:4926 [inlined]
autodiff at /home/michel/.julia/dev/Enzyme/src/Enzyme.jl:215 [inlined]
autodiff at /home/michel/.julia/dev/Enzyme/src/Enzyme.jl:238
unknown function (ip: 0x7f48e19edfba)
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
autodiff at /home/michel/.julia/dev/Enzyme/src/Enzyme.jl:224 [inlined]
macro expansion at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:37 [inlined]
macro expansion at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/usr/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
enzyme_testsuite at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:29
unknown function (ip: 0x7f49504d5c9f)
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
jl_apply at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/julia.h:1982 [inlined]
do_call at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/interpreter.c:126
eval_value at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/interpreter.c:223
eval_stmt_value at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/interpreter.c:174 [inlined]
eval_body at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/interpreter.c:617
jl_interpret_toplevel_thunk at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/interpreter.c:775
jl_toplevel_eval_flex at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/toplevel.c:934
jl_toplevel_eval_flex at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/toplevel.c:877
ijl_toplevel_eval_in at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/toplevel.c:985
eval at ./boot.jl:385 [inlined]
include_string at ./loading.jl:2070
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
_include at ./loading.jl:2130
include at ./Base.jl:495
jfptr_include_46343.1 at /home/michel/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/lib/julia/sys.so (unknown line)
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
exec_options at ./client.jl:318
_start at ./client.jl:552
jfptr__start_82703.1 at /home/michel/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/lib/julia/sys.so (unknown line)
_jl_invoke at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:2894 [inlined]
ijl_apply_generic at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/gf.c:3076
jl_apply at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/julia.h:1982 [inlined]
true_main at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/jlapi.c:582
jl_repl_entrypoint at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/src/jlapi.c:731
main at /cache/build/builder-amdci4-6/julialang/julia-release-1-dot-10/cli/loader_exe.c:58
unknown function (ip: 0x7f4967759d8f)
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8)
Allocations: 223384695 (Pool: 223135920; Big: 248775); GC: 129
[1]    32421 segmentation fault  julia --project=. reverse_gpu.jl

wsmoses · 2024-01-29T20:56:39Z

you should update Enzyme to latest (0.11.14)

michel2323 · 2024-02-06T17:03:11Z

The reverse kernel uses autodiff_deferred_thunk as opposed to the forward mode using autodiff_deferred. Indeed, there is no test for autodiff_deferred_thunk on CUDA in Enzyme.jl. Trying my luck, but not sure I'll figure it out.

 kernels: Error During Test at /home/michel/.julia/dev/KernelAbstractions/test/reverse_gpu.jl:28
  Got exception outside of a @test
  InvalidIRError: compiling MethodInstance for CUDAEnzymeExt.aug_fwd(::KernelAbstractions.CompilerMetadata{KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicCheck, Nothing, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, KernelAbstractions.NDIteration.NDRange{1, KernelAbstractions.NDIteration.DynamicSize, KernelAbstractions.NDIteration.DynamicSize, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}, CartesianIndices{1, Tuple{Base.OneTo{Int64}}}}}, ::typeof(gpu_square!), ::Val{(false, false, false)}, ::CuDeviceVector{Float64, 1}, ::Duplicated{CuDeviceVector{Float64, 1}}) resulted in invalid LLVM IR
  Reason: unsupported dynamic function invocation (call to autodiff_deferred_thunk(::EnzymeCore.ReverseModeSplit{ReturnPrimal, ReturnShadow, Width, ModifiedBetweenT, RABI}, ::Type{FA}, ::Type{A}, args...) where {FA<:Annotation, A<:Annotation, ReturnPrimal, ReturnShadow, Width, ModifiedBetweenT, RABI<:ABI} @ Enzyme ~/.julia/dev/Enzyme/src/Enzyme.jl:726)
  Stacktrace:
   [1] aug_fwd
     @ ~/.julia/dev/KernelAbstractions/ext/enzyme_utils.jl:7
  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

michel2323 · 2024-02-27T16:56:34Z

@vchuravy Cleaned up. Are we waiting for EnzymeAD/Enzyme.jl#1104 and JuliaGPU/CUDA.jl#2260 ?

vchuravy · 2024-04-09T15:55:48Z

Will need to change

KernelAbstractions.jl/Project.toml

Line 23 in c5fe83c

EnzymeCore = "0.6.4, 0.7"

to only be 0.7.1

wsmoses · 2024-05-11T01:58:06Z

@michel2323 given that the prerequisites have landed, mind getting this over the finish line?

michel2323 · 2024-05-31T17:47:09Z

@wsmoses @vchuravy Cleanup with working tests (if CUDA is working). Last unresolved issue is active arguments to a kernel. The compiler cannot figure out the type here for the actives, so all actives are marked Any which then leads to a wrong return type.

KernelAbstractions.jl/ext/EnzymeExt.jl

Line 334 in c21f6bb

res = ntuple(Val(N)) do i

I tried to fix it, but I'm not sure there's a way. So for now, it gracefully errors with

KernelAbstractions.jl/ext/EnzymeExt.jl

Line 259 in c21f6bb

error("Active kernel arguments not supported on GPU")

in the augmented forward run.

test/Project.toml

vchuravy · 2024-06-06T22:04:35Z

Will need rebase for #478

ext/EnzymeExt.jl

src/KernelAbstractions.jl

Fix examples tests with CUDA backend Add synchronize rule

wsmoses

Looks good to me but @vchuravy should do the proper approval/merge

michel2323 · 2024-07-03T15:10:28Z

@vchuravy Bump. Is there a blocker here?

vchuravy · 2024-07-03T17:29:09Z

The tests are a bit sparse and they should be enabled for more than the CPU backend?

test/runtests.jl

wsmoses commented Jan 24, 2024

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vchuravy reviewed Jan 25, 2024

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michel2323 force-pushed the enz_rev_gpu branch from d6c5a72 to 07d52b7 Compare January 25, 2024 15:57

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wsmoses commented Jan 27, 2024

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michel2323 force-pushed the enz_rev_gpu branch from 4a20dfd to 3c38fc7 Compare January 29, 2024 20:38

michel2323 force-pushed the enz_rev_gpu branch from 1ca1e3b to d216c5c Compare January 29, 2024 21:22

michel2323 force-pushed the enz_rev_gpu branch 2 times, most recently from 60112d6 to e92c3b8 Compare February 14, 2024 16:55

michel2323 mentioned this pull request Feb 27, 2024

Support enzyme in KA JuliaGPU/CUDA.jl#2260

Closed

michel2323 force-pushed the enz_rev_gpu branch from a9ceb66 to 8d4e7f8 Compare March 28, 2024 15:00

michel2323 changed the title ~~Enz rev gpu~~ Add GPU reverse mode to EnzymeExt Mar 28, 2024

michel2323 mentioned this pull request Apr 9, 2024

Add EnzymeCore extension for parent_job JuliaGPU/CUDA.jl#2281

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michel2323 force-pushed the enz_rev_gpu branch 3 times, most recently from ede2f76 to c21f6bb Compare May 31, 2024 17:43

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vchuravy mentioned this pull request Jun 10, 2024

issue with using enzyme on kernel abstractions on CUDA backend #480

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michel2323 force-pushed the enz_rev_gpu branch from c21f6bb to f69d47a Compare June 12, 2024 20:50

michel2323 force-pushed the enz_rev_gpu branch 2 times, most recently from eb9b999 to 29d4580 Compare June 27, 2024 20:00

wsmoses commented Jun 28, 2024

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michel2323 and others added 4 commits June 27, 2024 21:08

Add Enzyme GPU support

e59d534

Fix examples tests with CUDA backend Add synchronize rule

Update Project.toml

4797541

Update utils.jl

d851689

Buildkite for CUDA reverse

775eb1e

michel2323 force-pushed the enz_rev_gpu branch from 29d4580 to 775eb1e Compare June 28, 2024 02:08

wsmoses commented Jun 28, 2024

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vchuravy reviewed Jul 3, 2024

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Update test/runtests.jl

d6b9be9

vchuravy approved these changes Jul 6, 2024

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vchuravy merged commit 2053b1f into main Jul 6, 2024
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vchuravy deleted the enz_rev_gpu branch July 6, 2024 09:06

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Add GPU reverse mode to EnzymeExt #454

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		@show TapeType


		subtape = Array{TapeType}(undef, size(blocks(iterspace)))

Add GPU reverse mode to EnzymeExt #454

Add GPU reverse mode to EnzymeExt #454

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