Inference nondeterminisms can break GPU compilation

[sigmike]

Describe the bug

After a system and Julia update I can't train vgg_cifar10.jl from the Flux model zoo anymore. It generates this error:

ERROR: LoadError: InvalidIRError: compiling kernel rand!(CuDeviceVector{Float32, 1}, UInt32, UInt32) resulted in invalid LLVM IR
Reason: unsupported dynamic function invocation (call to CUDA.Philox2x32{R}() where R in CUDA at ~/.julia/packages/CUDA/01uIm/src/device/random.jl:46)

Stacktrace (from the MWE below)

 [1] Philox2x32
   @ ~/.julia/packages/CUDA/01uIm/src/device/random.jl:62
 [2] #default_rng
   @ ~/.julia/packages/CUDA/01uIm/src/device/random.jl:95
 [3] kernel
   @ ~/.julia/packages/CUDA/01uIm/src/random.jl:39
Reason: unsupported dynamic function invocation (call to rand(rng::AbstractRNG, ::Type{X}) where X in Random at /usr/share/julia/stdlib/v1.7/Random/src/Random.jl:257)
Stacktrace:
 [1] kernel
   @ ~/.julia/packages/CUDA/01uIm/src/random.jl:51
Hint: catch this exception as `err` and call `code_typed(err; interactive = true)` to introspect the erronous code
Stacktrace:
  [1] check_ir(job::GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{CUDA.var"#kernel#356", Tuple{CuDeviceVector{Float32, 1}, UInt32, UInt32}}}, args::LLVM.Module)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/EV8pB/src/validation.jl:139
  [2] macro expansion
    @ ~/.julia/packages/GPUCompiler/EV8pB/src/driver.jl:391 [inlined]
  [3] macro expansion
    @ ~/.julia/packages/TimerOutputs/LDL7n/src/TimerOutput.jl:252 [inlined]
  [4] macro expansion
    @ ~/.julia/packages/GPUCompiler/EV8pB/src/driver.jl:389 [inlined]
  [5] emit_asm(job::GPUCompiler.CompilerJob, ir::LLVM.Module; strip::Bool, validate::Bool, format::LLVM.API.LLVMCodeGenFileType)
    @ GPUCompiler ~/.julia/packages/GPUCompiler/EV8pB/src/utils.jl:64
  [6] cufunction_compile(job::GPUCompiler.CompilerJob, ctx::LLVM.Context)
    @ CUDA ~/.julia/packages/CUDA/01uIm/src/compiler/execution.jl:337
  [7] #260
    @ ~/.julia/packages/CUDA/01uIm/src/compiler/execution.jl:330 [inlined]
  [8] JuliaContext(f::CUDA.var"#260#261"{GPUCompiler.CompilerJob{GPUCompiler.PTXCompilerTarget, CUDA.CUDACompilerParams, GPUCompiler.FunctionSpec{CUDA.var"#kernel#356", Tuple{CuDeviceVector{Float32, 1}, UInt32, UInt32}}}})
    @ GPUCompiler ~/.julia/packages/GPUCompiler/EV8pB/src/driver.jl:74
  [9] cufunction_compile(job::GPUCompiler.CompilerJob)
    @ CUDA ~/.julia/packages/CUDA/01uIm/src/compiler/execution.jl:329
 [10] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(CUDA.cufunction_compile), linker::typeof(CUDA.cufunction_link))
    @ GPUCompiler ~/.julia/packages/GPUCompiler/EV8pB/src/cache.jl:90
 [11] cufunction(f::CUDA.var"#kernel#356", tt::Type{Tuple{CuDeviceVector{Float32, 1}, UInt32, UInt32}}; name::String, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ CUDA ~/.julia/packages/CUDA/01uIm/src/compiler/execution.jl:301
 [12] macro expansion
    @ ~/.julia/packages/CUDA/01uIm/src/compiler/execution.jl:102 [inlined]
 [13] rand!(rng::CUDA.RNG, A::CuArray{Float32, 1, CUDA.Mem.DeviceBuffer})
    @ CUDA ~/.julia/packages/CUDA/01uIm/src/random.jl:60
 [14] top-level scope
    @ mwe.jl:12
in expression starting at mwe.jl:12

To reproduce

I discussed that on discourse and was able to reduce the MWE to that:

using CUDA
using Random

struct X{T} end
Broadcast.broadcasted(f::X, args...) = map(f, args...)

struct Y <: Base.Broadcast.BroadcastStyle end
Base.Broadcast.BroadcastStyle(::Base.Broadcast.BroadcastStyle, ::Y) = Y()

a = CuArray{Float32}(undef, 2)
Random.rand!(CUDA.default_rng(), a)

But if the Random.rand! instruction is done first, then the error does not happen:

using CUDA
using Random

a = CuArray{Float32}(undef, 2)
Random.rand!(CUDA.default_rng(), a) # No error here

struct X{T} end
Broadcast.broadcasted(f::X, args...) = map(f, args...)

struct Y <: Base.Broadcast.BroadcastStyle end
Base.Broadcast.BroadcastStyle(::Base.Broadcast.BroadcastStyle, ::Y) = Y()

a = CuArray{Float32}(undef, 2)
Random.rand!(CUDA.default_rng(), a) # Nor here

The actual lines in the original packages that trigger the error are:

• these lines in Flux' deprecations.jl
• this instruction in DataFrames' broadcasting.jl

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Expected behavior

No error.

Version info

Details on Julia:

Julia Version 1.7.2
Commit bf53498635 (2022-02-06 15:21 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-12.0.1 (ORCJIT, skylake)

Details on CUDA:

CUDA toolkit 11.7, artifact installation
NVIDIA driver 510.68.2, 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+510.68.2
- CUDNN: 8.30.2 (for CUDA 11.5.0)
- CUTENSOR: 1.4.0 (for CUDA 11.5.0)

Toolchain:
- Julia: 1.7.2
- LLVM: 12.0.1
- 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
- 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

1 device:
  0: NVIDIA GeForce GTX 1080 (sm_61, 7.325 GiB / 8.000 GiB available)

[maleadt]

Thanks for the MWE. I'm not sure we can do much about this though; inference/compilation being dependent on additional methods being defined can be caused by an heuristic kicking in after a limit has been exceeded, or on 'bugs' like JuliaLang/julia#35800. Here, the code pulled in by RandomNumbers.AbstractRNG seems to be pretty massive, relying on device-side broadcast which fails to compile statically after those method definitions. Simplifying that code might be one course of action.

That said, I also noticed that the code works again on 1.8, so maybe that's an easier solution?

[stefanjwojcik]

I believe I'm having the same issue with the DCGAN example in the Flux model zoo. I haven't yet been able to resolve it.

[mossr]

FYI, I had the same error when using Dropout as a layer in Julia v1.7 and it was resolved when switching to Julia v1.8