Metal.jl
Metal.jl copied to clipboard
JuliaGPU
Int64 not supported on AMD GPUs?
I am running Julia Version 1.8.0-rc1 (2022-05-27) on OS X 12.4 with an AMD Radeon Pro 5700 XT GPU.
julia> a .+ 1
┌ Warning: Compilation of MetalLib to native code failed.
│ If you think this is a bug, please file an issue and attach /var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/jl_OUC1h1KIc6.metallib.
└ @ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:178
ERROR: MtlError: Compiler encountered an internal error (code 2, CompilerError)
Stacktrace:
[1] macro expansion
@ ~/.julia/packages/Metal/fQowO/lib/core/helpers.jl:68 [inlined]
[2] MtlComputePipelineState(d::MtlDevice, f::MtlFunction)
@ Metal.MTL ~/.julia/packages/Metal/fQowO/lib/core/compute_pipeline.jl:25
[3] mtlfunction_link(job::GPUCompiler.CompilerJob, compiled::NamedTuple{(:image, :entry), Tuple{Vector{UInt8}, String}})
@ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:172
[4] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(Metal.mtlfunction_compile), linker::typeof(Metal.mtlfunction_link))
@ GPUCompiler ~/.julia/packages/GPUCompiler/iaKrd/src/cache.jl:95
[5] mtlfunction(f::GPUArrays.var"#broadcast_kernel#15", tt::Type{Tuple{Metal.mtlKernelContext, MtlDeviceVector{Int64, 1}, Base.Broadcast.Broadcasted{Metal.MtlArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(+), Tuple{Base.Broadcast.Extruded{MtlDeviceVector{Int64, 1}, Tuple{Bool}, Tuple{Int64}}, Int64}}, Int64}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:143
[6] mtlfunction
@ ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:136 [inlined]
[7] macro expansion
@ ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:64 [inlined]
[8] #launch_heuristic#53
@ ~/.julia/packages/Metal/fQowO/src/gpuarrays.jl:14 [inlined]
[9] _copyto!
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:73 [inlined]
[10] copyto!
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:56 [inlined]
[11] copy
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:47 [inlined]
[12] materialize(bc::Base.Broadcast.Broadcasted{Metal.MtlArrayStyle{1}, Nothing, typeof(+), Tuple{MtlArray{Int64, 1}, Int64}})
@ Base.Broadcast ./broadcast.jl:860
[13] top-level scope
@ REPL[6]:1
[14] top-level scope
@ ~/.julia/packages/Metal/fQowO/src/initialization.jl:25
]
Here are the details
% ./usr/bin/julia
_
_ _ _(_)_ | Documentation: https://docs.julialang.org
(_) | (_) (_) |
_ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 1.8.0-rc1 (2022-05-27)
_/ |\__'_|_|_|\__'_| |
|__/ |
julia> import Pkg; Pkg.add("Metal")
Updating registry at `~/.julia/registries/General.toml`
Resolving package versions...
Installed GPUArrays ──────────── v8.4.0
Installed Metal_LLVM_Tools_jll ─ v0.3.0+1
Installed cmt_jll ────────────── v0.1.0+0
Installed GPUArraysCore ──────── v0.1.0
Installed CEnum ──────────────── v0.4.2
Installed LLVMExtra_jll ──────── v0.0.16+0
Installed GPUCompiler ────────── v0.16.1
Installed Metal ──────────────── v0.1.0
Installed LLVM ───────────────── v4.14.0
Downloaded artifact: Metal_LLVM_Tools
Downloaded artifact: LLVMExtra
Downloaded artifact: cmt
Updating `~/.julia/environments/v1.8/Project.toml`
[dde4c033] + Metal v0.1.0
Updating `~/.julia/environments/v1.8/Manifest.toml`
[79e6a3ab] + Adapt v3.3.3
[fa961155] + CEnum v0.4.2
[e2ba6199] + ExprTools v0.1.8
[0c68f7d7] + GPUArrays v8.4.0
[46192b85] + GPUArraysCore v0.1.0
[61eb1bfa] + GPUCompiler v0.16.1
[692b3bcd] + JLLWrappers v1.4.1
[929cbde3] + LLVM v4.14.0
[dde4c033] + Metal v0.1.0
[21216c6a] + Preferences v1.3.0
[189a3867] + Reexport v1.2.2
[a759f4b9] + TimerOutputs v0.5.20
[dad2f222] + LLVMExtra_jll v0.0.16+0
[0418c028] + Metal_LLVM_Tools_jll v0.3.0+1
[65323cdd] + cmt_jll v0.1.0+0
[0dad84c5] + ArgTools v1.1.1
[56f22d72] + Artifacts
[2a0f44e3] + Base64
[ade2ca70] + Dates
[f43a241f] + Downloads v1.6.0
[7b1f6079] + FileWatching
[b77e0a4c] + InteractiveUtils
[4af54fe1] + LazyArtifacts
[b27032c2] + LibCURL v0.6.3
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[ca575930] + NetworkOptions v1.2.0
[44cfe95a] + Pkg v1.8.0
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA v0.7.0
[9e88b42a] + Serialization
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[fa267f1f] + TOML v1.0.0
[a4e569a6] + Tar v1.10.0
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
[e66e0078] + CompilerSupportLibraries_jll v0.5.2+0
[deac9b47] + LibCURL_jll v7.81.0+0
[29816b5a] + LibSSH2_jll v1.10.2+0
[c8ffd9c3] + MbedTLS_jll v2.28.0+0
[14a3606d] + MozillaCACerts_jll v2022.2.1
[4536629a] + OpenBLAS_jll v0.3.20+0
[83775a58] + Zlib_jll v1.2.12+3
[8e850b90] + libblastrampoline_jll v5.1.0+0
[8e850ede] + nghttp2_jll v1.41.0+1
[3f19e933] + p7zip_jll v17.4.0+0
Precompiling project...
21 dependencies successfully precompiled in 12 seconds
julia> Metal.versioninfo()
ERROR: UndefVarError: Metal not defined
Stacktrace:
[1] top-level scope
@ REPL[2]:1
julia> using Metal
julia> Metal.versioninfo()
macOS 12.4.0, Darwin 21.5.0
Toolchain:
- Julia: 1.8.0-rc1
- LLVM: 13.0.1
1 device:
- AMD Radeon Pro 5700 XT (0 bytes allocated)
julia> a = MtlArray([1])
1-element MtlArray{Int64, 1}:
1
julia> a .+ 1
┌ Warning: Compilation of MetalLib to native code failed.
│ If you think this is a bug, please file an issue and attach /var/folders/3n/56fpv14n4wj0c1l1sb106pzw0000gn/T/jl_OUC1h1KIc6.metallib.
└ @ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:178
ERROR: MtlError: Compiler encountered an internal error (code 2, CompilerError)
Stacktrace:
[1] macro expansion
@ ~/.julia/packages/Metal/fQowO/lib/core/helpers.jl:68 [inlined]
[2] MtlComputePipelineState(d::MtlDevice, f::MtlFunction)
@ Metal.MTL ~/.julia/packages/Metal/fQowO/lib/core/compute_pipeline.jl:25
[3] mtlfunction_link(job::GPUCompiler.CompilerJob, compiled::NamedTuple{(:image, :entry), Tuple{Vector{UInt8}, String}})
@ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:172
[4] cached_compilation(cache::Dict{UInt64, Any}, job::GPUCompiler.CompilerJob, compiler::typeof(Metal.mtlfunction_compile), linker::typeof(Metal.mtlfunction_link))
@ GPUCompiler ~/.julia/packages/GPUCompiler/iaKrd/src/cache.jl:95
[5] mtlfunction(f::GPUArrays.var"#broadcast_kernel#15", tt::Type{Tuple{Metal.mtlKernelContext, MtlDeviceVector{Int64, 1}, Base.Broadcast.Broadcasted{Metal.MtlArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(+), Tuple{Base.Broadcast.Extruded{MtlDeviceVector{Int64, 1}, Tuple{Bool}, Tuple{Int64}}, Int64}}, Int64}}; name::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ Metal ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:143
[6] mtlfunction
@ ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:136 [inlined]
[7] macro expansion
@ ~/.julia/packages/Metal/fQowO/src/compiler/execution.jl:64 [inlined]
[8] #launch_heuristic#53
@ ~/.julia/packages/Metal/fQowO/src/gpuarrays.jl:14 [inlined]
[9] _copyto!
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:73 [inlined]
[10] copyto!
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:56 [inlined]
[11] copy
@ ~/.julia/packages/GPUArrays/EVTem/src/host/broadcast.jl:47 [inlined]
[12] materialize(bc::Base.Broadcast.Broadcasted{Metal.MtlArrayStyle{1}, Nothing, typeof(+), Tuple{MtlArray{Int64, 1}, Int64}})
@ Base.Broadcast ./broadcast.jl:860
[13] top-level scope
@ REPL[6]:1
[14] top-level scope
@ ~/.julia/packages/Metal/fQowO/src/initialization.jl:25
julia> device(a)
MtlDevice:
name: AMD Radeon Pro 5700 XT
lowpower: false
headless: false
removable: false
unified memory: false
registry id: 4294968934
transfer rate: 0
julia> task_local_storage()[:MtlDevice] = MtlDevice(1)
MtlDevice:
name: AMD Radeon Pro 5700 XT
lowpower: false
headless: false
removable: false
unified memory: false
registry id: 4294968934
transfer rate: 0
julia>
Int64 and Float64 are not supported and lead to those crashes. Try to convert the data to single-precision
b = convert(Array{Int32}, a)
1-element Vector{Int32}:
1
julia> b .+ 1
1-element Vector{Int64}:
2
b = convert(Array{Int32}, a) 1-element Vector{Int32}: 1
julia> b .+ 1 1-element Vector{Int64}: 2
... and? Of course CPU vectors support int64 addition, it's the GPU that's more limited here.
@PhilipVinc Int64 seems to work fine on my M1 though, and I can't imagine it not working since every literal in Julia is a 64-bit integer.
julia> using Metal
julia> a = MtlArray([1])
1-element MtlArray{Int64, 1}:
1
julia> b = convert(Array{Float32}, a)
1-element Vector{Float32}:
1.0
julia> b .+ 1
1-element Vector{Float32}:
2.0
Again, what are you trying to prove here? You're still broadcasting over a CPU vector. Please take the time to add some explanation, and not just dump code snippets; this is not helpful.