rand! does not work for tuple types
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bug
- Dominant language
- Julia
- Stars
- 344
- Forks
- 79
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- 2d 19h
- Merged PRs (30d)
- 25
Description
Questionnaire
- Does ROCm works for you outside of Julia, e.g. C/C++/Python?
Yes.
- Post output of
rocminfo.
ROCk module version 6.16.13 is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.14
Runtime Ext Version: 1.6
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD EPYC 7452 32-Core Processor
Uuid: CPU-XX
Marketing Name: AMD EPYC 7452 32-Core Processor
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32768(0x8000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2350
BDFID: 0
Internal Node ID: 0
Compute Unit: 64
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Memory Properties:
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 263766724(0xfb8c2c4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 263766724(0xfb8c2c4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 263766724(0xfb8c2c4) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx1100
Uuid: GPU-8459fddd3785d451
Marketing Name: AMD Radeon RX 7900 XTX
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
ASIC Revision: 0(0x0)
Cacheline Size: 128(0x80)
Max Clock Freq. (MHz): 2371
BDFID: 49920
Internal Node ID: 1
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
WatchPts on Addr. Ranges:4
Coherent Host Access: FALSE
Memory Properties:
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 542
SDMA engine uCode:: 24
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size: 25149440(0x17fc000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Recommended Granule:0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***
- Post output of
AMDGPU.versioninfo()if possible.
[ Info: AMDGPU versioninfo
┌───────────┬──────────────────┬───────────┬─────────────────────────────────────────────────────────────────────────────────────────┐
│ Available │ Name │ Version │ Path │
├───────────┼──────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────┤
│ + │ LLD │ - │ /opt/rocm-6.2.4/lib/llvm/bin/ld.lld │
│ + │ Device Libraries │ - │ /home/hernan68/.julia/artifacts/b46ab46ef568406312e5f500efb677511199c2f9/amdgcn/bitcode │
│ + │ HIP │ 6.2.41134 │ /opt/rocm-6.2.4/lib/libamdhip64.so │
│ + │ rocBLAS │ 4.2.4 │ /opt/rocm-6.2.4/lib/librocblas.so │
│ + │ rocSOLVER │ 3.26.2 │ /opt/rocm-6.2.4/lib/librocsolver.so │
│ + │ rocSPARSE │ 3.2.1 │ /opt/rocm-6.2.4/lib/librocsparse.so │
│ + │ rocRAND │ 2.10.5 │ /opt/rocm-6.2.4/lib/librocrand.so │
│ + │ rocFFT │ 1.0.30 │ /opt/rocm-6.2.4/lib/librocfft.so │
│ - │ MIOpen │ - │ - │
└───────────┴──────────────────┴───────────┴─────────────────────────────────────────────────────────────────────────────────────────┘
[ Info: AMDGPU devices
┌────┬────────────────────────┬──────────┬───────────┬────────────┬───────────────┐
│ Id │ Name │ GCN arch │ Wavefront │ Memory │ Shared Memory │
├────┼────────────────────────┼──────────┼───────────┼────────────┼───────────────┤
│ 1 │ AMD Radeon RX 7900 XTX │ gfx1100 │ 32 │ 23.984 GiB │ 64.000 KiB │
└────┴────────────────────────┴──────────┴───────────┴────────────┴───────────────┘
Reproducing the bug
- Describe what's not working.
Seems like there is some sampler implementation missing if one tries to generate random NTuple in-place using Random.rand! with AMDGPU arrays.
The same works fine with CUDA.jl.
- Provide MWE to reproduce it (if possible).
using AMDGPU
using Random
a_h = Vector{NTuple{2,Float32}}(undef,1)
a_d = ROCArray(a_h)
rand!(a_d)
Error message
ERROR: MethodError: no method matching Random.Sampler(::Type{GPUArrays.RNG}, ::Random.SamplerType{UInt32}, ::Val{1})
This error has been manually thrown, explicitly, so the method may exist but be intentionally marked as unimplemented.
Closest candidates are:
Random.Sampler(::Type{<:AbstractRNG}, ::Random.Sampler, ::Union{Val{1}, Val{Inf}})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:147
Random.Sampler(::Type{<:AbstractRNG}, ::Any, ::Union{Val{1}, Val{Inf}})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:183
Random.Sampler(::Type{<:AbstractRNG}, ::BitSet, ::Union{Val{1}, Val{Inf}})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:488
...
Stacktrace:
[1] Random.Sampler(T::Type{GPUArrays.RNG}, sp::Random.SamplerType{UInt32}, r::Val{1})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:147
[2] Random.Sampler(rng::GPUArrays.RNG, x::Random.SamplerType{UInt32}, r::Val{1})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:141
[3] rand(rng::GPUArrays.RNG, X::Random.SamplerType{UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:255
[4] rand(rng::GPUArrays.RNG, ::Type{UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:258
[5] rand(r::GPUArrays.RNG, ::Random.SamplerTrivial{Random.UInt23Raw{UInt32}, UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:112
[6] rand(rng::GPUArrays.RNG, X::Random.UInt23Raw{UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:255
[7] rand(r::GPUArrays.RNG, ::Random.SamplerTrivial{Random.UInt23{UInt32}, UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:124
[8] rand(rng::GPUArrays.RNG, X::Random.UInt23{UInt32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:255
[9] rand(r::GPUArrays.RNG, ::Random.SamplerTrivial{Random.CloseOpen01{Float32}, Float32})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:29
[10] (::Random.var"#rand##0#rand##1"{GPUArrays.RNG, Random.SamplerTag{Ref{Tuple{…}}, Tuple{Random.SamplerTrivial{…}}, Tuple{Float32, Float32}}})(i::Int64)
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:187
[11] ntuple
@ ./ntuple.jl:51 [inlined]
[12] rand(rng::GPUArrays.RNG, sp::Random.SamplerTag{Ref{Tuple{…}}, Tuple{Random.SamplerTrivial{…}}, Tuple{Float32, Float32}})
@ Random ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/generation.jl:187
[13] rand!
@ ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:273 [inlined]
[14] rand!
@ ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:269 [inlined]
[15] rand
@ ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:290 [inlined]
[16] rand
@ ~/.julia/juliaup/julia-1.12.4+0.x64.linux.gnu/share/julia/stdlib/v1.12/Random/src/Random.jl:293 [inlined]
[17] rand!
@ ~/.julia/packages/GPUArrays/lOLhM/src/host/random.jl:116 [inlined]
[18] rand!(A::ROCArray{Tuple{Float32, Float32}, 1, AMDGPU.Runtime.Mem.HIPBuffer})
@ AMDGPU ~/.julia/packages/AMDGPU/1sgkL/src/random.jl:53
[19] top-level scope
@ REPL[7]:1
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the provided ROCArray MWE and the stack trace through Random.rand! and GPUArrays.RNG. Compare the tuple-generation path with the working CUDA.jl behavior, then inspect the GPUArrays.RNG sampler dispatch involved in Random.Sampler. Done means rand!(a_d) succeeds for Vector{NTuple{2,Float32}} without the reported MethodError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- hpc
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100