Strange rand errors when sampling large matrices

[lpawela]

Describe the bug
Sampling large matrices (starting at dimension $5 \cdot 10^4$) with Float16 entries results in the following error:

ERROR: CUDA error: an illegal memory access was encountered (code 700, ERROR_ILLEGAL_ADDRESS)
Stacktrace:
 [1] throw_api_error(res::CUDA.cudaError_enum)
   @ CUDA ~/.julia/packages/CUDA/pCcGc/lib/cudadrv/libcuda.jl:27
 [2] check
   @ ~/.julia/packages/CUDA/pCcGc/lib/cudadrv/libcuda.jl:34 [inlined]
 [3] cuCtxSynchronize
   @ ~/.julia/packages/CUDA/pCcGc/lib/utils/call.jl:26 [inlined]
 [4] nonblocking_synchronize
   @ ~/.julia/packages/CUDA/pCcGc/lib/cudadrv/context.jl:329 [inlined]
 [5] device_synchronize()
   @ CUDA ~/.julia/packages/CUDA/pCcGc/lib/cudadrv/context.jl:319
 [6] top-level scope
   @ ~/.julia/packages/CUDA/pCcGc/src/initialization.jl:173

To reproduce

The Minimal Working Example (MWE) for this bug:

using CUDA
CUDA.rand(Float16, 5*10^4, 5*10^4)

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Expected behavior

Matrix available on the GPU

Version info

Details on Julia:

julia> versioninfo()
Julia Version 1.9.1
Commit 147bdf428cd (2023-06-07 08:27 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 26 × Intel(R) Xeon(R) Platinum 8480+
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, sapphirerapids)
  Threads: 1 on 26 virtual cores

Details on CUDA:

julia> CUDA.versioninfo()
CUDA runtime 12.1, artifact installation
CUDA driver 12.1
NVIDIA driver 525.85.12, originally for CUDA 12.0

CUDA libraries: 
- CUBLAS: 12.1.3
- CURAND: 10.3.2
- CUFFT: 11.0.2
- CUSOLVER: 11.4.5
- CUSPARSE: 12.1.0
- CUPTI: 18.0.0
- NVML: 12.0.0+525.85.12

Julia packages: 
- CUDA.jl: 4.3.2
- CUDA_Driver_jll: 0.5.0+1
- CUDA_Runtime_jll: 0.6.0+0
- CUDA_Runtime_Discovery: 0.2.2

Toolchain:
- Julia: 1.9.1
- LLVM: 14.0.6
- 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, 7.1, 7.2, 7.3, 7.4, 7.5
- Device capability support: sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86

1 device:
  0: NVIDIA H100 PCIe (sm_90, 79.324 GiB / 79.647 GiB available)

Additional context

Works correctly for Float32.

using CUDA
CUDA.rand(Float32, 10^5, 10^5) #works

Tested this on the instance mentioned in this report and others with RTX3090 and RTX4090. All have the same issue.

[maleadt]

That's curious. I can reproduce, I'll take a look.

As for the workaround using Float32, that relies on the CURAND library and not the native RNG, so it's an entirely different code path.

[maleadt]

#1969 should fix this, please verify.

Note that it's possible you'll run into similar issues with other array operations, as we haven't been careful when using 32-bit integer indices, and huge arrays like the one you allocated here may trip up some of those kernels (#1968).