Sparse matrix addition not working

[KlimkinND]

Self-explanatory. MWE below:

julia> using CUDA, SparseArrays

julia> CUDA.versioninfo()
CUDA toolkit 10.1.243, artifact installation
CUDA driver 10.1.0
NVIDIA driver 418.67.0

Libraries: 
- CUBLAS: 10.2.1
- CURAND: 10.1.1
- CUFFT: 10.1.1
- CUSOLVER: 10.2.0
- CUSPARSE: 10.3.0
- CUPTI: 12.0.0
- NVML: 10.0.0+418.67
- CUDNN: 8.0.4 (for CUDA 10.1.0)
- CUTENSOR: 1.2.1 (for CUDA 10.1.0)

Toolchain:
- Julia: 1.5.2
- LLVM: 9.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4
- Device support: sm_30, sm_32, sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75

1 device:
  0: Tesla P100-PCIE-16GB (sm_60, 15.605 GiB / 15.899 GiB available)

julia> A = B = CUSPARSE.CuSparseMatrixCSR(sprandn(100, 100, 0.01));

julia> A+B
ERROR: UndefVarError: geam not defined
Stacktrace:
 [1] +(::CUDA.CUSPARSE.CuSparseMatrixCSR{Float64}, ::CUDA.CUSPARSE.CuSparseMatrixCSR{Float64}) at /home/n.klimkin/.julia/packages/CUDA/0p5fn/lib/cusparse/interfaces.jl:49
 [2] top-level scope at REPL[7]:1

This error also reproduces when downgrading CUDA to v. 1.3.3. Julia version info:

Julia Version 1.5.2
Commit 539f3ce943 (2020-09-23 23:17 UTC)
Platform Info:
  OS: Linux (x86_64-pc-linux-gnu)
  CPU: Intel(R) Xeon(R) CPU E5-2695 v2 @ 2.40GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, ivybridge)
Environment:
  JULIA_NUM_THREADS = 32

[learning-chip]

cuSPARSE has csrgeam and csrgeam2 for adding two sparse matrices. See CuPy source code cusparse.py for how to wrap these kernels.

[kshyatt]

Has this been resolved now that csrgeam2 is wrapped?

[maleadt]

Going to assume this is fixed, please re-open if it isn't.