Repository: pytorch
Issue: RuntimeError: cuSPARSELT not supported on your machine. When I was calling: torch._cslt_compress()
Labels: module: binaries,module: sparse,triaged

### 🐛 Describe the bug

I have this issue.
When I was running:

>import torch
>from torch.sparse import to_sparse_semi_structured
>A = torch.Tensor([0, 0, 1, 1]).tile((128, 32)).half().cuda()
>A_sparse = to_sparse_semi_structured(A)

The error occurred:

>/opt/anaconda3/envs/sm_sparse/lib/python3.8/site-packages/torch/sparse/semi_structured.py:86: >UserWarning: The PyTorch API of SparseSemiStructuredTensor is in prototype stage and will change in >the near future. Please open a Github issue for features requests and see our documentation on the >torch.sparse module for further information about the project.
>warnings.warn(
>Traceback (most recent call last):
>File "<stdin>", line 1, in <module>
>File "/opt/anaconda3/envs/sm_sparse/lib/python3.8/site-packages/torch/sparse/semi_structured.py", line 434, in to_sparse_semi_structured
>return SparseSemiStructuredTensor(original_tensor, original_shape=original_tensor.shape, transposed=transposed)
>File "/opt/anaconda3/envs/sm_sparse/lib/python3.8/site-packages/torch/sparse/semi_structured.py", line 212, in init
>compressed_tensor = torch._cslt_compress(original_tensor)
>RuntimeError: cuSPARSELT not supported on your machine.


### Versions

My relevant versions are:

PyTorch version: 2.1.0
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.1 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.18 (default, Sep 11 2023, 13:40:15)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-113-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A6000
GPU 1: NVIDIA RTX A6000
GPU 2: NVIDIA RTX A6000
GPU 3: NVIDIA RTX A6000
GPU 4: NVIDIA A100 80GB PCIe
GPU 5: NVIDIA A100 80GB PCIe
GPU 6: NVIDIA A100 80GB PCIe
GPU 7: NVIDIA RTX A6000
GPU 8: NVIDIA A100 80GB PCIe

Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.6.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.4.0
/usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.4.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   43 bits physical, 48 bits virtual
CPU(s):                          256
On-line CPU(s) list:             0-254
Off-line CPU(s) list:            255
Thread(s) per core:              1
Core(s) per socket:              64
Socket(s):                       2
NUMA node(s):                    8
Vendor ID:                       AuthenticAMD
CPU family:                      23
Model:                           49
Model name:                      AMD EPYC 7H12 64-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         2117.314
CPU max MHz:                     2600.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5200.05
Virtualization:                  AMD-V
L1d cache:                       2 MiB
L1i cache:                       2 MiB
L2 cache:                        32 MiB
L3 cache:                        256 MiB
NUMA node0 CPU(s):               0-15,128-143
NUMA node1 CPU(s):               16-31,144-159
NUMA node2 CPU(s):               32-47,160-175
NUMA node3 CPU(s):               48-63,176-191
NUMA node4 CPU(s):               64-79,192-207
NUMA node5 CPU(s):               80-95,208-223
NUMA node6 CPU(s):               96-111,224-239
NUMA node7 CPU(s):               112-127,240-254
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd mba sev ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.1.0
[pip3] torchaudio==2.1.0
[pip3] torchvision==0.16.0
[pip3] triton==2.1.0
[conda] blas                      1.0                         mkl
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344
[conda] mkl-service               2.4.0            py38h5eee18b_1
[conda] mkl_fft                   1.3.8            py38h5eee18b_0
[conda] mkl_random                1.2.4            py38hdb19cb5_0
[conda] numpy                     1.24.3           py38hf6e8229_1
[conda] numpy-base                1.24.3           py38h060ed82_1
[conda] pytorch                   2.1.0           py3.8_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch                     2.1.1                    pypi_0    pypi
[conda] torchaudio                2.1.1                    pypi_0    pypi
[conda] torchtriton               2.1.0                      py38    pytorch
[conda] torchvision               0.16.1                   pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypi


cc @ezyang @gchanan @zou3519 @kadeng @seemethere @malfet @osalpekar @atalman @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer