CSR matrix add_ error with RuntimeError: CUDA error: kernel launch failure when calling cusparseXcsrgeam2Nnz
Labels: module: sparse, module: cuda, triaged

### 🐛 Describe the bug

I am working on a model built with linear layers made with CSR matrices for the weights. During training I encountered this error which I have reproduced in the example below, the original error I found to be caused by `lib/python3.10/site-packages/torch/optim/sgd.py line 269 in _single_tensor_sgd param.add_(d_p, alpha=-lr)`. One aspect of the error which I haven't been able to reproduce is its relationship with the sparsity of the sparse linear layers in my model, as in the model I am working with this error only seems to happen with 0.99 or higher sparsity. I have experienced this issue with both 1.13.1+cu117 and nightly 2.0.0.dev20230213+cu117.

Example:
```python
import torch
import time
print(torch.version.__version__)

nz = 10000
# the error persists no matter the sparsity in this example
sparsity = 0.0
density = 1.0 - sparsity

indices = torch.randint(0, nz, (2, int(nz*density)))
values = torch.randn((int(nz*density)))

x = torch.sparse_coo_tensor(indices, values, dtype=torch.float,device='cuda').coalesce().to_sparse_csr()

# A loop is used as this error often does not happen on the first add_ call
for i in range(100):
    x.add_(x)
    time.sleep(0.05) # Used to prevent async call errors
```


Error traceback:
```
Traceback (most recent call last):
  File "/.../temp.py", line 15, in <module>
    x = x.add_(x)
RuntimeError: CUDA error: kernel launch failure when calling `cusparseXcsrgeam2Nnz( handle, m, n, descrA, nnzA, csrSortedRowPtrA, csrSortedColIndA, descrB, nnzB, csrSortedRowPtrB, csrSortedColIndB, descrC, csrSortedRowPtrC, nnzTotalDevHostPtr, workspace)`
```

### Versions

Collecting environment information...
PyTorch version: 2.0.0.dev20230213+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Manjaro Linux (x86_64)
GCC version: (GCC) 12.2.0
Clang version: 14.0.6
CMake version: version 3.25.0
Libc version: glibc-2.36

Python version: 3.10.8 (main, Nov  1 2022, 14:18:21) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-5.15.85-1-MANJARO-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1080
Nvidia driver version: 525.60.11
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.6.0
/usr/lib/libcudnn_adv_infer.so.8.6.0
/usr/lib/libcudnn_adv_train.so.8.6.0
/usr/lib/libcudnn_cnn_infer.so.8.6.0
/usr/lib/libcudnn_cnn_train.so.8.6.0
/usr/lib/libcudnn_ops_infer.so.8.6.0
/usr/lib/libcudnn_ops_train.so.8.6.0
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
Address sizes:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          12
On-line CPU(s) list:             0-11
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen 5 2600 Six-Core Processor
CPU family:                      23
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              6
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU(s) scaling MHz:              65%
CPU max MHz:                     3400.0000
CPU min MHz:                     1550.0000
BogoMIPS:                        6801.44
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 rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev sev_es
Virtualization:                  AMD-V
L1d cache:                       192 KiB (6 instances)
L1i cache:                       384 KiB (6 instances)
L2 cache:                        3 MiB (6 instances)
L3 cache:                        16 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-11
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Mitigation; untrained return thunk; SMT vulnerable
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, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-triton==2.0.0+0d7e753227
[pip3] torch==2.0.0.dev20230213+cu117
[pip3] torchaudio==2.0.0.dev20230215+cu117
[pip3] torchvision==0.15.0.dev20230215+cu117
[conda] blas                      1.0                         mkl  
[conda] mkl                       2020.0                      166  
[conda] mkl-service               2.3.0            py37he904b0f_0  
[conda] mkl_fft                   1.0.15           py37ha843d7b_0  
[conda] mkl_random                1.1.0            py37hd6b4f25_0  
[conda] numpy                     1.18.1           py37h4f9e942_0  
[conda] numpy-base                1.18.1           py37hde5b4d6_1  
[conda] numpydoc                  0.9.2                      py_0  


Collecting environment information...
PyTorch version: 1.13.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Manjaro Linux (x86_64)
GCC version: (GCC) 12.2.0
Clang version: 14.0.6
CMake version: version 3.25.1
Libc version: glibc-2.36

Python version: 3.10.8 (main, Nov  1 2022, 14:18:21) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-5.15.85-1-MANJARO-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1080
Nvidia driver version: 525.60.11
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.6.0
/usr/lib/libcudnn_adv_infer.so.8.6.0
/usr/lib/libcudnn_adv_train.so.8.6.0
/usr/lib/libcudnn_cnn_infer.so.8.6.0
/usr/lib/libcudnn_cnn_train.so.8.6.0
/usr/lib/libcudnn_ops_infer.so.8.6.0
/usr/lib/libcudnn_ops_train.so.8.6.0
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
Address sizes:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          12
On-line CPU(s) list:             0-11
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen 5 2600 Six-Core Processor
CPU family:                      23
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              6
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU(s) scaling MHz:              63%
CPU max MHz:                     3400.0000
CPU min MHz:                     1550.0000
BogoMIPS:                        6801.44
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 rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev sev_es
Virtualization:                  AMD-V
L1d cache:                       192 KiB (6 instances)
L1i cache:                       384 KiB (6 instances)
L2 cache:                        3 MiB (6 instances)
L3 cache:                        16 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-11
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Mitigation; untrained return thunk; SMT vulnerable
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, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] torch==1.13.1
[pip3] torch-scatter==2.1.0+pt113cu117
[pip3] torch-sparse==0.6.16+pt113cu117
[pip3] torchvision==0.14.1
[pip3] torchviz==0.0.2
[conda] blas                      1.0                         mkl  
[conda] mkl                       2020.0                      166  
[conda] mkl-service               2.3.0            py37he904b0f_0  
[conda] mkl_fft                   1.0.15           py37ha843d7b_0  
[conda] mkl_random                1.1.0            py37hd6b4f25_0  
[conda] numpy                     1.18.1           py37h4f9e942_0  
[conda] numpy-base                1.18.1           py37hde5b4d6_1  
[conda] numpydoc                  0.9.2                      py_0 

cc @alexsamardzic @nikitaved @pearu @cpuhrsch @amjames @bhosmer @ngimel