Error when using sparse_coo tensor with optimizer
Labels: module: sparse, triaged

### 🐛 Describe the bug

The problem when I using "sparse_coo" tensor

```
import torch
print(torch.__version__)
cuda = torch.device('cuda:0')

a_o = torch.tensor([-3, -4]
                   , dtype=torch.float32, device=cuda)

a_p = torch.tensor([[-3, 1],
                    [ 1,-1],
                    [ 1, 2],]
                   , dtype=torch.float32, device=cuda)

#  /->
""" without this line everything works,
but I need to compress terribly large matrices
so that the data fits in the memory of the GPU
"""
a_p = a_p.to_sparse_coo()
#  <-/

b_p = torch.tensor([  0, -2, -14,]
                   , dtype=torch.float32, device=cuda)

x = torch.zeros([len(a_o)], dtype=torch.float32, device=cuda)
x.requires_grad = True

def model_lp(x):
    return (torch.mul(a_o, x).sum()) + (torch.add(b_p, torch.sum(torch.mul(a_p, x), dim=1)).relu().square().sum()*10_000)

optimizer = torch.optim.Adam([x], lr=3e-3)

for step in range(10000):
    pred = model_lp(x)  
    optimizer.zero_grad()  
    pred.backward()  
    optimizer.step() 
    if step % 1000 == 0:
        print(f'step {step}: f(x)={pred.item()}, x={x.tolist()},' ) 
```
Error message 

```
2.0.1+cu118
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
[<ipython-input-54-8f4f7b22619a>](https://localhost:8080/#) in <cell line: 31>()
     32     pred = model_lp(x)
     33     optimizer.zero_grad()
---> 34     pred.backward()
     35     optimizer.step()
     36     if step % 1000 == 0:

1 frames
[/usr/local/lib/python3.10/dist-packages/torch/_tensor.py](https://localhost:8080/#) in backward(self, gradient, retain_graph, create_graph, inputs)
    485                 inputs=inputs,
    486             )
--> 487         torch.autograd.backward(
    488             self, gradient, retain_graph, create_graph, inputs=inputs
    489         )

[/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py](https://localhost:8080/#) in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    198     # some Python versions print out the first line of a multi-line function
    199     # calls in the traceback and some print out the last line
--> 200     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    201         tensors, grad_tensors_, retain_graph, create_graph, inputs,
    202         allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass

NotImplementedError: Could not run 'aten::view' with arguments from the 'SparseCUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'aten::view' is only available for these backends: [CPU, CUDA, Meta, QuantizedCPU, QuantizedCUDA, MkldnnCPU, NestedTensorCPU, NestedTensorCUDA, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradHIP, AutogradXLA, AutogradMPS, AutogradIPU, AutogradXPU, AutogradHPU, AutogradVE, AutogradLazy, AutogradMeta, AutogradMTIA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, AutogradNestedTensor, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].

CPU: registered at aten/src/ATen/RegisterCPU.cpp:31034 [kernel]
CUDA: registered at aten/src/ATen/RegisterCUDA.cpp:43986 [kernel]
Meta: registered at aten/src/ATen/RegisterMeta.cpp:26824 [kernel]
QuantizedCPU: registered at aten/src/ATen/RegisterQuantizedCPU.cpp:929 [kernel]
QuantizedCUDA: registered at aten/src/ATen/RegisterQuantizedCUDA.cpp:459 [kernel]
MkldnnCPU: registered at aten/src/ATen/RegisterMkldnnCPU.cpp:507 [kernel]
NestedTensorCPU: registered at aten/src/ATen/RegisterNestedTensorCPU.cpp:603 [kernel]
NestedTensorCUDA: registered at aten/src/ATen/RegisterNestedTensorCUDA.cpp:744 [kernel]
BackendSelect: fallthrough registered at ../aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:144 [backend fallback]
FuncTorchDynamicLayerBackMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:491 [backend fallback]
Functionalize: registered at aten/src/ATen/RegisterFunctionalization_3.cpp:22788 [kernel]
Named: registered at ../aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: fallthrough registered at ../aten/src/ATen/ConjugateFallback.cpp:21 [kernel]
Negative: fallthrough registered at ../aten/src/ATen/native/NegateFallback.cpp:23 [kernel]
ZeroTensor: registered at aten/src/ATen/RegisterZeroTensor.cpp:161 [kernel]
ADInplaceOrView: registered at ../torch/csrc/autograd/generated/ADInplaceOrViewType_1.cpp:5017 [kernel]
AutogradOther: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradCPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradCUDA: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradHIP: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradXLA: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradMPS: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradIPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradXPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradHPU: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradVE: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradLazy: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradMeta: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradMTIA: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradPrivateUse1: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradPrivateUse2: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradPrivateUse3: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:16043 [autograd kernel]
AutogradNestedTensor: registered at ../torch/csrc/autograd/generated/VariableType_3.cpp:17036 [kernel]
Tracer: registered at ../torch/csrc/autograd/generated/TraceType_3.cpp:14198 [kernel]
AutocastCPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:487 [backend fallback]
AutocastCUDA: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:354 [backend fallback]
FuncTorchBatched: registered at ../aten/src/ATen/functorch/BatchRulesViews.cpp:559 [kernel]
FuncTorchVmapMode: fallthrough registered at ../aten/src/ATen/functorch/VmapModeRegistrations.cpp:28 [backend fallback]
Batched: registered at ../aten/src/ATen/LegacyBatchingRegistrations.cpp:1077 [kernel]
VmapMode: fallthrough registered at ../aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at ../aten/src/ATen/functorch/TensorWrapper.cpp:210 [backend fallback]
PythonTLSSnapshot: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:152 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:487 [backend fallback]
PythonDispatcher: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:148 [backend fallback]
```


### Versions

```
--2023-08-13 17:58:10--  https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 21653 (21K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py      100%[===================>]  21.15K  --.-KB/s    in 0.009s  

2023-08-13 17:58:10 (2.39 MB/s) - ‘collect_env.py’ saved [21653/21653]

Collecting environment information...
PyTorch version: 2.0.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.25.2
Libc version: glibc-2.31

Python version: 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070 Laptop GPU
Nvidia driver version: 536.67
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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:                   48 bits physical, 48 bits virtual
CPU(s):                          16
On-line CPU(s) list:             0-15
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           80
Model name:                      AMD Ryzen 7 5800H with Radeon Graphics
Stepping:                        0
CPU MHz:                         3193.912
BogoMIPS:                        6387.82
Virtualization:                  AMD-V
Hypervisor vendor:               Microsoft
Virtualization type:             full
L1d cache:                       256 KiB
L1i cache:                       256 KiB
L2 cache:                        4 MiB
L3 cache:                        16 MiB
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:          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, PBRSB-eIBRS Not affected
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 tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm

Versions of relevant libraries:
[pip3] numpy==1.22.4
[pip3] torch==2.0.1+cu118
[pip3] torchaudio==2.0.2+cu118
[pip3] torchdata==0.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2+cu118
[pip3] triton==2.0.0
[conda] Could not collect
```

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