Multiprocess DataLoader doesn't work with sparse tensor as it'll try to access the underlying storage
Labels: module: sparse, module: dataloader, triaged

### 🐛 Describe the bug

# Summary
When using `DataLoader` with multiprocess loading to load a dataset with sparse tensor elements, it'll try to access the underlying storage of the tensor, but sparse tensor (COO, CSF etc) doesn't support accessing storage.

I've put the **minimal reproduction sample** in this colab notebook: https://colab.research.google.com/drive/16q_tzyUz5ylZSCcpzhJ52pxVSUpMZX-M#scrollTo=o0KeaWnVz9Hm&uniqifier=1 

## Case 1: default collation
When using default collate (auto_collate=True [here](https://github.com/pytorch/pytorch/blob/v2.0.1/torch/utils/data/dataloader.py#L366)), to collate on a sparse tensor, it'll attempt to access the `elem._typed_storage` [here](https://github.com/pytorch/pytorch/blob/v2.0.1/torch/utils/data/_utils/collate.py#L160), thus hitting error:
```

NotImplementedError: Caught NotImplementedError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
    return self.collate_fn(data)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py", line 265, in default_collate
    return collate(batch, collate_fn_map=default_collate_fn_map)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py", line 119, in collate
    return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/collate.py", line 160, in collate_tensor_fn
    storage = elem._typed_storage()._new_shared(numel, device=elem.device)
  File "/usr/local/lib/python3.10/dist-packages/torch/_tensor.py", line 238, in _typed_storage
    untyped_storage = self.untyped_storage()
NotImplementedError: Cannot access storage of SparseTensorImpl
```

## Case 2: Manual collation
Without auto collation, (set `batch_size=None` so that it'll use `default_convert` method, OR provide a `collate_fn`) we get around the issue in default_collate in case 1; But later on when the worker process is feeding into the `worker_result_queue` the loaded data, it'll again attemp to access the underlying storage, thus hitting
```
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/torch/multiprocessing/reductions.py", line 152, in reduce_tensor
    storage = tensor._typed_storage()
  File "/usr/lib/python3.10/multiprocessing/queues.py", line 244, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/usr/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
  File "/usr/local/lib/python3.10/dist-packages/torch/_tensor.py", line 238, in _typed_storage
    untyped_storage = self.untyped_storage()
NotImplementedError: Cannot access storage of SparseTensorImpl
Traceback (most recent call last):
  File "/usr/lib/python3.10/multiprocessing/queues.py", line 244, in _feed
    obj = _ForkingPickler.dumps(obj)
  File "/usr/lib/python3.10/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
  File "/usr/local/lib/python3.10/dist-packages/torch/multiprocessing/reductions.py", line 152, in reduce_tensor
    storage = tensor._typed_storage()
  File "/usr/local/lib/python3.10/dist-packages/torch/_tensor.py", line 238, in _typed_storage
    untyped_storage = self.untyped_storage()
NotImplementedError: Cannot access storage of SparseTensorImpl
```

So that anyway we cannot do multiprocess loading with sparse tensor.

### Versions

```
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 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.25.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.109+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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
Address sizes:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          2
On-line CPU(s) list:             0,1
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                      6
Model:                           79
Thread(s) per core:              2
Core(s) per socket:              1
Socket(s):                       1
Stepping:                        0
BogoMIPS:                        4399.99
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       32 KiB (1 instance)
L1i cache:                       32 KiB (1 instance)
L2 cache:                        256 KiB (1 instance)
L3 cache:                        55 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0,1
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Mmio stale data:   Vulnerable
Vulnerability Retbleed:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.23.5
[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 @SsnL @VitalyFedyunin @ejguan @dzhulgakov