torch.sparse_csr_tensor() stops gradients
Labels: module: sparse, triaged

### 🐛 Describe the bug

I would expect the following code to produce a gradient w.r.t. `a`, but instead it stops the gradient flow at `torch.sparse_csr_tensor()`.

```python
import torch

a = torch.randn(3, requires_grad=True)
b = torch.sparse_csr_tensor(
    torch.tensor([0, 2, 2, 3]),
    torch.tensor([0, 1, 2]),
    a,
)
print(b.grad_fn)
torch.sum(b).backward()
```

Accordingly, this is the output:
```
None

Traceback (most recent call last):
  File "/path/csr_grad.py", line 58, in <module>
    main_csr()
  File "/path/csr_grad.py", line 36, in main_csr
    torch.sum(b).backward()
  File "/venv/lib64/python3.11/site-packages/torch/_tensor.py", line 487, in backward
    torch.autograd.backward(
  File "/venv/lib64/python3.11/site-packages/torch/autograd/__init__.py", line 200, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
```

The same code works for COO tensors, but I need the performance benefits of CSR tensors.

### Versions

```
Collecting environment information...
PyTorch version: 2.0.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Fedora Linux 37 (KDE Plasma) (x86_64)
GCC version: (GCC) 12.2.1 20221121 (Red Hat 12.2.1-4)
Clang version: Could not collect
CMake version: version 3.26.1
Libc version: glibc-2.36

Python version: 3.11.2 (main, Feb  8 2023, 00:00:00) [GCC 12.2.1 20221121 (Red Hat 12.2.1-4)] (64-bit runtime)
Python platform: Linux-6.2.7-200.fc37.x86_64-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070 SUPER
Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.8.1
/usr/lib64/libcudnn_adv_infer.so.8.8.1
/usr/lib64/libcudnn_adv_train.so.8.8.1
/usr/lib64/libcudnn_cnn_infer.so.8.8.1
/usr/lib64/libcudnn_cnn_train.so.8.8.1
/usr/lib64/libcudnn_ops_infer.so.8.8.1
/usr/lib64/libcudnn_ops_train.so.8.8.1
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):                          16
On-line CPU(s) list:             0-15
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen 7 2700 Eight-Core Processor
CPU family:                      23
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU(s) scaling MHz:              67%
CPU max MHz:                     3200.0000
CPU min MHz:                     1550.0000
BogoMIPS:                        6387.18
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 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 sev sev_es
Virtualization:                  AMD-V
L1d cache:                       256 KiB (8 instances)
L1i cache:                       512 KiB (8 instances)
L2 cache:                        4 MiB (8 instances)
L3 cache:                        16 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-15
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
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] mypy-extensions==1.0.0
[pip3] numpy==1.24.2
[pip3] torch==2.0.0
[pip3] triton==2.0.0
[conda] Could not collect
```

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