Repository: pytorch
Issue: Bug in element-wise multiplication of `torch.sparse_csr_tensor`s on GPU - 0's in result considered significant - PyTorch 2.1.1
Labels: module: sparse,triaged,module: regression

### 🐛 Describe the bug

**Problem:**
The problem occurs when using the PyTorch version 2.1.1. The result of element-wise multiplication between two `torch.sparse_csr_tensor`s consider 0s as significant values and retain them in their sparse form. This is not the expected behavior. Moreover, this bug occurs only when the operation is run on a gpu.

**Code:**
```py
import torch

A = [[0, 0], 
     [1, 0], 
     [0, 2]]

B = [[1, 0],
     [0, 0],
     [2, 3]]

a = torch.tensor(A, device='cuda:0').float().to_sparse_csr()
b = torch.tensor(B, device='cuda:0').float().to_sparse_csr()

# a = torch.tensor(A).float().to_sparse_csr() # for runs on cpu
# b = torch.tensor(B).float().to_sparse_csr()

print("Torch version: ", torch.__version__)
print(a * b)
```

**Output (Torch 2.0.0 - GPU):** _Expected Output_
```
Torch version:  2.0.0
tensor(crow_indices=tensor([0, 0, 0, 1]),
       col_indices=tensor([1]),
       values=tensor([6.]), device='cuda:0', size=(3, 2), nnz=1, layout=torch.sparse_csr)
```
**Output (Torch 2.1.1 - CPU):** _Expected Output_
```
Torch version:  2.1.1
tensor(crow_indices=tensor([0, 0, 0, 1]),
       col_indices=tensor([1]),
       values=tensor([6.]), size=(3, 2), nnz=1, layout=torch.sparse_csr)
```
**Output (Torch 2.1.1 - GPU):** 
```
Torch version:  2.1.1
tensor(crow_indices=tensor([0, 0, 1, 2]),
       col_indices=tensor([0, 1]),
       values=tensor([0., 6.]), device='cuda:0', size=(3, 2), nnz=2, layout=torch.sparse_csr)
```
The resulting 0 from multiplication is considered significant.

### Versions

```
PyTorch version: 2.1.1
Is debug build: False
CUDA used to build PyTorch: 12.1
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: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.11.5 (main, Sep 11 2023, 13:54:46) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
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 3060 Laptop GPU
Nvidia driver version: 526.98
cuDNN version: Could not collect
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:                      48 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 5800H with Radeon Graphics
CPU family:                         25
Model:                              80
Thread(s) per core:                 2
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           6387.84
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
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          256 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           4 MiB (8 instances)
L3 cache:                           16 MiB (1 instance)
Vulnerability Gather data sampling: Not affected
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 rstack overflow: Mitigation; safe RET, no microcode
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

Versions of relevant libraries:
[pip3] numpy==1.26.0
[pip3] torch==2.1.1
[pip3] torchaudio==2.1.1
[pip3] torchvision==0.16.1
[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           py311h5eee18b_1
[conda] mkl_fft                   1.3.8           py311h5eee18b_0
[conda] mkl_random                1.2.4           py311hdb19cb5_0
[conda] numpy                     1.26.0          py311h08b1b3b_0
[conda] numpy-base                1.26.0          py311hf175353_0
[conda] pytorch                   2.1.1           py3.11_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.1.1               py311_cu121    pytorch
[conda] torchtriton               2.1.0                     py311    pytorch
[conda] torchvision               0.16.1              py311_cu121    pytorch
```

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