SparseSemiStructuredTensors are constructed differently from the original dense ones
Labels: module: sparse, triaged

### 🐛 Describe the bug

I'm working on semi-structured sparsity using the recently introduced features of SparseSemiStructuredTensors.

However, I have encountered inconsistent results between the dense (i.e., HalfTensor) and sparse (i.e., SparseSemiStructuredTensors) formats of 2:4 pruned weights.
My models almost always perform worse when using sparse formats than dense formats.
Moreover, when I reconstructed a dense tensor from a sparse tensor, I found several weights were indexed differently.
There may exist some errors in encoding indices resulting in these inconsistent behaviors.

Here's a minimal code for reproducing the problem.

```
import torch
from torch.sparse import to_sparse_semi_structured

a = torch.randn(3072, 768).cuda().half()/100
pruning_inds = a.abs().view(-1, 4).argsort(dim=1)[:, :2]

a.view(-1, 4).scatter_(1, pruning_inds, torch.zeros_like(pruning_inds).half())

b = to_sparse_semi_structured(a).to_dense()

print(((a-b)**2).sum())
print(a[a!=b][:32])
print(b[a!=b][:32])
```

Here's my result of running the above code. It can be clearly found that the remaining weights are positioned wrong.
```
tensor(239.8750, device='cuda:0', dtype=torch.float16)
tensor([ 0.0000, -0.0087,  0.0000,  0.0162,  0.0000, -0.0073,  0.0163,  0.0000,                                                                                                                                                       
         0.0000,  0.0155,  0.0000, -0.0177,  0.0128,  0.0000,  0.0000, -0.0084,                                                                                                                                                       
        -0.0058,  0.0000,  0.0000,  0.0124,  0.0000,  0.0093,  0.0000,  0.0060,                                                                                                                                                       
         0.0078,  0.0145,  0.0000, -0.0056,  0.0000,  0.0157,  0.0146,  0.0000],                                                                                                                                                      
       device='cuda:0', dtype=torch.float16)
tensor([-0.0087,  0.0000,  0.0162,  0.0000, -0.0073,  0.0000,  0.0000,  0.0163,                                                                                                                                                       
         0.0155,  0.0000, -0.0177,  0.0000,  0.0000,  0.0128, -0.0084,  0.0000,                                                                                                                                                       
         0.0000, -0.0058,  0.0124,  0.0000,  0.0093,  0.0000,  0.0060,  0.0000,
         0.0000,  0.0078,  0.0145,  0.0000, -0.0056,  0.0000,  0.0157,  0.0146],
       device='cuda:0', dtype=torch.float16)
```

Thanks.

### Versions

```
Collecting environment information...                                                                                                                                                                                                 
PyTorch version: 2.2.0a0+gitb193f29                                                                                                                                                                                                   
Is debug build: False                                                                                                                                                                                                                 
CUDA used to build PyTorch: 12.2                                                                                                                                                                                                      
ROCM used to build PyTorch: N/A                                                                                                                                                                                                       

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.31

Python version: 3.10.12 (main, Jul  5 2023, 18:54:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-162-generic-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 RTX A5000
GPU 1: NVIDIA RTX A5000
GPU 2: NVIDIA RTX A5000
GPU 3: NVIDIA RTX A5000

Nvidia driver version: 535.86.10
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4                                                                                                                                                                                           
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4                                                                                                                                                                                 
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4                                                                                                                                                                                 
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4                                                                                                                                                                                 
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4
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:                      46 bits physical, 48 bits virtual
CPU(s):                             36
On-line CPU(s) list:                0-35
Thread(s) per core:                 2
Core(s) per socket:                 18
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz                                                                                                                                                        
Stepping:                           7                                                                                                                                                                                                 
CPU MHz:                            1200.079                                                                                                                                                                                          
CPU max MHz:                        4800.0000                                                                                                                                                                                         
CPU min MHz:                        1200.0000                                                                                                                                                                                         
BogoMIPS:                           6000.00                                                                                                                                                                                           
Virtualization:                     VT-x                                                                                                                                                                                              
L1d cache:                          576 KiB                                                                                                                                                                                           
L1i cache:                          576 KiB                                                                                                                                                                                           
L2 cache:                           18 MiB
L3 cache:                           24.8 MiB
NUMA node0 CPU(s):                  0-35
Vulnerability Gather data sampling: Mitigation; Microcode 
Vulnerability Itlb multihit:        KVM: Mitigation: Split huge pages
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
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; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; TSX disabled
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] pytorch-triton==2.1.0+e6216047b8
[pip3] torch==2.2.0a0+gitb193f29
[pip3] torchaudio==2.2.0.dev20230906+cu121
[pip3] torchvision==0.16.0.dev20230906+cu121
[conda] magma-cuda121             2.6.1                         1    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46343  
[conda] mkl-include               2023.1.0         h06a4308_46343  
[conda] numpy                     1.25.2                   pypi_0    pypi
[conda] pytorch-triton            2.1.0+e6216047b8          pypi_0    pypi
[conda] torch                     2.2.0a0+gitb193f29           dev_0    <develop>
[conda] torchaudio                2.2.0.dev20230906+cu121          pypi_0    pypi
[conda] torchvision               0.16.0.dev20230906+cu121          pypi_0    pypi
```

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