Repository: pytorch
Issue: Pruning under channels_last format
Labels: triaged,module: memory format,module: pruning

### 🐛 Describe the bug

Pruning do not seem to be compatible with models stored in channels_last format because torch.view() is currently used, and it requires a contiguous input tensor.

Minimal Working Example : 
```
import torch
from torch.nn.utils import prune
model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
model.to(memory_format=torch.channels_last)
for name, module in model.named_modules():
            if isinstance(module, torch.nn.Conv2d):
                prune.l1_unstructured(module, name="weight", amount=0.5)
                break
```
Error log:
```
---------------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/user/scripts/mwe.py", line 8, in <module>
    prune.l1_unstructured(module, name="weight", amount=0.5)
  File "/home/user/.local/lib/python3.9/site-packages/torch/nn/utils/prune.py", line 923, in l1_unstructured
    L1Unstructured.apply(
  File "/home/user/.local/lib/python3.9/site-packages/torch/nn/utils/prune.py", line 562, in apply
    return super(L1Unstructured, cls).apply(
  File "/home/user/.local/lib/python3.9/site-packages/torch/nn/utils/prune.py", line 205, in apply
    raise e
  File "/home/user/.local/lib/python3.9/site-packages/torch/nn/utils/prune.py", line 191, in apply
    mask = method.compute_mask(importance_scores, default_mask=default_mask)
  File "/home/user/.local/lib/python3.9/site-packages/torch/nn/utils/prune.py", line 536, in compute_mask
    topk = torch.topk(torch.abs(t).view(-1), k=nparams_toprune, largest=False)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```

### Versions

Collecting environment information...
PyTorch version: 1.12.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.18.4
Libc version: glibc-2.31

Python version: 3.9.2 (default, Feb 28 2021, 17:03:44)  [GCC 10.2.1 20210110] (64-bit runtime)
Python platform: Linux-5.10.0-20-amd64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.2.152
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2080 SUPER
GPU 1: NVIDIA GeForce RTX 2080 SUPER

Nvidia driver version: 470.161.03
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          32
On-line CPU(s) list:             0-31
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           45
Model name:                      Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz
Stepping:                        7
CPU MHz:                         1605.327
CPU max MHz:                     3300.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        5199.96
Virtualization:                  VT-x
L1d cache:                       512 KiB
L1i cache:                       512 KiB
L2 cache:                        4 MiB
L3 cache:                        40 MiB
NUMA node0 CPU(s):               0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30
NUMA node1 CPU(s):               1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Unknown: No mitigations
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 dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid xsaveopt dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.1
[pip3] numpy-groupies==0+unknown
[pip3] numpydoc==1.1.0
[pip3] torch==1.12.0
[pip3] torchvision==0.13.0
[conda] Could not collect


cc @jamesr66a