`jacfwd` fails when computing the gradient for `channels_last` tensor
Labels: triaged, module: memory format, module: functorch

### 🐛 Describe the bug

`jacfwd` fails when computing the gradient for `channels_last` tensor but `jacrev` succeeds without any error

```py
import torch
from torch.func import jacrev, jacfwd


input = torch.randn(1, 2, 3, 4).to(memory_format=torch.channels_last)

def func(input):
    input = input.reshape([1, 2, 3, 4])
    input = input.to(memory_format=torch.channels_last)
    return input
test_inputs = [input]

print(jacrev(func)(input))
# succeed
print(jacfwd(func)(input))
# RuntimeError: required rank 4 tensor to use channels_last format
```

`jacfwd` raises an error that "required rank 4 tensor to use channels_last format"

### Versions

```
Collecting environment information...
PyTorch version: 2.1.0.dev20230311+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: 14.0.0-1ubuntu1
CMake version: version 3.25.0
Libc version: glibc-2.35

Python version: 3.9.16 (main, Mar  8 2023, 14:00:05)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-58-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.78.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.1
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:                   48 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          64
On-line CPU(s) list:             0-63
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen Threadripper PRO 5975WX 32-Cores
CPU family:                      25
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU max MHz:                     7006.6401
CPU min MHz:                     1800.0000
BogoMIPS:                        7186.68
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 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                  AMD-V
L1d cache:                       1 MiB (32 instances)
L1i cache:                       1 MiB (32 instances)
L2 cache:                        16 MiB (32 instances)
L3 cache:                        128 MiB (4 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-63
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 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 always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-triton==2.1.0+2c32f43999
[pip3] torch==2.1.0.dev20230311+cu117
[pip3] torchaudio==2.0.0.dev20230311+cu117
[pip3] torchvision==0.15.0.dev20230311+cu117
[conda] numpy                     1.24.1                   pypi_0    pypi
[conda] pytorch-triton            2.1.0+2c32f43999          pypi_0    pypi
[conda] torch                     2.1.0.dev20230311+cu117          pypi_0    pypi
[conda] torchaudio                2.0.0.dev20230311+cu117          pypi_0    pypi
[conda] torchvision               0.15.0.dev20230311+cu117          pypi_0    pypi
```

cc @jamesr66a @zou3519 @Chillee @samdow @soumith @kshitij12345 @janeyx99