ONNX Export error
Labels: module: onnx, triaged

### 🐛 Describe the bug

@thiagocrepaldi I met this Error `ValueError Expected more than 1 value per channel when training`, when setting training=torch.onnx.TrainingMode.TRAINING on my export() call. It seems that this error is caused by batchnorm, how to fix this issue ? #95867  
I cannot reopen that issue, so I create a new issue here. Thanks a lot.

### Versions

PyTorch version: 1.13.0a0+git49444c3
Is debug build: False
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.31

Python version: 3.8.16 (default, Mar  2 2023, 03:21:46)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA TITAN RTX
GPU 1: NVIDIA TITAN RTX
GPU 2: NVIDIA TITAN RTX
GPU 3: NVIDIA TITAN RTX

Nvidia driver version: 515.86.01
cuDNN version: Probably one of the following:
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.1.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.1.1
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          40
On-line CPU(s) list:             0-39
Thread(s) per core:              2
Core(s) per socket:              10
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
Stepping:                        4
CPU MHz:                         2318.408
CPU max MHz:                     3000.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4400.00
Virtualization:                  VT-x
L1d cache:                       640 KiB
L1i cache:                       640 KiB
L2 cache:                        20 MiB
L3 cache:                        27.5 MiB
NUMA node0 CPU(s):               0-9,20-29
NUMA node1 CPU(s):               10-19,30-39
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:   Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:          Mitigation; 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; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable
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 smx 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 pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm 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 pku ospke md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.23.1
[pip3] pytorch-triton==2.1.0+7d1a95b046
[pip3] torch==1.13.0a0+git49444c3
[pip3] torchvision==0.15.1a0+42759b1
[conda] magma-cuda110             2.5.2                         1    pytorch
[conda] mkl                       2023.1.0         h6d00ec8_46342  
[conda] mkl-include               2023.1.0         h06a4308_46342  
[conda] numpy                     1.23.1                   pypi_0    pypi
[conda] pytorch-triton            2.1.0+7d1a95b046          pypi_0    pypi
[conda] torch                     1.13.0a0+git49444c3          pypi_0    pypi
[conda] torchvision               0.15.1a0+42759b1          pypi_0    pypi