Repository: pytorch
Issue: Misleading error message in multilabel_margin_loss when passing incompatible tensor dimensions
Labels: module: nn,triaged,actionable

### 🐛 Describe the bug

While using MultiLabelMarginLoss in PyTorch, I received a RuntimeError with a misleading error message. Here's the traceback
```
RuntimeError: Expected non-empty vector or matrix with optional 0-dim batch size, but got: [20, 9, 20]
```
The error message suggests that I'm passing an empty vector or matrix, but that's not the case. I have tried tensors of different shapes, and the error message remains the same.

In my first example, my input tensor shape was [20, 9, 20] and the target tensor was a 7-D tensor. In a second example, the input tensor shape was [1, 2, 3] and the target tensor was a 3-D tensor. In both cases, I suspect the issue is that tensor shapes are uncompatiable.

While I understand that multilabel_margin_loss is expected to receive 1-D or 2-D tensors, the error message could be more descriptive. It would be helpful if it indicated the expected and received tensor dimensions, rather than just saying it expected a non-empty vector or matrix.

Here are the examples that led to this error:
```python
# First example
input_tensor = torch.rand([20, 9, 20])
target_tensor = torch.rand([2, 2, 2, 2, 2, 2, 2])  # 7-D tensor

loss_function = nn.MultiLabelMarginLoss()
loss = loss_function(input_tensor, target_tensor)

# Second example
input_tensor = torch.rand([1, 2, 3])
target_tensor = torch.rand([2, 2, 2])  # 3-D tensor

loss_function = nn.MultiLabelMarginLoss()
loss = loss_function(input_tensor, target_tensor)
```
Please let me know if there's any additional information you need or if there's a better way to approach this problem. If this behavior is expected and known, feel free to close this issue. Thank you!

### Versions

PyTorch version: 2.1.0.dev20230622+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
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: 12.0.0-3ubuntu1~20.04.5
CMake version: version 3.26.1
Libc version: glibc-2.31

Python version: 3.9.16 (main, Mar  8 2023, 14:00:05)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-152-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 2070
GPU 1: NVIDIA GeForce RTX 2070
GPU 2: NVIDIA GeForce RTX 2070
GPU 3: NVIDIA GeForce RTX 2070

Nvidia driver version: 530.30.02
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.2
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:                           63
Model name:                      Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz
Stepping:                        2
CPU MHz:                         1224.656
CPU max MHz:                     3200.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        4794.39
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: Split huge pages
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:          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 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 cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts md_clear flush_l1d

Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-triton==2.1.0+440fd1bf20
[pip3] torch==2.1.0.dev20230622+cu118
[pip3] torchaudio==2.1.0.dev20230622+cu118
[pip3] torchvision==0.16.0.dev20230622+cu118
[conda] blas                      1.0                         mkl

[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2023.1.0         h6d00ec8_46342

[conda] mkl-service               2.4.0            py39h5eee18b_1

[conda] mkl_fft                   1.3.6            py39h417a72b_1

[conda] mkl_random                1.2.2            py39h417a72b_1

[conda] numpy                     1.24.1                   pypi_0    pypi
[conda] pytorch-mutex             1.0                         cpu    pytorch
[conda] pytorch-triton            2.1.0+440fd1bf20          pypi_0    pypi
[conda] torch                     2.1.0.dev20230622+cu118          pypi_0    pypi
[conda] torchaudio                2.1.0.dev20230622+cu118          pypi_0    pypi
[conda] torchvision               0.16.0.dev20230622+cu118          pypi_0    pypi

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki