Repository: pytorch
Issue: Incorrect error message on incorrect byte boundries on _scaled_mm on fp8
Labels: triaged,module: float8

### 🐛 Describe the bug

Incorrect error message:

```python
import torch

tensor1 = torch.rand(2048,2048, device="cuda").to(torch.float8_e4m3fn)
tensor2 = torch.rand(2048*2048+1, device="cuda").to(torch.float8_e4m3fn)[1:].view(2048,2048).T

print(torch._scaled_mm(tensor1, tensor2, torch.tensor(1.0, device="cuda"), torch.tensor(1.0, device="cuda")))
```

Current error message:
```RuntimeError: CUDA error: CUBLAS_STATUS_NOT_SUPPORTED when calling cublasLtMatmul with transpose_mat1 t transpose_mat2 n m 2048 n 2048 k 2048 mat1_ld 2048 mat2_ld 2048 result_ld 2048 computeType 68 scaleType 0```

correct error message should be something like:
```RuntimeError: Boundary Error: tensor.storage_offset must be divisible by 32 for tensor b```

### Versions

PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Fedora Linux 39 (Workstation Edition) (x86_64)
GCC version: (GCC) 13.3.1 20240913 (Red Hat 13.3.1-3)
Clang version: Could not collect
CMake version: version 3.30.2
Libc version: glibc-2.38

Python version: 3.12.7 (main, Oct  1 2024, 00:00:00) [GCC 13.3.1 20240913 (Red Hat 13.3.1-3)] (64-bit runtime)
Python platform: Linux-6.11.7-100.fc39.x86_64-x86_64-with-glibc2.38
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4060 Laptop GPU
Nvidia driver version: 560.35.03
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.9.7
/usr/lib64/libcudnn.so.9.5.1
/usr/lib64/libcudnn_adv.so.9.5.1
/usr/lib64/libcudnn_adv_infer.so.8.9.7
/usr/lib64/libcudnn_adv_train.so.8.9.7
/usr/lib64/libcudnn_cnn.so.9.5.1
/usr/lib64/libcudnn_cnn_infer.so.8.9.7
/usr/lib64/libcudnn_cnn_train.so.8.9.7
/usr/lib64/libcudnn_engines_precompiled.so.9.5.1
/usr/lib64/libcudnn_engines_runtime_compiled.so.9.5.1
/usr/lib64/libcudnn_graph.so.9.5.1
/usr/lib64/libcudnn_heuristic.so.9.5.1
/usr/lib64/libcudnn_ops.so.9.5.1
/usr/lib64/libcudnn_ops_infer.so.8.9.7
/usr/lib64/libcudnn_ops_train.so.8.9.7
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):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            AuthenticAMD
Model name:                           AMD Ryzen 9 7940HS w/ Radeon 780M Graphics
CPU family:                           25
Model:                                116
Thread(s) per core:                   2
Core(s) per socket:                   8
Socket(s):                            1
Stepping:                             1
Frequency boost:                      enabled
CPU(s) scaling MHz:                   61%
CPU max MHz:                          5263.0000
CPU min MHz:                          400.0000
BogoMIPS:                             7985.44
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 amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 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 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca flush_l1d amd_lbr_pmc_freeze
Virtualization:                       AMD-V
L1d cache:                            256 KiB (8 instances)
L1i cache:                            256 KiB (8 instances)
L2 cache:                             8 MiB (8 instances)
L3 cache:                             16 MiB (1 instance)
NUMA node(s):                         2
NUMA node0 CPU(s):                    0-15
NUMA node1 CPU(s):                    
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   Mitigation; Safe RET
Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnxruntime==1.20.0
[pip3] pytorch-lightning==2.4.0
[pip3] rotary-embedding-torch==0.6.5
[pip3] torch==2.5.1
[pip3] torchvision==0.20.1
[pip3] triton==3.1.0
[pip3] vector-quantize-pytorch==1.17.4
[conda] Could not collect


cc @yanbing-j @vkuzo @albanD @kadeng @penguinwu