Repository: pytorch
Issue: Cannot Convert Pytorch model with fft_rfftn layers  to ONNX using latest torch.onnx.dynamo_export
Labels: module: onnx,triaged

### 🐛 Describe the bug

I am trying to convert the well renowned [LAMA Inpainting](https://github.com/advimman/lama) model to ONNX via the new dynamo_export, as earlier these fft_rfftn were not supported by onnx there wasn't a way to export this model to ONNX, although there was a workaround by adding a custom [FourierUnitJIT](https://github.com/Carve-Photos/lama/blob/f5fb39a18022c34a71bf9a47a6ec393c804b49ca/saicinpainting/training/modules/ffc.py#L153) class which made it compatible with older torch.onnx.export itself but has accuracy losses.
Conversion [Notebook ](https://github.com/Carve-Photos/lama/blob/main/export_LaMa_to_onnx.ipynb )of older approach.

As now we do have the support of these layers in onnx and dynamo_export allows us to export these, there is still some issue 

Minimum Reproducible Code:
```bash
!curl -LJO "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt"
```
```py
import torch
model = torch.jit.load("big-lama.pt", map_location="cpu")

dummy_image = torch.randn(1, 3, 512, 512)
dummy_mask = torch.randint(0, 2, (1, 1, 512, 512)).float()

onnx_program = torch.onnx.dynamo_export(model, (dummy_image, dummy_mask))
```
Torch ONNX Dynamo gives an error stating 
OnnxExporterError: Failed to export the model to ONNX. Generating SARIF report at 'report_dynamo_export.sarif'. SARIF is a standard format for the output of static analysis tools
[SARIF REPORT](https://drive.google.com/file/d/1uF9JO-Kn83qIhEYe2ggXNb7V_q08jMoa/view?usp=sharing)

```py
torch.onnx.export(model, (dummy_image, dummy_mask), "lama_inpainting.onnx", 
                    opset_version = 18,
                    input_names=["image", "mask"], 
                    output_names=["output"],
                    dynamic_axes={'image': {0: 'batch_size'},
                                'mask': {0: 'batch_size'},
                                'output': {0: 'batch_size'}})
```
Tried using the old onnx exporter but obviously it wont work as stated in #107588 and the code errors out stating 'aten::fft_rfftn' is not supported as expected 

### Versions

Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA L4
Nvidia driver version: 535.183.01
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
Address sizes:                        43 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               128
On-line CPU(s) list:                  0-127
Vendor ID:                            AuthenticAMD
Model name:                           AMD EPYC 7742 64-Core Processor
CPU family:                           23
Model:                                49
Thread(s) per core:                   2
Core(s) per socket:                   64
Socket(s):                            1
Stepping:                             0
Frequency boost:                      enabled
CPU max MHz:                          2250.0000
CPU min MHz:                          1500.0000
BogoMIPS:                             4491.48
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 sse4_1 sse4_2 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 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 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 rdpid overflow_recov succor smca sev sev_es
Virtualization:                       AMD-V
L1d cache:                            2 MiB (64 instances)
L1i cache:                            2 MiB (64 instances)
L2 cache:                             32 MiB (64 instances)
L3 cache:                             256 MiB (16 instances)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-127
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:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
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; Retpolines; 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] numpy==1.26.4
[pip3] onnx==1.16.2
[pip3] onnxscript==0.1.0.dev20240817
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] torchvision               0.19.0                   pypi_0    pypi