Repository: pytorch
Issue: `aot_module` + `torch.onnx.export` bug or user error?
Labels: module: onnx,triaged,oncall: pt2,oncall: export

### 🐛 Describe the bug

I posted a question in the pytorch forum, but then I made some progress that made me think there might be a bug.  Please see here for discussion https://discuss.pytorch.org/t/how-do-i-get-torch-onnx-export-to-work-with-aot-module/201189.

I'll copy here:

I’m trying to use aot_module to compile forward and backward graph of a model. I want to be able to export the graph to onnx but I’m having trouble figuring out how to do that. Here is an example script

```python
import torch
from functorch.compile import aot_module
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.fx.experimental.symbolic_shapes import ShapeEnv
from functorch.compile import make_boxed_func
import torch._C._onnx as _C_onnx


class Linear(torch.nn.Module):
    def __init__(self):
        super(Linear, self).__init__()
        self.linear = torch.nn.Linear(128, 10)
        self.activation = torch.nn.ReLU()

    def forward(self, *inputs):
        input = self.linear(inputs[0])
        input = self.activation(input)
        return input


def compiler_fn(fx_module: torch.fx.GraphModule, example_inputs):
    print(fx_module)
    torch.onnx.export(
        fx_module,
        tuple(example_inputs),
        "model.onnx",
        training=_C_onnx.TrainingMode.PRESERVE,
        do_constant_folding=False,
        export_params=False,
    )
    return make_boxed_func(fx_module)


model = Linear()
model.train()
loss_fn = torch.nn.MSELoss()

with FakeTensorMode(allow_non_fake_inputs=True, shape_env=ShapeEnv()) as fake_mode:
    input = torch.randn((64, 128), requires_grad=True)
    labels = torch.randn((64, 10), requires_grad=True)

    c_model = aot_module(model, fw_compiler=compiler_fn, bw_compiler=compiler_fn)
    output = c_model(input)
    loss = loss_fn(output, labels)
    loss.backward()
```
I get

```
TypeError: f_(): incompatible function arguments. The following argument types are supported:
    1. (self: torch._C.Node, arg0: str, arg1: float) -> torch._C.Node

Invoked with: %5 : Tensor = onnx::Gemm(%primals_3, %t, %primals_2), scope: torch.fx.graph_module.GraphModule.__new__.<locals>.GraphModuleImpl::
, 'alpha', i1 
(Occurred when translating addmm).
```
and have no idea what I’m doing wrong. Any guidance would be appreciated. Thanks!

So, if i turn bias off in LinearLayer i get further. This gets passed the first error on the forward graph. Now I get something like

```
torch.onnx.errors.UnsupportedOperatorError: Exporting the operator 'aten::threshold_backward' to ONNX opset version 17 is not supported. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues.
```
So is the first error i showed in the original post a bug then?

### Error logs

_No response_

### Minified repro

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.2.0+rocm5.7
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 5.7.31921-d1770ee1b

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.29.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             20
On-line CPU(s) list:                0-19
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i7-13700H
CPU family:                         6
Model:                              186
Thread(s) per core:                 2
Core(s) per socket:                 14
Socket(s):                          1
Stepping:                           2
CPU max MHz:                        5000.0000
CPU min MHz:                        400.0000
BogoMIPS:                           5836.80
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 tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          544 KiB (14 instances)
L1i cache:                          704 KiB (14 instances)
L2 cache:                           11.5 MiB (8 instances)
L3 cache:                           24 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-19
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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
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, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.4
[pip3] onnx==1.16.0
[pip3] pytorch-triton-rocm==2.2.0
[pip3] torch==2.2.0+rocm5.7
[conda] Could not collect

cc @avikchaudhuri @gmagogsfm @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4 @penguinwu @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang