Quantized Transformer ONNX Export Fails
Labels: module: onnx, oncall: quantization, triaged

### 🐛 Describe the bug

I am trying to export the torch-native PTQ model using the timm repo. I am working on exporting the deit_tiny_patch_16 model.
The model conversion is however failing with runtime error:

```RuntimeError: ONNX symbolic expected node type prim::TupleConstruct, got `%456 : Tensor = onnx::Concat[axis=1](%quantize_per_tensor_5, %patch_embed_norm) # <eval_with_key>.10:32:0```

I am not aware on how to debug this specific type of issue, tried changing the specific layer to suppress if the error is because of that layer, however, I start getting the error in some other layer.

For reproducing, we just need to export the quantized timm network, timm can be directly cloned, and the torch-native quantization can be used from https://pytorch.org/blog/quantization-in-practice/ .

Basically,

```
import timm
import torch
from torch.quantization import quantize_fx

torch_model = timm.create_model('deit_tiny_patch16_224')
backend = "fbgemm"
qconfig_dict = {"": torch.quantization.get_default_qconfig(backend)}

model_prepared = quantize_fx.prepare_fx(torch_model, qconfig_dict)

# We can skip the calibration for the export purpose

model_quantized = quantize_fx.convert_fx(model_prepared)

torch.onnx.export(model_quantized,
                  x, 
                  'model.onnx', 
                  export_params=True, 
                  opset_version=13, 
                  do_constant_folding=False, 
                  input_names=['input'], 
                  output_names=['output'],
                  dynamic_axes={"input":{0:"batch_size"},
                                "output":{0:"batch_size"}}
                  )
```



### Versions

Collecting environment information...
PyTorch version: 1.12.1
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27

Python version: 3.8.13 (default, Oct 21 2022, 23:50:54)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-136-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: 
GPU 0: NVIDIA Graphics Device
GPU 1: NVIDIA Graphics Device
GPU 2: NVIDIA Graphics Device

Nvidia driver version: 465.19.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.23.3
[pip3] torch==1.12.1
[pip3] torchaudio==0.12.1
[pip3] torchvision==0.13.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.1            py38hd3c417c_0  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] numpy                     1.23.3           py38h14f4228_1  
[conda] numpy-base                1.23.3           py38h31eccc5_1  
[conda] pytorch                   1.12.1          py3.8_cuda11.3_cudnn8.3.2_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                0.12.1               py38_cu113    pytorch
[conda] torchvision               0.13.1               py38_cu113    pytorch

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel