Fix GPT-J onnx conversion
# What does this PR do?

Fix some problems encountered while converting a GPT-J model to Onnx.
Thanks to @ri938 who found where to fix bugs (on 🤗 Discord).

Models:
gpt2: @patrickvonplaten, @LysandreJik
and @lewtun because you reviewed the first PR for GPT-J Onnx Config, here #16274

I'm currently uploading a fully converted `EleutherAI/gpt-j-6B` model to the hub which demonstrate that the conversion command line worked with these fixes. Find it [here](https://huggingface.co/OWG/gpt-j-6B)

Here is the command I used (I had to fix `atol` to 1e-04 because 1e-05 was not true while validating the model):

```bash
python -m transformers.onnx --model=EleutherAI/gpt-j-6B --feature=causal-lm --atol=1e-04 onnx/
```
_The documentation is not available anymore as the PR was closed or merged._<|||||>Fine I'm going to accept @lewtun 's suggestion. :hugs: 

I see there are some failing checks, and I also see that there are problems with linting is it normal ?<|||||>The `run_tests_torch_and_tf`  failure is unrelated, to fix the `check_code_quality` test, run the `make fixup` command and push again.<|||||>> The `run_tests_torch_and_tf` failure is unrelated, to fix the `check_code_quality` test, run the `make fixup` command and push again.

Last time I tried to run `make fixup` it changed linting on more than 87 files not related to the PR so I reverted the fixup<|||||>@ChainYo 
hi, thanks for your contribution. I tested it with my basic gptj model and I think it's working pretty well. 

But I don't think it's working well when I tested it with a model that was extracted using a method called 'use cache' or 'use past'. Can you give me an example or check if there's anything wrong with my code??

Below is the test i did
```python
ort_session = onnxruntime.InferenceSession(onnx_path, providers=['CUDAExecutionProvider'])

#check session's input
for ort_session_input in ort_session.get_inputs():
    print(ort_session_input.name, ort_session_input.shape, ort_session_input.type)
#input_ids ['batch', 'sequence'] tensor(int64)
#past_key_values.0.key ['batch', 16, 'past_sequence + sequence', 256] tensor(float)
#past_key_values.0.value ['batch', 16, 'past_sequence + sequence', 256] tensor(float) 
#...
#past_key_values.27.key ['batch', 16, 'past_sequence + sequence', 256] tensor(float)
#past_key_values.27.value ['batch', 16, 'past_sequence + sequence', 256] tensor(float) 
#attention_mask ['batch', 'past_sequence + sequence'] tensor(float)

input_txt_list = [
    'text for test', 
    'gptj'
]
ort_input = make_onnx_inputs(input_txt_list)
for k,v in ort_input.items():
    print(k,v.size(),v.dtype)
#input_ids torch.Size([2, 3]) torch.int64
#past_key_values.0.key torch.Size([2, 16, 0, 256]) torch.float32
#past_key_values.0.value torch.Size([2, 16, 0, 256]) torch.float32
#...
#past_key_values.27.key torch.Size([2, 16, 0, 256]) torch.float32
#past_key_values.27.value torch.Size([2, 16, 0, 256]) torch.float32
#attention_mask torch.Size([2, 3]) torch.float32

#TypeError
ort_output = ort_session.run(None, ort_input)
```

And this is the error message
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_1166/2757688042.py in <module>
----> 1 ort_output = ort_session.run(None, ort_input)

/opt/conda/lib/python3.7/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py in run(self, output_names, input_feed, run_options)
    190             output_names = [output.name for output in self._outputs_meta]
    191         try:
--> 192             return self._sess.run(output_names, input_feed, run_options)
    193         except C.EPFail as err:
    194             if self._enable_fallback:

TypeError: run(): incompatible function arguments. The following argument types are supported:
    1. (self: onnxruntime.capi.onnxruntime_pybind11_state.InferenceSession, arg0: List[str], arg1: Dict[str, object], arg2: onnxruntime.capi.onnxruntime_pybind11_state.RunOptions) -> List[object]

Invoked with: <onnxruntime.capi.onnxruntime_pybind11_state.InferenceSession object at 0x7f319b7e3030>, ['logits', 'present.0.key', 'present.0.value', 'present.1.key', ...'present.27.key', 'present.27.value'], {{'input_ids': tensor([[24496,  1956, 15560], [    3,     3, 18566]]), 'past_key_values.0.key': tensor([], size=(2, 16, 0, 256)), 'past_key_values.0.value': tensor([], size=(2, 16, 0, 256)), 
...
'past_key_values.27.key': tensor([], size=(2, 16, 0, 256)), 'past_key_values.27.value': tensor([], size=(2, 16, 0, 256)), 'attention_mask': tensor([[1.,1.,1.],[0.,0.,1.]])}, None
```
<|||||>> @ChainYo hi, thanks for your contribution. I tested it with my basic gptj model and I think it's working pretty well.
> 
> But I don't think it's working well when I tested it with a model that was extracted using a method called 'use cache' or 'use past'. Can you give me an example or check if there's anything wrong with my code??

Hi @lsn1106 could you try to add your model to [netron.app](https://netron.app/) and check the expected inputs by clicking on the first layer ?

I'm not sure, but maybe `use_past` is a feature that is only implemented under the hood in the Transformers library which is not available in the Onnxruntime library.

<|||||>@ChainYo 
Thank you for your kind advice. Maybe I should refer to this github code [[link](https://github.com/microsoft/onnxruntime/blob/70d97bdf532502cb9da8ff8711fe3b0ff11cfec3/onnxruntime/python/tools/transformers/gpt2_helper.py#L56)]<|||||>Hey @lsn1106 looking at your error in ORT

```
TypeError: run(): incompatible function arguments. The following argument types are supported:
```

it seems that you're not passing inputs with the correct types. What happens if you cast your inputs to NumPy arrays and ensure that `ort_input` truly is a `dict`?
<|||||>@lewtun 
i've already tried that but same error occured. thank you :)<|||||>> Last time I tried to run `make fixup` it changed linting on more than 87 files not related to the PR so I reverted the fixup

Maybe you can rebase on `main` and run `make fixup` again? I'm not entirely sure why it should lint so many files, but this might resolve the problem<|||||>@lsn1106 would you mind sharing a reproducible code snippet that shows how you export the model, are creating the inputs for ORT, etc?<|||||>> > Last time I tried to run `make fixup` it changed linting on more than 87 files not related to the PR so I reverted the fixup
> 
> Maybe you can rebase on `main` and run `make fixup` again? I'm not entirely sure why it should lint so many files, but this might resolve the problem

Well it seems to be solved, thanks!<|||||>I think the last thing we need to do is run `make style && make quality` and then this should be good to go 🚀 !<|||||>> I think the last thing we need to do is run `make style && make quality` and then this should be good to go rocket !

Yes sorry the first `make fixup` didn't run black, it should be good now!<|||||>Great, thanks for fixing the style issues! Merging this since the issue reported by @lsn1106 is unrelated to the fix provided by this PR