Error converting fine-tuned GPTNeoForCausalLM model to ONNX
Hi, I am trying to convert a fine-tuned GPT-Neo (125M) model to ONNX using the code below:

```
from transformers import pipeline, convert_graph_to_onnx, GPTNeoForCausalLM, GPT2Tokenizer
from pathlib import Path
import torch

model_name = "EleutherAI/gpt-neo-125M" 
pipeline_name = "text-generation"

tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-125M", bos_token='<|startoftext|>',
                                                          eos_token='<|endoftext|>', pad_token='<|pad|>')
nlp = pipeline(pipeline_name, model=model_name, tokenizer=tokenizer)

with torch.no_grad():
    (
        input_names,
        output_names,
        dynamic_axes,
        tokens,
    ) = convert_graph_to_onnx.infer_shapes(nlp, "pt")
    ordered_input_names, model_args = convert_graph_to_onnx.ensure_valid_input(
        nlp.model, tokens, input_names
    )

model_name = 'gpt-neo'

predictor_path = './' + model_name 

model2 = GPTNeoForCausalLM.from_pretrained(predictor_path)

text = "I feel happy and "
prompt = f'<|startoftext|>Review: {text} Sentiment: <|endoftext|>'
encodings_dict = nlp.tokenizer(prompt, truncation=True, max_length=300, padding="max_length", return_tensors="pt")

torch.onnx.export(
    model2,
    (encodings_dict['input_ids'], encodings_dict['attention_mask']),
    'model_test.onnx',
    input_names=input_names,
    output_names=output_names,
    dynamic_axes=dynamic_axes,
    do_constant_folding=True,
    use_external_data_format=True, # Needed because of model size
    enable_onnx_checker=True,
    opset_version=13
)

```
But I get this error:

```
ValueError                                Traceback (most recent call last)
<ipython-input-15-024e093371a4> in <module>()
     43     use_external_data_format=True, # Needed because of model size
     44     enable_onnx_checker=True,
---> 45     opset_version=13
     46 )

3 frames
/usr/local/lib/python3.7/dist-packages/torch/onnx/utils.py in _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)
   1300             for i, x in enumerate(value):
   1301                 if not isinstance(x, int):
-> 1302                     raise ValueError("The type of axis index is expected to be an integer")
   1303                 if x in value_dict:
   1304                     warnings.warn("Duplicate dynamic axis index {} was provided for input {}."

ValueError: The type of axis index is expected to be an integer
```

But if I remove dynamic_axes, I get this:

```
IndexError                                Traceback (most recent call last)
<ipython-input-16-89ddee8c10f8> in <module>()
     43     use_external_data_format=True, # Needed because of model size
     44     enable_onnx_checker=True,
---> 45     opset_version=13
     46 )

15 frames
/usr/local/lib/python3.7/dist-packages/transformers/models/gpt_neo/modeling_gpt_neo.py in forward(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)
    545             past_key_values = tuple([None] * len(self.h))
    546         else:
--> 547             past_length = past_key_values[0][0].size(-2)
    548 
    549         device = input_ids.device if input_ids is not None else inputs_embeds.device

IndexError: dimension specified as -2 but tensor has no dimensions
```

Can someone help me please?
cc @lewtun @michaelbenayoun <|||||>Hey @toby-htx thanks for raising the issue! It looks like you're using the `convert_graph_to_onnx` package which has been deprecated in favour of the `transformers.onnx` package. You can export that checkpoint using the following command:

```bash
# We need to increase the tolerance from the default for this model head
python -m transformers.onnx --model=EleutherAI/gpt-neo-125M  --feature=causal-lm --atol=5e-4 ./onnx/
```<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.