convert_graph_to_onnx.convert broken for translation model facebook/wmt19-en-de
## Environment info
- `transformers` version: 4.2.2
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.1 (True)
- Tensorflow version (GPU?): 2.5.0 (True)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False

### Who can help
@mfuntowicz (based on initial commit of convert_graph_to_onnx)
@stas00 (based on model used here)
@thomwolf (based on history)

## Information

Model I am using (Bert, XLNet ...): facebook/wmt19-en-de

The problem arises when using:
* [X] the official example scripts: transformers.convert_graph_to_onnx.convert
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [X] my own task or dataset: converting the translation model to onnx

## To reproduce

Steps to reproduce the behavior:

```
import torch
import transformers
from transformers import convert_graph_to_onnx
from pathlib import Path

nlp = transformers.pipeline("translation_en_to_de", model="facebook/wmt19-en-de", tokenizer="facebook/wmt19-en-de")
convert_graph_to_onnx.convert(
    framework="pt",
    model="facebook/wmt19-en-de",
    output=Path("encoder/en_de_trans.onnx"),
    opset=12,
    tokenizer="facebook/wmt19-en-de",
    use_external_format= False,
    pipeline_name= "translation_en_to_de",
)
```
Raises:
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-d46bec961b86> in <module>
      5 
      6 nlp = transformers.pipeline("translation_en_to_de", model="facebook/wmt19-en-de", tokenizer="facebook/wmt19-en-de")
----> 7 convert_graph_to_onnx.convert(
      8     framework="pt",
      9     model="facebook/wmt19-en-de",

~/anaconda3/envs/dev/lib/python3.8/site-packages/transformers/convert_graph_to_onnx.py in convert(framework, model, output, opset, tokenizer, use_external_format, pipeline_name)
    365     # Export the graph
    366     if framework == "pt":
--> 367         convert_pytorch(nlp, opset, output, use_external_format)
    368     else:
    369         convert_tensorflow(nlp, opset, output)

~/anaconda3/envs/dev/lib/python3.8/site-packages/transformers/convert_graph_to_onnx.py in convert_pytorch(nlp, opset, output, use_external_format)
    274 
    275     with torch.no_grad():
--> 276         input_names, output_names, dynamic_axes, tokens = infer_shapes(nlp, "pt")
    277         ordered_input_names, model_args = ensure_valid_input(nlp.model, tokens, input_names)
    278 

~/anaconda3/envs/dev/lib/python3.8/site-packages/transformers/convert_graph_to_onnx.py in infer_shapes(nlp, framework)
    196     tokens = nlp.tokenizer("This is a sample output", return_tensors=framework)
    197     seq_len = tokens.input_ids.shape[-1]
--> 198     outputs = nlp.model(**tokens) if framework == "pt" else nlp.model(tokens)
    199     if isinstance(outputs, ModelOutput):
    200         outputs = outputs.to_tuple()

~/anaconda3/envs/dev/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

TypeError: forward() got an unexpected keyword argument 'token_type_ids'
```

Subsequently, the call of the raise can be boiled down to inferring the shapes for [torch.onnx.export](https://github.com/huggingface/transformers/blob/6a346f0358a40f89ec384d441233bf54cac44f6a/src/transformers/convert_graph_to_onnx.py#L196)

I think that may be due to the incompatibility of the tokenizer() vs tokenizer.encode() for this very model.

```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/wmt19-en-de")
model = transformers.AutoModelForSeq2SeqLM.from_pretrained("facebook/wmt19-en-de")
string = "Hello. How are you?"

# model.generate(tokenizer(string, return_tensors="pt")) # Fails

model.generate(tokenizer.encode(string, return_tensors="pt")) # Succeeds
```

## Expected behavior

Model export should work properly.

Thank you for this excellent report, @oborchers - I'll investigate and report back.<|||||>Fixed in https://github.com/huggingface/transformers/pull/9736 

But found another problem: https://github.com/huggingface/transformers/issues/9737. Fixed in https://github.com/huggingface/transformers/pull/9738

So you will need both PRs for your task to work in case you want to try before they are merged.


<|||||>Awesome! Thank you, @stas00! Looking forward to try it out after PRs have been merged. Much appreciated <|||||>The problem you reported has been fixed in https://github.com/huggingface/transformers/pull/9736 (merged already)

But then another one poped up in https://github.com/huggingface/transformers/issues/9737

You can just use the https://github.com/huggingface/transformers/pull/9738 branch - since it contains both fixes.

Not sure how quickly it will get merged, since we might want to solve this for other models too. I made only a local for fsmt fix in that PR.<|||||>Great, thank you for the fast response and issue handling. I will provide a followup on #9738. While export works as intended, there is an issue I encounter while running the following code (built on 1st example):

```
sess = rt.InferenceSession(str(Path("encoder/en_de_trans.onnx")), opt)
spans = [
    "My name is Bert", # Succeeds
    "My name is Bert and" # Fails
]
for span in spans:
    model_input = nlp.tokenizer.encode_plus(span)
    model_input = {name : np.atleast_2d(value) for name, value in model_input.items()}
    out = nlp.model(**nlp.tokenizer(span, return_tensors="pt"))
    trans_1 = out[0].detach().cpu().numpy()
    trans_2 = out[1].detach().cpu().numpy()
    onnx_1, onnx_2 = sess.run(None, model_input)
    assert np.allclose(trans_1, onnx_1, atol=1e-5)
    assert np.allclose(trans_2, onnx_2, atol=1e-5)
```

"My name is Bert and" will raise:
```
---------------------------------------------------------------------------
RuntimeException                          Traceback (most recent call last)
<ipython-input-3-3ef2da9bdd5e> in <module>
     10     trans_1 = out[0].detach().cpu().numpy()
     11     trans_2 = out[1].detach().cpu().numpy()
---> 12     onnx_1, onnx_2 = sess.run(None, model_input)
     13     assert np.allclose(trans_1, onnx_1, atol=1e-5)
     14     assert np.allclose(trans_2, onnx_2, atol=1e-5)

~/anaconda3/envs/dev/lib/python3.8/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py in run(self, output_names, input_feed, run_options)
    122             output_names = [output.name for output in self._outputs_meta]
    123         try:
--> 124             return self._sess.run(output_names, input_feed, run_options)
    125         except C.EPFail as err:
    126             if self._enable_fallback:

RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Reshape node. Name:'Reshape_74' Status Message: /data/shared/packages/onnxruntime/onnxruntime/core/providers/cpu/tensor/reshape_helper.h:43 onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) gsl::narrow_cast<int64_t>(input_shape.Size()) == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{1,6}, requested shape:{5}
```

Solely based on intuition I'd assume that some dynamic shape of was not inferred properly/not passed to the dynamic_shapes of torch.onnx.export. But thats just a quick guess. Or did I miss something?


I see that I would have to look-into/re-implement the generate function, as only the tensors are passed back. I'm going to create a feature suggestion to support the [ORT Custom Ops](https://github.com/microsoft/ort-customops). Perhaps It would be possible to retrieve the actual translated string in the far future, instead of the tensors (or specify the output). 

As promised follow up feature request + suggestion under #9784 <|||||>Honestly, I don't know much about the ONNX-side of things. I asked @mfuntowicz to hopefully have a look and address this.

Also tagging @LysandreJik and @patrickvonplaten who perhaps may have some answers as well.

I wonder if this is an issue project-wise, e.g. do you have the same issue if you do this on a Bart model? I'm asking since fsmt is Bart with some tweaks.

Also I think it's best to open a new issue, since now we are dealing with a different issue, so it'd be easier to track and monitor.<|||||>Thank you for your help, @stas00! I followed your advice and created a new issue.<|||||>@oborchers It seems that it is a problem of the pythorch export of the dynamic_axes.
Using the nightly version (torch-1.9.0.dev20210212 + cpu) it works.

On the other hand, I am interested in using the onnx models to generate, (translate and summarize). 
Could you give me some indication of how to do a custom forward using the onnx model, to use in the generation_utils.generate function.

PS: for what you comment here [9784](https://github.com/huggingface/transformers/issues/9784) you plan to work on a User-specific re-implementation.
Thanks