Add ONNX support for Pegasus
# What does this PR do?

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This PR adds ONNX support for the Pegasus model. Linked to https://github.com/huggingface/transformers/issues/16308


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## Who can review?

Anyone in the community is free to review the PR once the tests have passed.
@lewtun 

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_The documentation is not available anymore as the PR was closed or merged._<|||||>Hey @pramodith, this PR looks excellent! Did you try to convert a `Pegasus` model with this config?

I'm pinging @lewtun for reviewing.<|||||>@ChainYo  besides the pytests, I also tried executing the Onnx export and validation using this script. The difference between the pytorch tensors and the Onnx tensors was in the order of 1e-5. However, I did notice that the onnx export would fail when I set use_past=True, not sure if I need to do anything about that.

```from transformers import AutoConfig, AutoTokenizer, PegasusForCausalLM,PegasusForConditionalGeneration,PegasusModel
from transformers.models.pegasus import PegasusOnnxConfig
from transformers.onnx import export, validate_model_outputs
from pathlib import Path

def check_onnx_model(task):
    if task == "default":
        config = AutoConfig.from_pretrained("google/pegasus-large")
        model = PegasusModel.from_pretrained("google/pegasus-large")
        tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large")
    elif task == "seq2seq-lm":
        config = AutoConfig.from_pretrained("google/pegasus-xsum")
        model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum", add_cross_attention=True)
        tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
    else:
        config = AutoConfig.from_pretrained("google/pegasus-xsum")
        model = PegasusForCausalLM.from_pretrained("google/pegasus-xsum")
        tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")

    onnx_config = PegasusOnnxConfig(config, task=task, use_past=False)
    onnx_path = Path("model.onnx")
    onnx_inputs, onnx_outputs = export(tokenizer, model, onnx_config, onnx_config.default_onnx_opset, onnx_path)
    print(onnx_inputs)
    print(onnx_outputs)
    print(validate_model_outputs(onnx_config,tokenizer,model,onnx_path,onnx_outputs,onnx_config.atol_for_validation))

check_onnx_model("causal-lm")
```<|||||>> @ChainYo, besides the pytests, I also tried executing the Onnx export and validation using this script. The difference between the PyTorch tensors and the Onnx tensors was in the order of 1e-5. However, I did notice that the onnx export would fail when I set use_past=True, not sure if I need to do anything about that.

I'm not sure you can use past when you convert your model to ONNX. You have to rewrite the logic when using that feature with ONNX.

**Edit**: I never converted a model which uses `past`, but it could work if your config inherits from `OnnxSeq2SeqConfigWithPast`, which is the case.<|||||>> > @ChainYo, besides the pytests, I also tried executing the Onnx export and validation using this script. The difference between the PyTorch tensors and the Onnx tensors was in the order of 1e-5. However, I did notice that the onnx export would fail when I set use_past=True, not sure if I need to do anything about that.
> 
> I'm not sure you can use past when you convert your model to ONNX. You have to rewrite the logic when using that feature with ONNX.
> 
> **Edit**: I never converted a model which uses `past`, but it could work if your config inherits from `OnnxSeq2SeqConfigWithPast`, which is the case.

Gotcha! I'll wait for any comments by the reviewers in that case.<|||||>Hi, I just checked the failed test in the CI/CD pipeline, and it doesn't seem to come from your code. Could you please try to fetch/rebase your branch with the main upstream branch? We will see if it's solved.

[CI/CD error](https://app.circleci.com/pipelines/github/huggingface/transformers/44492/workflows/4da69f9f-f332-4ba8-97e7-3446ec6a559e/jobs/517244?invite=true#step-111-3911) 

`FAILED tests/models/bart/test_modeling_tf_bart.py::TFBartModelTest::test_decoder_model_past_large_inputs`<|||||>@ChainYo I've rebased and all the tests pass now!<|||||>> @ChainYo I've rebased, and all the tests pass now!

Super let's just wait for a reviewer now!