`pipeline` backed with ONNX Runtime and quantization for faster inference
# 🚀 Feature request

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     Please provide a link to the paper and code in case they exist. -->

It would be cool, when we load a pipeline, to do, during the loading, the conversion to an ONNX `InferenceSession` along with (optionally) the quantization, as these features provide significant speedups. About the live conversion, maybe it could have been done before, i.e. we could load the `.onnx` file from the Hub (among other model assets). Anyway, the main goal is the use of the speedups provided by ONNX Runtime in the pipeline object.

One would instantiate the `pipeline` object that way:
```python
nlp = pipeline('text-classification', onnx_runtime=True, quantization=True)
```
Reference: https://onnxruntime.ai/docs/tutorials/inferencing/huggingface.html

## Motivation

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     related to a problem? e.g., I'm always frustrated when [...]. If this is related
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The speed and memory (with quantization) gains.

## Your contribution

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I could try :)
@sgugger @LysandreJik find this interesting?<|||||>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.

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