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Support FP8 in PyTorch BERT models:

* add fp8 hybrid/e4m3/e5m2 in precision arguments
* build BERT encoders with `te.TransformerLayer` to repalce
`transformers.BertModel`
* wrap forward steps with fp8 autocast

**Description**
Revise the all the term `onnx` to `onnxruntime`.

Benchmarks: Add Feature - Add DistributedImpl and DistributedBackend arguments for micro benchmark. (#100)

Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

* benchmarks init.
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>