onnxruntime

Update LLaMA attention fusions (#19200)

### Description
This PR updates the LLaMA-2 attention fusions by adding the following.

- Loading the PyTorch model from Hugging Face with the `LlamaAttention`
class before exporting
- Updating the attention mask pattern matching to support another case

This PR also fixes [this
issue](https://github.com/microsoft/onnxruntime/issues/19040).

### Motivation and Context
Recent changes to Hugging Face's `transformers` library break the
existing pattern matching. Since the attention fusions aim to change the
graph from `LayerNorm Op --> Set of Attention Nodes --> LayerNorm Op` to
`LayerNorm Op --> Attention Op --> LayerNorm Op` per layer, ultimately
it does not matter what nodes comprise the `Set of Attention Nodes`
because they will all be removed and replaced by the `Attention Op` in
the end.

Therefore, it does not matter whether the `LlamaAttention` class or a
different attention class is used to load the PyTorch model before
exporting because the expected graphs after the attention fusions will
look identical no matter the attention class chosen. By loading the
PyTorch model with the `LlamaAttention` class instead of other attention
classes (e.g. `LlamaFlashAttention2` or `LlamaSdpaAttention`) and then
exporting it to ONNX, the existing pattern matching will continue to
work.