llama model: causal_mask does not exist
Author: xadupre
Pull request: no

### System Info

Line: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L372C43-L372C54

`causal_mask` does not exist if attention_mask is None.

```python
  if attention_mask is not None:  # no matter the length, we just slice it
      if cache_position is not None:
          causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
      attn_weights = attn_weights + causal_mask
```

### Who can help?

@onnx

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```python
config = LlamaConfig(
    num_hidden_layers=num_hidden_layers,
    vocab_size=vocab_size,
    hidden_size=hidden_size,
    intermediate_size=intermediate_size,
    max_position_embeddings=max_position_embeddings,
    num_attention_heads=num_attention_heads,
)
config._attn_implementation = _attn_implementation
model = LlamaAttention(config)
torch.compile(model)
optimized_model = torch.compile(model)
optimized_model(*inputs)
```

### Expected behavior

No exception.

Comments:
cc @ArthurZucker @younesbelkada 

Seems to be a [regression](https://github.com/huggingface/transformers/pull/27931/files#diff-06392bad3b9e97be9ade60d4ac46f73b6809388f4d507c2ba1384ab872711c51R358) from #27931

@BowenBao did we hit that during ONNX Export?

> @BowenBao did we hit that during ONNX Export?

At that time no, but things might have changed, or the test may had run with a different set of arguments.

Just tried with 4.38 rel and is running into below error when exporting with dynamo

> AssertionError: Mutating module attribute _cos_cached during export.

This is full graph dynamo export regression introduced by https://github.com/huggingface/transformers/pull/29109

I can get dynamo export to work e2e by commenting out the two lines below. I guess on my end it's using a different set of inputs so not repro-ing OP's error.
```
        self._cos_cached = cos
        self._sin_cached = sin
```

I'll open a fix right now, these should have been buffers registered. 

Could you try with the updated PR? If this work I think we can do a Patch release

Thank you for the quick patch @ArthurZucker ! Validated locally this unblocks full graph dynamo export.

I'm not sure if OP's issue still persists. @xadupre could you share the set of `inputs` in your repro?

Alright, I'll do an actual patch release on Monday as I think this is important to have ! 

Thank you.

Out of curiosity, how long does a transformers patch release take in terms of hours?

Here is a short example failing with 4.38.1. It does not even need torch.compile and return ``UnboundLocalError: local variable 'causal_mask' referenced before assignment``.

```python
import torch
from transformers import LlamaConfig
from transformers.models.llama.modeling_llama import LlamaAttention

config = LlamaConfig(
    hidden_size=16,
    num_hidden_layers=1,
    vocab_size=1024,
    intermediate_size=16,
    max_position_embeddings=1024,
    num_attention_heads=2,
)
config._attn_implementation = "eager"

class LlamaAttentionWrapper(torch.nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = LlamaAttention(config, layer_idx=0)

    def forward(self, hidden_states, attention_mask, position_ids):
        attn_output, _, _ = self.attention(
            hidden_states, attention_mask, position_ids
        )
        return attn_output

def generate_example_inputs(batch: int, seq: int, hidden_size: int):
    hidden_state = torch.randn(batch, seq, hidden_size)
    attention_mask = torch.zeros(batch, 1, seq, seq, dtype=torch.float)
    position_ids = torch.arange(0, seq, dtype=torch.int64)
    position_ids = position_ids.unsqueeze(0).view(-1, seq)
    return hidden_state, attention_mask, position_ids

example_args = generate_example_inputs(2, 1024, 16)
model = LlamaAttentionWrapper(config)
model(*example_args)
```

@xadupre this fixes it:
```diff
         if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask
             if cache_position is not None:
                 causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
```
which we do have for `sdpa`. Will be included in the patch as well.