Repository: huggingface/transformers
Pull request: Fixes the inconsistency of the optionality of attention_mask

# What does this PR do?

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This PR fixes Issue #37046.
This issue discusses the inconsistency of the optionality of the parameter "attention_mask" in different functions. Specifically, the type of attention_mask in `LlamaForCausalLM`, `LlamaModel`, and `LlamaDecoderLayer` are `Optional[torch.Tensor] = None`. In [`LlamaAttention`](https://github.com/huggingface/transformers/blob/d6b3c7486b441296366f788fde57109337f63bca/src/transformers/models/llama/modeling_llama.py#L269) (a class which wraps the attention interface), the type is `Optional[torch.Tensor]` because `attention_mask` will be passed to it from `LlamaModel`, whether the type of `attention_mask` is `torch.Tensor` or `NoneType`. Therefore, `attention_mask` will be passed to the attention_interface, no matter it's a tensor or NoneType.
The key problem lies in the type specification in flash_attention. In [`flash_attention_forward`](https://github.com/huggingface/transformers/blob/d6b3c7486b441296366f788fde57109337f63bca/src/transformers/integrations/flash_attention.py#L17), the type of `attention_mask` is still `Optional[torch.Tensor]`whereas in [`_flash_attention_forward`](https://github.com/huggingface/transformers/blob/d6b3c7486b441296366f788fde57109337f63bca/src/transformers/modeling_flash_attention_utils.py#L234), a function called in `flash_attention_forward`, the type is `torch.Tensor`, which is unreasonable because:
1. it's inconsistent with the specification in `flash_attention_forward`. 
2. `_flash_attention_forward` is usable even if `attention_mask` is None. 

Therefore, this PR fixes the specification in `_flash_attention_forward` as well as the docstring to address the aforementioned issue.

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Changed files:
filename: src/transformers/modeling_flash_attention_utils.py
patch: @@ -280,7 +280,7 @@ def _flash_attention_forward(
     query_states: torch.Tensor,
     key_states: torch.Tensor,
     value_states: torch.Tensor,
-    attention_mask: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
     query_length: int,
     is_causal: bool,
     dropout: float = 0.0,
@@ -308,7 +308,7 @@ def _flash_attention_forward(
             Input key states to be passed to Flash Attention API
         value_states (`torch.Tensor`):
             Input value states to be passed to Flash Attention API
-        attention_mask (`torch.Tensor`):
+        attention_mask (`torch.Tensor`, *optional*):
             The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
             position of padding tokens and 1 for the position of non-padding tokens.
         dropout (`float`):