`MistralAttention`: where is the sliding window

[fteufel]

Hi,

I'm trying to understand the implementation of Mistral's attention in MistralAttention. https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L195 It is my understanding that it should always be using local window attention. In MistralFlashAttention2 this is very obvious, with config.sliding_window being used.

However, I'm not sure where the sliding window is used in the base MistralAttention without flash attention:

class MistralAttention(nn.Module):
    """
    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
    and "Generating Long Sequences with Sparse Transformers".
    """

but the forward pass simply reads

attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

which I understand as full self attention.

Is the sliding window only used when running with Flash Attention, or am I missing something? Thanks!

[amyeroberts]

cc @ArthurZucker @younesbelkada

[PenutChen]

I think the sliding window trick is based on masking? https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L998-L1018

[fteufel]

Thanks, I see. But wouldn't this throw away any computational efficiency gains expected from using a sliding window in the first place?

[PenutChen]

I have the same question. I think the sliding window has two aspects:

1. From the perspective of the attention mask, it essentially acts as a token-level sliding window that influences each token's view of the context.
2. From a kv-cache perspective, truncating the cache outside the window can improve computational efficiency.

Just my guess above.

[ArthurZucker]

Yes this would throw away the gains, and it is pretty much expected as the best way to use sliding_window is through the sdpa or the flash_attention api, unless a rotating buffer is used.

Closing as expected, feel free to discuss! 🤗

[fteufel]

Hi @ArthurZucker interesting - so sdpa actually exploits the local window structure of the attention mask in the backend?

[ArthurZucker]

It should if the mask is correctly passed yeah. New sdpa has the sliding_window argument anyway. Not sure it was correctly prepared before, important PR: #29407

[ehuaa]

It should if the mask is correctly passed yeah. New sdpa has the sliding_window argument anyway. Not sure it was correctly prepared before, important PR: #29407

@ArthurZucker Did you mention this pr? https://github.com/pytorch/pytorch/pull/114823, which is not use sliding_window param explicitly but can handle the sliding window mask in the sdpa function, am i right? So if we pass the right mask through _prepare_4d_causal_attention_mask_for_sdpa as you mentioned here, https://github.com/huggingface/transformers/pull/29407, we can use local window feature of Mistral. But I think we can still gain some computational efficiency with local attention without the rotating buffer, because of the sparsity of attention mask of sliding window attention.

[ArthurZucker]

Sorry I meant the new SDPA codepath in transformers but it's not merged yet, yes as you say handles the mask