This doesn't match my mental model (or implemented model in the case of GPT2) of how self-attention works (you need to calculate the residual stream for each individual token, attending to all prior tokens before it). Have a link?
edit - This occurred to me after the fact but I wonder if the difference is that the use case I work with is processing batches of many different embedding requests (but computed in one batch), therefore it has to process `min(longest embedding, N)` tokens so any individual request in theory has no difference. This would also be the case for Anthropic however.
I could imagine something where encoders pad up to the context length because causal masking doesn't apply and the self attention has learned to look across the whole context-window.
[1] Original Google Paper - https://arxiv.org/abs/1706.03762
[2] Original GPT Paper - https://s3-us-west-2.amazonaws.com/openai-assets/research-co...