11/21/2023 0 Comments Xmplify mac![]() If a lot of time has passed since a feature was worked on and a bug is spotted or tackled then it might take a fair bit of time to figure out how everything works again before you can fix it. If a bug turns up later or perhaps soon after it's deployed you might have an idea of where it might be and track it down fairly quickly. It might go something like this: If you spot a bug as you're writing a new feature everything is fresh in your mind and it can sometimes take just a moment to fix. In general, the longer the time between when a bug was first introduced and when the bug is identified and fixed the more expensive it is in both time and money. Just as mistakes and the unexpected are part of life, bugs are part of software development. ![]() Length 8K, and 100x faster at sequence length 64K. Hyena operators are twice as fast as highly optimized attention at sequence Quality with a 20% reduction in training compute required at sequence lengthĢK. Modeling in standard datasets (WikiText103 and The Pile), reaching Transformer New state-of-the-art for dense-attention-free architectures on language Other implicit and explicit methods, matching attention-based models. Sequences of thousands to hundreds of thousands of tokens, Hyena improvesĪccuracy by more than 50 points over operators relying on state-spaces and In this work, we propose Hyena, a subquadratic drop-in replacementįor attention constructed by interleaving implicitly parametrized longĬonvolutions and data-controlled gating. Subquadratic methods based on low-rank and sparse approximations need to beĬombined with dense attention layers to match Transformers, indicating a gap inĬapability. Sequence length, limiting the amount of context accessible. However, the core buildingīlock of Transformers, the attention operator, exhibits quadratic cost in ![]() Transformers due to their ability to learn at scale. Recent advances in deep learning have relied heavily on the use of large
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