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This research introduces a novel fine-tuning technique for sparse attention mechanisms, enabling transformer models to handle long contexts efficiently even on limited hardware like a single A100 GPU. By allowing models to co-adapt with specific KV cache policies, the method often outperforms traditional exact attention approaches while providing an open-source library to facilitate adoption.
Learning how to Forget: Fine-tuning for Long-Context Sparse Attention
arXiv:2608.19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets. We provide a new method for fine-tuning models with sparse attention. It works for any KV cache policy, runs on a moderate hardware budget (e.g., a single Nvidia A100 GPU with 40 GB RAM), and allows the model to co-adapt with the policy, often outperforming models trained with exact attention (sequence parallelism). We also provide an efficient implementation of H2O sparse attention (the leading policy in our experiments) with dedicated scaled dot product attention kernel support. KeysAndValues (https://github.com/awslabs/keys_values), a new open source library for long-context inference and fine-tuning, provides easy-to-use and performant code for all methods discussed here.
Abstract:A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets. We provide a new method for fine-tuning models with sparse attention. It works for any KV cache policy, runs on a moderate hardware budget (e.g., a single Nvidia A100 GPU with 40 GB RAM), and allows the model to co-adapt with the policy, often outperforming models trained with exact attention (sequence parallelism). We also provide an efficient implementation of H2O sparse attention (the leading policy in our experiments) with dedicated scaled dot product attention kernel support. KeysAndValues (this https URL), a new open source library for long-context inference and fine-tuning, provides easy-to-use and performant code for all methods discussed here.
Computation and Language (cs.CL)
arXiv:2608.19920 [cs.CL]
(or arXiv:2608.19920v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.19920 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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