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Luoyang Sun, Cheng Deng, Jiwen Jiang, Xinjian Wu, Haifeng Zhang, Lei Chen, Lionel Ni, Jun Wang
Outline
In this paper, we propose a G rouped-Head Laten T to solve the computational and memory overhead problems of the attention mechanism, which plays an important role in improving the performance of large-scale language models (LLMs). We propose Attention (GTA). GTA consists of two components: sharing the attention map across multiple heads and compressing the value cache into the latent space. It aims to reduce the FLOPs of attention computation by up to 62.5% and the KV cache by up to 70%, while maintaining performance while reducing memory usage and computational complexity. As a result, the GTA model shows an effect of improving the end-to-end inference speed by a factor of 2.
Takeaways, Limitations
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Takeaways:
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We present a novel method that can significantly improve the computational and memory efficiency of LLM's attention mechanism.
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Increase LLM deployment efficiency by up to 2x faster end-to-end inference speed.
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Expanding LLM deployment possibilities in resource-constrained environments by reducing memory usage.
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Performance improvements in both pre-fill and decoding steps.
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Limitations:
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Further research is needed to determine whether GTA's performance improvements apply equally to all types of LLMs and datasets.
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Further analysis is needed to determine the generalizability of the proposed method and to compare it with other attention mechanisms.
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Further analysis is needed on the possibility of information loss during compression into latent space.
