TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs
This addresses the bottleneck of MHA optimization in LLMs for researchers and practitioners, offering a novel compression and denoising method that is incremental by building on existing FFN techniques.
The paper tackles the problem of inefficiently utilizing the Multi-head Attention (MHA) block in LLMs for reasoning enhancement and compression by proposing a tensorisation framework that compresses MHA weights using Tucker decomposition, resulting in up to 250 times compression and improved reasoning across benchmarks without extra data or training.
The reasoning abilities of Large Language Models (LLMs) can be improved by structurally denoising their weights, yet existing techniques primarily focus on denoising the feed-forward network (FFN) of the transformer block, and can not efficiently utilise the Multi-head Attention (MHA) block, which is the core of transformer architectures. To address this issue, we propose a novel intuitive framework that, at its very core, performs MHA compression through a multi-head tensorisation process and the Tucker decomposition. This enables both higher-dimensional structured denoising and compression of the MHA weights, by enforcing a shared higher-dimensional subspace across the weights of the multiple attention heads. We demonstrate that this approach consistently enhances the reasoning capabilities of LLMs across multiple benchmark datasets, and for both encoder-only and decoder-only architectures, while achieving compression rates of up to $\sim 250$ times in the MHA weights, all without requiring any additional data, training, or fine-tuning. Furthermore, we show that the proposed method can be seamlessly combined with existing FFN-only-based denoising techniques to achieve further improvements in LLM reasoning performance.