Transformer tricks: Removing weights for skipless transformers (2404.12362v1)

Abstract: He and Hofmann (arXiv:2311.01906) detailed a skipless transformer without the V and P (post-attention projection) linear layers, which reduces the total number of weights. However, this scheme is only applicable to MHA (multi-head attention), but not for MQA (multi-query attention) and GQA (grouped-query attention). The latter schemes are used by many popular LLMs such as Llama 2, Mistral, Mixtral, PaLM, and Gemma. Therefore, this micro-paper proposes mathematically equivalent versions that are suitable for MQA and GQA. For example, removing Q and P from a skipless version of Mistral-7B would remove 15% of its weights (and thus reduce its compute and memory complexity). See arXiv:2402.13388 and https://github.com/OpenMachine-ai/transformer-tricks for code and more transformer tricks.

• The paper introduces innovative weight removal techniques for skipless transformers, achieving up to 16% parameter reduction.
• The paper mathematically merges redundant linear layers into the feedforward network to maintain functional equivalence.
• The paper demonstrates improved inference speeds, with models like Mistral-7B gaining approximately 1.17–1.19x faster performance.

An Analysis of Transformer Weight Reduction Techniques in Skipless Architectures

The paper "Transformer Tricks: Removing Weights for Skipless Transformers" by Nils Graef offers a nuanced exploration of transformer architectures, specifically focusing on weight reduction techniques in skipless transformers. It addresses the limitations of existing models, such as MQA (multi-query attention) and GQA (grouped-query attention), by developing mathematically equivalent configurations suitable for these attention mechanisms, commonly utilized in widely-recognized models like Llama 2, Mistral, Mixtral, PaLM, and Gemma.

Key Methodologies

This research expands on prior work that eliminated V and P (post-attention projection) linear layers in MHA (multi-head attention) based transformers. It extends these methods to MQA and GQA, enabling the elimination of the Q and P layers, which in certain models such as the Mistral-7B, results in significant weight savings of 15%. The paper illustrates the mathematical transformations required for merging various linear layers directly into the feedforward network (FFN), thereby preserving functional equivalence while reducing parameter count and computational complexity.

The details are systematically presented through visual representations, such as Figure 1, which showcases the elimination of skip connections and the merging of linear layers. This technique allows for the reduction of weights without impacting the intended parallel and serial functionalities.

Numerical Results and Practical Implications

The paper provides concrete computational savings by using Pythia-6.9B and Mistral-7B as case studies. It reports a reduction in total weights by about 16% and 15% respectively, which translates to improved inference speed by approximately 1.17-1.19x. These empirical results are notable as they offer a substantial decrease in model size and required compute resources, thereby enhancing deployment efficiency.

Theoretical Implications

The mathematical framework provided for weight reduction in transformers broadens the understanding of skipless architectures. The requirement for invertible matrices Q, K, and V is theoretically underpinned by the rarity exception that random square matrices are non-invertible. This framework underlines a deeper insight into restructuring transformer architectures for optimized performance.

Future Prospects

The potential extension of these methods to transformers with traditional normalization and skip connections presents a compelling avenue for future investigation. This potential adaptation could further streamline the training process and improve the convergence of models more effectively within established transformer architectures.

Conclusion

By refining transformer architectures through the elimination of redundant weights and maintaining functionality, this paper makes a significant contribution to optimizing large-scale models. The implications of such work potentially extend well beyond currently explored architectures, prompting further exploration into transformer efficiency and scalability, which is essential given the growing complexity and deployment requirements of modern AI systems. This paper not only bolsters the theoretical foundations of transformer design but also provides practical guidelines for real-world application and innovation.