MAC-Attention: a Match-Amend-Complete Scheme for Fast and Accurate Attention Computation

1. 1Identify semantically similar tokens in the input sequence
2. 2Reuse prior attention computations for these tokens
3. 3Amend the computations as needed to maintain accuracy
4. 4Complete the attention computation by combining the reused and amended computations

Machine learning researchers and engineers working on large language models (LLMs) can benefit from MAC-Attention to improve decoding efficiency without sacrificing fidelity or accessibility

💡 Reusing prior attention computations for semantically similar tokens can significantly accelerate decoding in LLMs

💡 Accelerate LLM decoding with MAC-Attention, preserving fidelity and access