While the additive quantization (AQLM) approach offers significant advantages in terms of compression efficiency and accuracy, it also has potential limitations when compared to other compression techniques such as sparse or hybrid quantization:
Complexity of Implementation: AQLM's reliance on multiple codebooks and the optimization of discrete codes can lead to increased implementation complexity. This complexity may result in longer training times and require more sophisticated optimization techniques, making it less accessible for practitioners compared to simpler methods like sparse quantization.
Sensitivity to Initialization: The performance of AQLM can be sensitive to the initialization of codebooks. If the initial codebooks are not representative of the weight distributions, the algorithm may converge to suboptimal solutions, leading to higher quantization errors. In contrast, sparse quantization techniques often have more robust initialization strategies.
Limited Handling of Outliers: AQLM may struggle with outlier weights, which can significantly impact the accuracy of the quantized model. While hybrid quantization techniques often incorporate mechanisms to handle outliers effectively, AQLM's homogeneous format may not provide the same level of robustness against extreme values.
Trade-off Between Compression and Accuracy: Although AQLM aims to achieve low bit-width quantization (e.g., 2-3 bits), this extreme compression can lead to significant accuracy drops, especially in smaller models. Other techniques, such as hybrid quantization, may offer better trade-offs between model size and accuracy by allowing for mixed precision representations.
Scalability Issues: As model sizes increase, the computational overhead associated with AQLM's multi-codebook approach may become a bottleneck. Sparse and hybrid quantization methods can often scale more effectively, allowing for faster inference times and lower memory footprints.
In summary, while AQLM presents a promising approach to extreme compression of LLMs, its complexity, sensitivity to initialization, handling of outliers, trade-offs between compression and accuracy, and scalability issues may limit its applicability compared to other compression techniques.