The AQLM algorithm, which focuses on the additive quantization of weights in large language models (LLMs), could be extended to quantize both weights and activations by incorporating a dual quantization framework. This would involve the following steps:
Joint Optimization Framework: AQLM could be modified to include a joint optimization process that simultaneously quantizes weights and activations. This would require the algorithm to consider the distribution of both weights and activations during the quantization process, ensuring that the quantization of one does not adversely affect the other.
Adaptive Codebook Learning: Similar to how AQLM learns codebooks for weights, the algorithm could also learn separate codebooks for activations. This would involve adapting the existing codebook learning mechanism to account for the unique characteristics of activation distributions, which may differ significantly from weight distributions.
Layer-wise Calibration: The algorithm could implement a layer-wise calibration approach where both weights and activations are calibrated based on the input data. This would ensure that the quantization process is sensitive to the specific input distributions encountered during inference, thereby improving the overall accuracy of the model.
Error Minimization: The objective function could be expanded to minimize the reconstruction error for both weights and activations. This would involve formulating a combined loss function that captures the squared error between the original and quantized outputs for both components, allowing for a more holistic approach to quantization.
Fine-tuning Integration: Finally, the AQLM algorithm could integrate fine-tuning mechanisms that adjust both weights and activations post-quantization. This would help in mitigating any accuracy loss incurred during the quantization process, ensuring that the model remains performant on downstream tasks.
By implementing these strategies, AQLM could effectively extend its capabilities to quantize both weights and activations, thereby enhancing the compression efficiency and performance of large language models.