RPTQ: Reorder-based Post-training Quantization for Large Language Models
This work addresses the deployment problem for users of LLMs by enabling more efficient memory usage, though it is incremental as it builds on existing quantization techniques.
The paper tackles the challenge of high memory usage in large language models (LLMs) by introducing RPTQ, a reorder-based post-training quantization method that addresses varying activation ranges across channels, achieving a breakthrough with 3-bit activations and reducing memory consumption by up to 80% for models like OPT-175b.
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be alleviated through quantization. In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers. To address this challenge, we introduce a quantization method called RPTQ, which utilizes a reorder-based approach. By rearranging the channels and quantizing them in clusters, RPTQ effectively mitigates the impact of range differences between channels. To minimize the overhead of the reorder operation, we fuse it into the layer norm operation and weights in linear layers. In our experiments, RPTQ achieved a significant breakthrough by utilizing 3-bit activation in LLMs for the first time, resulting in a substantial reduction in memory usage. For instance, quantizing OPT-175b can lead to a memory consumption reduction of up to 80%.