RPTQ: Reorder-based Post-training Quantization for Large Language Models (2304.01089v4)

Abstract: Large-scale 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%.

Reorder-based Post-training Quantization for LLMs (RPTQ)

The paper "RPTQ: Reorder-based Post-training Quantization for LLMs" addresses the significant challenge of deploying large-scale LLMs due to their extensive memory requirements. LLMs, such as the OPT-175B model, with 175 billion parameters, are formidable in their performance but demand substantial resources for storage and computation. The authors propose a novel technique, RPTQ, to reduce the memory footprint through quantization, particularly focusing on activation quantization.

Key Insights and Approach

The research identifies that the primary difficulty in quantizing LLM activations arises not only due to outliers but significantly from varying value ranges across channels. Conventional quantization strategies often fail to address these range discrepancies, leading to substantial quantization errors. The innovation of RPTQ lies in its approach to cluster channel activations based on their value ranges, enabling more precise quantization.

• Channel Clustering: The method clusters channels exhibiting similar activation ranges, employing a reorder operation. Instead of treating outliers separately, this clustering allows for differentiated quantization parameters across channels. The clustering is done using the K-Means algorithm where channels are grouped based on their maximum and minimum activation values.
• Mechanism and Implementation: RPTQ efficiently integrates the reorder operation into existing processes like layer normalization, reducing overhead. It modifies the layer norm operation such that reordered activations are produced directly. Furthermore, the weight matrices in linear layers are pre-organized to match the reordered activations, eliminating misalignment during inference.

Experimental Results and Implications

The experiments conducted on several models, including OPT-175B, demonstrated that RPTQ facilitates 3-bit activation quantization for the first time, achieving notable reductions in memory usage without significant losses in accuracy. For example, quantizing the OPT-175B model yielded memory reductions of up to 80%, indicating a substantial impact on the efficiency of deploying large sequence-length tasks.

• Perplexity and Zero-shot Task Performance: Across various datasets, the quantized models retained high accuracy levels. The use of fewer bits in activation quantization under the new method displayed competitive perplexity scores compared to FP16 models, maintaining accuracy across diverse zero-shot tasks.
• Memory Efficiency: The paper reports impressive memory savings, which are critical for high-performance computing applications that involve LLMs. Models like OPT-175B saw reductions in memory usage from over 300 GB to just over 60 GB under certain configurations.

Future Directions

The implications of this research lie predominantly in the computational efficiency it offers for deploying LLMs. This advancement holds promise for enabling large-scale models even under constrained resources such as single GPUs, facilitating broader accessibility and application potential in AI-driven fields. Future research may explore further integration of this quantization technique with other model compression strategies or its applicability to different architectures beyond transformer-based LLMs. Additionally, addressing computational challenges related to real-time reordering and exploring adaptive quantization schemes may enhance the efficacy and deployment flexibility of RPTQ.