Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs

• URL: http://arxiv.org/abs/2405.14597v2
• Date: Tue, 28 May 2024 07:17:47 GMT
• Title: Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
• Authors: Qingyuan Li, Ran Meng, Yiduo Li, Bo Zhang, Yifan Lu, Yerui Sun, Lin Ma, Yuchen Xie,
• Abstract summary: Scale is a free lunch as it requires no extra calibration or fine-tuning which will otherwise incur additional costs. It can be used plug-and-play for most fine-grained quantization methods.
• Score: 11.418680497763445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantization approaches while maintaining similar accuracies. Integer Scale is a free lunch as it requires no extra calibration or fine-tuning which will otherwise incur additional costs. It can be used plug-and-play for most fine-grained quantization methods. Its integration results in at most 1.85x end-to-end speed boost over the original counterpart with comparable accuracy. Additionally, due to the orchestration of the proposed Integer Scale and fine-grained quantization, we resolved the quantization difficulty for Mixtral-8x7B and LLaMA-3 models with negligible performance degradation, and it comes with an end-to-end speed boost of 2.13x, and 2.31x compared with their FP16 versions respectively.

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