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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.14590 |
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| _version_ | 1866915948871548928 |
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| author | Zheng, Zhen Song, Xiaonan Liu, Chuanjie |
| author_facet | Zheng, Zhen Song, Xiaonan Liu, Chuanjie |
| contents | Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or low system efficiency. In this paper, we propose MixLLM that explores the optimization space of mixed-precision quantization between output features, based on the insight that different features matter differently in the model. MixLLM identifies the important output features in the global view rather than within each single layer, effectively assigning larger bit-width to output features that need it the most to achieve high accuracy and low memory usage. We present the sweet spot of quantization configuration of algorithm-system co-design with high accuracy and system efficiency. To address the system challenge, we design the two-step dequantization to make use of the Tensor Core easily and fast data type conversion to reduce dequantization overhead, and present the software pipeline to overlap the memory access, dequantization and the MatMul to the best. Extensive experiments show that with only 10\% more bits, the perplexity increase can be reduced from about 0.5 in SOTA to within 0.2 for Llama 3.1 70B, while MMLU-Pro loss can be reduced from 1.92 to 0.99 over the SOTA of three popular models. Besides its superior accuracy, MixLLM also achieves state-of-the-art system efficiency. Code is released at https://github.com/microsoft/MixLLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14590 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design Zheng, Zhen Song, Xiaonan Liu, Chuanjie Machine Learning Quantization has become one of the most effective methodologies to compress LLMs into smaller size. However, the existing quantization solutions still show limitations of either non-negligible accuracy drop or low system efficiency. In this paper, we propose MixLLM that explores the optimization space of mixed-precision quantization between output features, based on the insight that different features matter differently in the model. MixLLM identifies the important output features in the global view rather than within each single layer, effectively assigning larger bit-width to output features that need it the most to achieve high accuracy and low memory usage. We present the sweet spot of quantization configuration of algorithm-system co-design with high accuracy and system efficiency. To address the system challenge, we design the two-step dequantization to make use of the Tensor Core easily and fast data type conversion to reduce dequantization overhead, and present the software pipeline to overlap the memory access, dequantization and the MatMul to the best. Extensive experiments show that with only 10\% more bits, the perplexity increase can be reduced from about 0.5 in SOTA to within 0.2 for Llama 3.1 70B, while MMLU-Pro loss can be reduced from 1.92 to 0.99 over the SOTA of three popular models. Besides its superior accuracy, MixLLM also achieves state-of-the-art system efficiency. Code is released at https://github.com/microsoft/MixLLM. |
| title | MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.14590 |