STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914966930456576 |
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| author | Dong, Peijie Li, Lujun Zhong, Yuedong Du, Dayou Fan, Ruibo Chen, Yuhan Tang, Zhenheng Wang, Qiang Xue, Wei Guo, Yike Chu, Xiaowen |
| author_facet | Dong, Peijie Li, Lujun Zhong, Yuedong Du, Dayou Fan, Ruibo Chen, Yuhan Tang, Zhenheng Wang, Qiang Xue, Wei Guo, Yike Chu, Xiaowen |
| contents | In this paper, we present the first structural binarization method for LLM compression to less than 1-bit precision. Although LLMs have achieved remarkable performance, their memory-bound nature during the inference stage hinders the adoption of resource-constrained devices. Reducing weights to 1-bit precision through binarization substantially enhances computational efficiency. We observe that some weights in binarized LLMs can be randomly flipped without significant performance degradation, suggesting the potential for further compression. To exploit this, our STBLLM employs an N:M sparsity technique to achieve structural binarization of the weights. Specifically, we introduce a novel Standardized Importance (SI) metric, which considers weight magnitude and input feature norm to more accurately assess weight significance. Then, we propose a layer-wise approach, allowing different layers of the LLM to be sparsified with varying N:M ratios, thereby balancing compression and accuracy. Furthermore, we implement a fine-grained grouping strategy for less important weights, applying distinct quantization schemes to sparse, intermediate, and dense regions. Finally, we design a specialized CUDA kernel to support structural binarization. We conduct extensive experiments on LLaMA-1/2/3, OPT family, and Mistral to evaluate the effectiveness of STBLLM. The results demonstrate that our approach performs better than other compressed binarization LLM methods while significantly reducing memory requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs Dong, Peijie Li, Lujun Zhong, Yuedong Du, Dayou Fan, Ruibo Chen, Yuhan Tang, Zhenheng Wang, Qiang Xue, Wei Guo, Yike Chu, Xiaowen Machine Learning Computation and Language In this paper, we present the first structural binarization method for LLM compression to less than 1-bit precision. Although LLMs have achieved remarkable performance, their memory-bound nature during the inference stage hinders the adoption of resource-constrained devices. Reducing weights to 1-bit precision through binarization substantially enhances computational efficiency. We observe that some weights in binarized LLMs can be randomly flipped without significant performance degradation, suggesting the potential for further compression. To exploit this, our STBLLM employs an N:M sparsity technique to achieve structural binarization of the weights. Specifically, we introduce a novel Standardized Importance (SI) metric, which considers weight magnitude and input feature norm to more accurately assess weight significance. Then, we propose a layer-wise approach, allowing different layers of the LLM to be sparsified with varying N:M ratios, thereby balancing compression and accuracy. Furthermore, we implement a fine-grained grouping strategy for less important weights, applying distinct quantization schemes to sparse, intermediate, and dense regions. Finally, we design a specialized CUDA kernel to support structural binarization. We conduct extensive experiments on LLaMA-1/2/3, OPT family, and Mistral to evaluate the effectiveness of STBLLM. The results demonstrate that our approach performs better than other compressed binarization LLM methods while significantly reducing memory requirements. |
| title | STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2408.01803 |