Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs
<|reference_start|>Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs: The Large Language Model (LLM) is widely employed for tasks such as intelligent assistants, text summarization, translation, and multi-modality on mobile phones. However, the current methods for on-device LLM deployment maintain slow inference speed, which causes poor user experience. To facilitate high-efficiency LLM deployment on device GPUs, we propose four optimization techniques: (a) a symbolic expression-based approach to support dynamic shape model inference; (b) operator optimizations and execution priority setting to enhance inference speed and reduce phone lagging; (c) an FP4 quantization method termed M0E4 to reduce dequantization overhead; (d) a sub-tensor-based technique to eliminate the need for copying KV cache after LLM inference. Furthermore, we implement these methods in our mobile inference engine, Transformer-Lite, which is compatible with both Qualcomm and MTK processors. We evaluated Transformer-Lite's performance using LLMs with varied architectures and parameters ranging from 2B to 14B. Specifically, we achieved prefill and decoding speeds of 121 token/s and 14 token/s for ChatGLM2 6B, and 330 token/s and 30 token/s for smaller Gemma 2B, respectively. Compared with CPU-based FastLLM and GPU-based MLC-LLM, our engine attains over 10x speedup for the prefill speed and 2~3x speedup for the decoding speed.<|reference_end|>
@article{li2024transformer-lite:,
    title={Transformer-Lite: High-efficiency Deployment of Large Language Models on
  Mobile Phone GPUs},
    author={Luchang Li, Sheng Qian, Jie Lu, Lunxi Yuan, Rui Wang, Qin Xie},
    journal={arXiv preprint arXiv:2403.20041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.20041},
    primaryClass={cs.CL}
}