FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient Finetuning

• URL: http://arxiv.org/abs/2402.18789v1
• Date: Thu, 29 Feb 2024 01:33:08 GMT
• Title: FlexLLM: A System for Co-Serving Large Language Model Inference and Parameter-Efficient Finetuning
• Authors: Xupeng Miao, Gabriele Oliaro, Xinhao Cheng, Mengdi Wu, Colin Unger, Zhihao Jia
• Abstract summary: Existing systems cannot handle workloads that include a mix of inference and PEFT finetuning requests. We present FlexLLM, the first system that can serve inference and parameter-efficient finetuning requests in the same iteration. Compared to existing systems, FlexLLM's co-serving approach reduces the activation GPU memory overhead by up to 8x, and the end-to-end GPU memory requirement of finetuning by up to 36%.
• Score: 9.979010592887096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-efficient finetuning (PEFT) is a widely used technique to adapt large language models for different tasks. Service providers typically create separate systems for users to perform PEFT model finetuning and inference tasks. This is because existing systems cannot handle workloads that include a mix of inference and PEFT finetuning requests. As a result, shared GPU resources are underutilized, leading to inefficiencies. To address this problem, we present FlexLLM, the first system that can serve inference and parameter-efficient finetuning requests in the same iteration. Our system leverages the complementary nature of these two tasks and utilizes shared GPU resources to run them jointly, using a method called co-serving. To achieve this, FlexLLM introduces a novel token-level finetuning mechanism, which breaks down the finetuning computation of a sequence into smaller token-level computations and uses dependent parallelization and graph pruning, two static compilation optimizations, to minimize the memory overhead and latency for co-serving. Compared to existing systems, FlexLLM's co-serving approach reduces the activation GPU memory overhead by up to 8x, and the end-to-end GPU memory requirement of finetuning by up to 36% while maintaining a low inference latency and improving finetuning throughput. For example, under a heavy inference workload, FlexLLM can still preserve more than 80% of the peak finetuning throughput, whereas existing systems cannot make any progress with finetuning. The source code of FlexLLM is publicly available at https://github.com/flexflow/FlexFlow.

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