Batched Low-Rank Adaptation of Foundation Models
Yeming Wen, Swarat Chaudhuri
TL;DR
This paper introduces FLoRA, a batching framework for low-rank adaptation of foundation models that enables efficient handling of diverse, task-specific requests in real-time applications without sacrificing performance.
Contribution
FLoRA extends LoRA by allowing per-input low-rank weights in minibatches, improving efficiency for personalized, multi-task inference scenarios.
Findings
FLoRA achieves competitive results on MultiPL-E code generation benchmark.
FLoRA performs well on multilingual speech recognition tasks.
FLoRA maintains LoRA's performance advantages in diverse tasks.
Abstract
Low-Rank Adaptation (LoRA) has recently gained attention for fine-tuning foundation models by incorporating trainable low-rank matrices, thereby reducing the number of trainable parameters. While LoRA offers numerous advantages, its applicability for real-time serving to a diverse and global user base is constrained by its incapability to handle multiple task-specific adapters efficiently. This imposes a performance bottleneck in scenarios requiring personalized, task-specific adaptations for each incoming request. To mitigate this constraint, we introduce Fast LoRA (FLoRA), a framework in which each input example in a minibatch can be associated with its unique low-rank adaptation weights, allowing for efficient batching of heterogeneous requests. We empirically demonstrate that FLoRA retains the performance merits of LoRA, showcasing competitive results on the MultiPL-E code…
Peer Reviews
Decision·ICLR 2024 oral
The paper presents several strong points. The proposed approach improves latency and throughput as well as a theoretical cost estimation. Several model sizes from starCorder and LLama 2 are considered for throughput and latency estimation. The accuracy of the proposed method is similar or better to that of LORA and IA3 and report improvements/checks on several models such as Llama2, whisper or starCoder.
The approach requires re-adapting the models that have already been adapted with LORA to leverage the improvements. There is a breaking point where FLORA doesn't improve over LORA effectively. Intuitively, there is at least 4 factors for this: the model, the gpu architecture, the rank of the adaptation and the batch size . The rank is taken into account but it is not very clear how the other elements will come into play in practice. Eq 7 claims only important factors are the dimension of the mul
The paper clearly introduces the problem and the contributions compared to the state of the art. The contribution is significant to cope with practical challenges of using foundation models in real-time serving scenarios, especially when considering world-wide incoming requests. The paper looks theoretically and technically sound and the presentation is clear, well framed in the context, and easy to follow.
I don’t find major weaknesses. Minor comments are indicated in the following section.
1. The orientation is clear. It can important to equip language models with various task-specific adapters for diverse requests. The overall idea is well-motivated. 2. The formulation is clear and analysis of computational consumption is in detailed.
1. If each example in a minibatch has its own adapters, the overall performance is expected to overcome LoRA, however, it's almost the same as LoRA. So the "performance bottleneck in scenarios requiring personalized, task-specific adaptations for each incoming request" isn't largely solved. 2. The whole mechanism and the algorithm isn't mentioned clearly. e.g., how to choose the batch size for real situations, how to make each example corresponding to its appropriate adapters during inference. T
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Taxonomy
TopicsSpeech and Audio Processing · Speech Recognition and Synthesis · Indoor and Outdoor Localization Technologies
MethodsBalanced Selection