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arxiv:2309.14717

QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Published on Sep 26, 2023
· Submitted by akhaliq on Sep 27, 2023
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Abstract

Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden largely restricts the application of LLMs especially when one needs to deploy them onto edge devices. In this paper, we propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation, and the solution is to use group-wise operators which increase the degree of freedom of quantization meanwhile decreasing that of adaptation. QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy. We apply QA-LoRA to the LLaMA and LLaMA2 model families and validate its effectiveness in different fine-tuning datasets and downstream scenarios. Code will be made available at https://github.com/yuhuixu1993/qa-lora.

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Here is an ML-generated summary

Objective
The paper proposes QA-LoRA, a quantization-aware low-rank adaptation method to efficiently fine-tune and deploy large language models by balancing the degrees of freedom between quantization and adaptation.

Insights

  • Quantization-awareness is important for joint optimization of quantization and adaptation. Otherwise post-training quantization causes accuracy loss.
  • There is an imbalance between the degrees of freedom of quantization and adaptation in methods like QLoRA. This imbalance causes large quantization errors.
  • Introducing group-wise operations increases quantization flexibility and reduces adaption parameters to achieve better balance.
  • QA-LoRA allows end-to-end INT4 quantization without post-training quantization. It is efficient and achieves higher accuracy than QLoRA.
  • QA-LoRA works well across different model sizes, tasks, and is particularly effective in very low-bit quantization like INT2/INT3.

Implementation

  • Quantization-awareness is important for joint optimization of quantization and adaptation. Otherwise post-training quantization causes accuracy loss.
  • There is an imbalance between the degrees of freedom of quantization and adaptation in methods like QLoRA. This imbalance causes large quantization errors.
  • Introducing group-wise operations increases quantization flexibility and reduces adaption parameters to achieve better balance.
  • QA-LoRA allows end-to-end INT4 quantization without post-training quantization. It is efficient and achieves higher accuracy than QLoRA.
  • QA-LoRA works well across different model sizes, tasks, and is particularly effective in very low-bit quantization like INT2/INT3.

Results
Experiments on LLaMA models show QA-LoRA consistently outperforms QLoRA in accuracy, especially in low-bit quantization, while being efficient without post-training quantization.

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This is really cool!

Here is a summary of the key points from the paper:

  • The paper proposes a new method called Quantization-Aware Low-Rank Adaptation (QA-LoRA) for efficient fine-tuning and deployment of large language models (LLMs).

  • QA-LoRA introduces quantization awareness into the low-rank adaptation process. It balances the degrees of freedom between quantization and adaptation by using group-wise operations.

  • This allows QA-LoRA to quantize the LLM weights into low-bit integers during fine-tuning to reduce memory and computation. The quantized weights can be merged with the low-rank adapter weights after fine-tuning without accuracy loss.

  • Experiments on LLaMA and LLaMA2 models show QA-LoRA outperforms methods like QLoRA and PEQA, especially for smaller models and lower bit widths. It is efficient in both tuning and inference.

Steps to reproduce QA-LoRA:

  1. Obtain pretrained LLM like LLaMA or LLaMA2 as the base model

  2. Define quantization settings - group size, bit width

  3. Implement the group-wise quantization and pooling operations

  4. Initialize and optimize the low-rank adapter parameters

  5. Quantize weights during fine-tuning backprop and merge with adapters after fine-tuning

  6. Evaluate on benchmarks like MMLU, commonsense QA

FAQ:

Q: What is the key advantage of QA-LoRA?

A: QA-LoRA allows efficient deployment of quantized LLMs without accuracy loss that normally occurs during post-training quantization.

Q: How does QA-LoRA work?

A: It uses group-wise quantization and low-rank adapters to balance the degrees of freedom and reduce quantization errors.

Q: What models can use QA-LoRA?

A: QA-LoRA can work with any large pretrained language model like BERT, GPT-3, T5 as the base model.

Q: Does QA-LoRA improve accuracy over baseline methods?

A: Yes, QA-LoRA shows gains over QLoRA and PEQA especially for smaller models and aggressive quantization.

Q: What are the limitations of QA-LoRA?

A: The group size hyperparameter may need tuning for optimal efficiency-accuracy tradeoff. More analysis on very low-bit quantization can be done.

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Unfortunately, the original repo: https://github.com/yuhuixu1993/qa-lora is no longer available.

Hi Team,
Need your help.
How we can use QA-LoRA for Vision Foundation models like OWL-ViT and Grounding Dino? If we need to deploy the VFM in edge devices we need to reduce the memory size of VFM. Can we use QA-LoRA for this purpose.

Your feedback and reference link will be useful.

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