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Beyond KV Caching: Shared Attention for Efficient LLMs

[paper]

Abstract

The efficiency of large language models (LLMs) remains a critical challenge, particularly in contexts where computational resources are limited. Traditional attention mechanisms in these models, while powerful, require significant computational and memory resources due to the necessity of recalculating and storing attention weights across different layers. This paper introduces a novel Shared Attention (SA) mechanism, designed to enhance the efficiency of LLMs by directly sharing computed attention weights across multiple layers. Unlike previous methods that focus on sharing intermediate Key-Value (KV) caches, our approach utilizes the isotropic tendencies of attention distributions observed in advanced LLMs post-pretraining to reduce both the computational flops and the size of the KV cache required during inference. We empirically demonstrate that implementing SA across various LLMs results in minimal accuracy loss on standard benchmarks. Our findings suggest that SA not only conserves computational resources but also maintains robust model performance, thereby facilitating the deployment of more efficient LLMs in resource-constrained environments.

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Usage

Environment Setup

conda create -n shareAtt python=3.8
conda activate shareAtt

pip install torch torchvision torchaudio
pip install transformers==4.33.0 accelerate datasets scipy sentencepiece

Prepare Weights

Download the Llama-2-7B-hf weights (.safetensor files) into the models/Llama2-7b-hf folder.

Direct Application of Shared Attention

To apply Shared Attention, modify modeling_llama.py in models/Llama2-7b-hf at line 262. For instance, for SA from layers 27 to 30 (excluding layer_idx from the list):

self.share_attn = self.layer_idx not in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 31]

Reproduction of Evaluations

Install lm-evaluation-harness from EleutherAI's repository.

Replace the modeling_llama.py file in the transformers library with the modified file in models/Llama2-7b-hf.

Run the evaluation:

CUDA_VISIBLE_DEVICES=0 lm_eval --model hf --model_args pretrained=./models/Llama2-7b-hf/ --tasks mmlu,glue,gsm8k,hellaswag --batch_size auto --output_path ./eval_out/llama2-7b-23_26  --use_cache ./eval_cache/llama2-7b-23_26

Fine-tuning

Set up Accelerate with DeepSpeed:

accelerate config

Download Llama-3-8b and modify corresponding files.

Download Alpaca instruct dataset alpaca_data_cleaned.json from gururise's repository.

Train the model:

ACCELERATE_USE_DEEPSPEED=true CUDA_VISIBLE_DEVICES="0,1" accelerate launch alpaca_finetuning.py

Citation

If you find our works useful or relevant to your project and research, please kindly cite our paper:

@article{liao2024shareAtt,
        title={Beyond KV Caching: Shared Attention for Efficient LLMs},
        author={Bingli Liao and Danilo Vasconcellos Vargas},
        journal={arXiv},
        year={2024}
        }