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SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget

Abstract

Optimizing the Key-Value (KV) cache of the Large Language Model (LLM) has been considered critical to saving the cost of inference. Most of the existing KV-cache compression algorithms attempted to sparsify the sequence of tokens by taking advantage of the different importance of tokens. In this work, we found that by identifying the importance of attention layers, we could optimize the KV-cache jointly from two dimensions. Based on our observations regarding layer-wise importance in inference, we propose SqueezeAttention to precisely optimize the allocation of KV-cache budget among layers on-the-fly and then incorporate three representative token sparsification algorithms to compress the KV-cache for each layer with its very own budget. By optimizing the KV-cache from both sequence's and layer's dimensions, SqueezeAttention achieves around 30% to 70% of the memory reductions and up to 2.2 $\times$ of throughput improvements in a wide range of LLMs and benchmarks.

Installation

Use the following command to install dependencies.

pip install crfm-helm
# cuda >= 11.6
pip install -r requirements.txt

Then, install FlashAttention-2

pip install flash-attn --no-build-isolation

Next, run modify_transformers.py to modify transformers. ⚠️ Please make sure you do run this program, otherwise, error will be reported after executing the following codes.

python modify_transformers.py

Usage and Example

We provide two dataset(samsum from LongBench and xsum from HELM) and two models(Mistral and LLama2-7b-32k as examples. You can also use LLama of other sizes.

Before you run the experiments, please modify config/model2path.json to provide correct path of your models based on you machines.

Evaluation on samsum using Mistral

  1. Using Sliding Window without SqueezeAttention.

    This code will generate the output from Mistral with 21% KV Budget(0.21 * prompt length) of each layer.

    python pred.py --model Mistral --pred Long --enable_squeeze --model_arch Mistral --device cuda:0 --ini_size 0.21 --KV_class3 0.21

    Then, running eval.py, the result will be written into a json file which can be found in pred_Long/Mistral/0.21/21/result.json, you can also find detailed result in samsum.jsonl

    python eval.py --model Mistral --pred Long --ini_size 0.21 --KV_class3 0.21

  2. Using Sliding Window with SqueezeAttention.

    In program pred.py, parameter --ini_size means the initial KV Budget of each layer, --KV_calss3 means KV Budget of layers in class3(probably are second half layers) after cluster. If you set --KV_class3 to other number, SqueezeAttention will compute the KV Budget of remaining layers to ensure that the total KV Budget of all layers before and after change is equal. Here, we provide a parameter where SqueezeAttention can significantly improve score.

    python pred.py --model Mistral --pred Long --enable_squeeze --model_arch Mistral --device cuda:0 --ini_size 0.21 --KV_class3 0.08

    Running eval.py and check the result.

    python eval.py --model Mistral --pred Long --ini_size 0.21 --KV_class3 0.08

Evaluation on xsum using LLama2-7b-32k

  1. Using streamingLLM without SqueezeAttention.

    StreamingLLM needs a few "sink tokens", in our experiments, we take first 4 tokens as "sink tokens", so you should add a parameter --start_token.

    python pred.py --model llama2-7b-32k --pred xsum --enable_squeeze --model_arch llama --device cuda:0 --ini_size 0.4 --KV_class3 0.4 --sample_num 300 --start_size 4

    Evaluate xsum can be a little complicated, first, you should open script/helm.sh and modify ini_size=0.4, KV_class3=0.4 based on your last command. Then, run helm.sh.

    sh helm.sh

    The main result will be printed on your screen, you can also find detailed result in helm/benchmark_output/runs/latest.

  2. Using streaming with SqueezeAttention.

    Generating output is quite similar with Mistral, all you need to do is modifying --KV_class3.

    python pred.py --model llama2-7b-32k --pred xsum --enable_squeeze --model_arch llama --device cuda:0 --ini_size 0.4 --KV_class3 0.25 --sample_num 300 --start_size 4

    Before you evaluate the result, don't forget to change parameter --KV_class3=0.25 in script/helm.sh.

    sh helm.sh

Cite SqueezeAttention

@inproceedings{wang2024squeezeattention,
title={SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget},
author={Zihao Wang and Shaoduo Gan},
year={2024},
eprint={2404.04793},
archivePrefix={arXiv},
primaryClass={cs.LG}
}