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KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

Implementation of KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

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Overview

KIVI is a new plug-and-play 2bit KV cache quantization algorithm without any fine-tuning. This algorithm optimizes memory usage by quantizing the key cache per-channel and the value cache per-token to 2bit. KIVI's hardware-friendly design allows LLMs like Llama-2, Falcon, and Mistral to maintain comparable quality levels while reducing peak memory usage by 2.6 times. This enables up to 4 times larger batch sizes and significantly increases throughput by 2.35 to 3.47 times in real LLM inference workloads, effectively addressing the bottleneck issues in speed and memory usage.

Illustration of KIVI quantization scheme: key cache per-channel and value cache per-token.

<p align="center"> <img width="300" src="./img/quant_scheme.png"> </p>

Illustration of KIVI algorithm during inference prefill and decoding phase:

<p align="center"> <img width="700" src="./img/algo.png"> </p>

How to use KIVI

Setup

To install the required packages:

conda create -n kivi python=3.10
conda activate kivi
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

Then install our CUDA implementation:

cd quant && pip install -e .

Example

Load model with KIVI: (e.g., Llama-2-7b)

# LLaMA model with KIVI
import torch
import os
from models.llama_kivi import LlamaForCausalLM_KIVI
from transformers import LlamaConfig, AutoTokenizer
config = LlamaConfig.from_pretrained("meta-llama/Llama-2-7b-hf")

config.k_bits = K_BITS # current support 2/4 bit for KV Cache
config.v_bits = V_BITS # current support 2/4 bit for KV Cache
config.group_size = GROUP_SIZE
config.residual_length = RESIDUAL_LENGTH # the number of recent fp16 tokens
CACHE_DIR = PATH_TO_YOUR_SAVE_DIR

model = LlamaForCausalLM_KIVI.from_pretrained(
    pretrained_model_name_or_path='meta-llama/Llama-2-7b-hf',
    config=config,
    cache_dir=CACHE_DIR,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained(
    'meta-llama/Llama-2-7b-hf', 
    use_fast=False, 
    trust_remote_code=True, 
    tokenizer_type='llama')

# Inference
# e.g., model.generate(...)

GSM8K example

We use GSM8K as an example to show how to use KIVI. You can check example.py:

python example.py

Passkey retrieval example

Passkey retrieval with KIVI. You can check long_context_example.py:

python long_context_example.py

Evaluate KIVI on LongBench

We currently support Llama and Mistral family of models. We recently test KIVI on Mistral-7B-Instruct-v0.2 and Longchat-7b-v1.5-32k. Please check long_bench.md for more details.

bash scripts/long_test.sh {GPU_ID} {K_BITS} {V_BITS} {GROUP_LENGTH} {RESIDUAL_LENGTH} {MODEL_NAME}
python eval_long_bench.py --model {MODEL} # MODEL is the dir name under pred/ Currently it support Llama family model and Mistral model.

Citation

If you find our method useful, please kindly cite our paper.

@article{liu2024kivi,
  title={KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache},
  author={Liu, Zirui and Yuan, Jiayi and Jin, Hongye and Zhong, Shaochen and Xu, Zhaozhuo and Braverman, Vladimir and Chen, Beidi and Hu, Xia},
  journal={arXiv preprint arXiv:2402.02750},
  year={2024}
}

Contributing

We welcome contributions from the research community to improve KIVI. If you have any idea or would like to report a bug, please open an issue or submit a pull request.

License

The code is released under the MIT License.