Awesome

training-free, high-throughput long-context LLM inference

Environment Set Up

To reproduce the results in the paper, you need to set up the environment as follows with a single A100 GPU:

# create env
conda create -n ShadowKV python=3.10 -y
conda activate ShadowKV

# install packages
pip install -r requirements.txt
pip install flash-attn --no-build-isolation

# nemo dependencies (for dataset building)
pip install wheel
pip install Cython
pip install youtokentome
pip install nemo_toolkit[all]==1.23

# flashinfer
pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3/

# cutlass
mkdir 3rdparty
git clone https://github.com/NVIDIA/cutlass.git 3rdparty/cutlass

# build kernels for ShadowKV
python setup.py build_ext --inplace

Supported Models

Currently, we support the following LLMs:

• Llama-3-8B-1M: gradientai/Llama-3-8B-Instruct-Gradient-1048k
• GLM-4-9B-1M: THUDM/glm-4-9b-chat-1m
• Llama-3.1-8B: meta-llama/Meta-Llama-3.1-8B-Instruct
• Yi-9B-200K: 01-ai/Yi-9B-200K
• Phi-3-Mini-128K: microsoft/Phi-3-mini-128k-instruct (only NIAH test supported)
• Qwen2-7B-128K: Qwen/Qwen2-7B-Instruct (only NIAH test supported)

Accuracy Evaluations

Here we provide an example to build the dataset and run evaluation for the RULER benchmark with Llama-3-8B-1M.

Build Datasets

To build RULER dataset, please run the following command:

# build RULER
python -c "import nltk; nltk.download('punkt')"
cd data/ruler
bash create_dataset.sh "gradientai/Llama-3-8B-Instruct-Gradient-1048k" "llama-3"

Run Evaluations

For the accuracy evaluation, please run the following command with 8xA100 GPUs:

# Full attention
OMP_NUM_THREADS=48 torchrun --standalone --nnodes=1 --nproc_per_node 8 test/eval_acc.py --datalen 131072 --method full --dataset_name "ruler/niah_single_1,ruler/niah_single_2,ruler/niah_single_3,ruler/niah_multikey_1,ruler/niah_multikey_2,ruler/niah_multiquery,ruler/niah_multivalue,ruler/vt,ruler/fwe,ruler/qa_1,ruler/qa_2" --model_name "gradientai/Llama-3-8B-Instruct-Gradient-1048k"

# ShadowKV
OMP_NUM_THREADS=48 torchrun --standalone --nnodes=1 --nproc_per_node 8 test/eval_acc.py --datalen 131072 --method shadowkv --dataset_name "ruler/niah_single_1,ruler/niah_single_2,ruler/niah_single_3,ruler/niah_multikey_1,ruler/niah_multikey_2,ruler/niah_multiquery,ruler/niah_multivalue,ruler/vt,ruler/fwe,ruler/qa_1,ruler/qa_2" --sparse_budget 2048 --rank 160 --chunk_size 8

Compatibility with MInference

ShadowKV is compatible with pre-filling acceleration techniques, such as MInference. To enable MInference, please add the --minference flag to the command. For example:

# Full attention with MInference
OMP_NUM_THREADS=48 torchrun --standalone --nnodes=1 --nproc_per_node 8 test/eval_acc.py --datalen 131072 --method full --dataset_name "ruler/niah_single_1,ruler/niah_single_2,ruler/niah_single_3,ruler/niah_multikey_1,ruler/niah_multikey_2,ruler/niah_multiquery,ruler/niah_multivalue,ruler/vt,ruler/fwe,ruler/qa_1,ruler/qa_2" --minference

# ShadowKV with MInference
OMP_NUM_THREADS=48 torchrun --standalone --nnodes=1 --nproc_per_node 8 test/eval_acc.py --datalen 131072 --method shadowkv --dataset_name "ruler/niah_single_1,ruler/niah_single_2,ruler/niah_single_3,ruler/niah_multikey_1,ruler/niah_multikey_2,ruler/niah_multiquery,ruler/niah_multivalue,ruler/vt,ruler/fwe,ruler/qa_1,ruler/qa_2" --sparse_budget 2048 --rank 160 --chunk_size 8 --minference

Efficiency Evaluations

For the efficiency evaluation, please run the following command with a single A100 GPU:

python test/e2e.py --model_name "meta-llama/Meta-Llama-3.1-8B-Instruct" --datalen "122k"

Citation

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

@article{sun2024shadowkv,
  title={ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference},
  author={Sun, Hanshi and Chang, Li-Wen and Bao, Wenlei and Zheng, Size and Zheng, Ningxin and Liu, Xin and Dong, Harry and Chi, Yuejie and Chen, Beidi},
  journal={arXiv preprint arXiv:2410.21465},
  year={2024}
}