Relative Importance and Activations (RIA)

PLUG-AND-PLAY: AN EFFICIENT POST-TRAINING PRUNING METHOD FOR LARGE LANGUAGE MODELS

Yingtao Zhang^1,2, Haoli Bai⁴, Haokun Lin⁵, Jialin Zhao^1,2, Lu Hou⁴, & Carlo Vittorio Cannistraci^1,2,3

¹ Center for Complex Network Intelligence, Tsinghua Laboratory of Brain and Intelligence
² Department of Computer Science, Tsinghua University
³ Department of Biomedical Engineering, Tsinghua University
⁴ Huawei Noah’s Ark Lab
⁵ Institute of Automation, Chinese Academy of Sciences

Corresponding to {zhangyingtao1024, kalokagathos.agon}@gmail.com

Setup

Step 1: Create a new conda environment:

conda create -n ria python=3.10
conda activate ria

Step 2: Install relevant packages

pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/nightly/cu121

Step 3: install lm-evaluation-harness (if --eval_zero_shot)

Follow the installation here: https://github.com/EleutherAI/lm-evaluation-harness

Usage

RIA with unstructured 50% sparsity

python main.py \
	--model YOUR_MODEL_NAME \
	--prune_method ria \
	--sparsity_ratio 0.5 \
	--sparsity_type unstructured \
	--save \

Here the prune_method can be replaced with wanda, sparsegpt, ri, magnitude

RIA with semi-structured sparsity

python main.py \
	--model YOUR_MODEL_NAME \
	--prune_method ria \
	--sparsity_ratio 0.5 \
	--sparsity_type 2:4 \
	--save \

sparsity_type can be any type of semi-structured sparsity pattern, for instance: 1:4, 2:4.

Enable --reallocation if you want to use heuristic channel reallocation.

Enable --lsa if you want to further finetune the channels after reallocation with linear sum assignment.

Enable --fast if you want to use a fast version of linear sum assignment.

End-to-End inference speedup with semi-structured sparsity

---

Currently, this repo only supports the acceleration after direct N:M sparsity. The acceleration of N:M sparsity after channel permutation is still under testing.

python main.py \
	--model YOUR_MODEL_NAME \
	--prune_method ria \
	--sparsity_ratio 0.5 \
	--sparsity_type 2:4 \
	--semi_sparse_acc \
	--save \

Requirements:

• PyTorch >= 2.1.
• A NVIDIA GPU with semi-structured sparsity support (Compute Capability 8.0+).

Make sure that your GPU support cusparselt, otherwise please set

SparseSemiStructuredTensor._FORCE_CUTLASS = True

Which force to use CUTLASS

Additional Experimental Results on LLaMA3

---

LLaMA3-8B on Wikitext2: fully connected: PPL 6.14

	50% unstructured sparsity	2:4	2:4+Channel Permutation	4:8
Magnitude	2499.39
Relative Importance	135.77
Wanda	10.82	24.18	22.03
Sparsegpt	9.40	16.26		12.13
Ria	9.34	23.08	20.05

LLaMA3-70B on Wikitext2: fully connected: PPL 2.85

	50% unstructured sparsity	2:4	2:4+Channel Permutation	4:8
Magnitude	19.11
Relative Importance	6.09
Wanda	6.56	9.28
Sparsegpt	5.79
Ria	5.49	8.35

Acknowledgment

---

This repository is built upon the SparseGPT and Wanda repository.

Citation

---

If you use our code, please consider to cite:

@inproceedings{
zhang2024plugandplay,
title={Plug-and-Play: An Efficient Post-training Pruning Method for Large Language Models},
author={Yingtao Zhang and Haoli Bai and Haokun Lin and Jialin Zhao and Lu Hou and Carlo Vittorio Cannistraci},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Tr0lPx9woF}
}