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Achieving Sparse Activation in Small Language Models

Introduction

This is the official code repository for the paper "Achieving Sparse Activation in Small Language Models". We aim to achieve sparse activation in SLMs. We demonstrated that the existing magnitude-based sparse activation cannot be applied to SLMs, and using gradient-based attribution scores for sparse activation is a better choice. By applying a corrective term onto the existing GxO attribution metric, our approach can achieve 80% sparsification ratio on SLMs with <5% accuracy loss.

Requirement

Install all the required packages.

pip install -r requirements.txt

General Usage

We use Phi-2 and the TruthfulQA dataset to demonstrate an example of sparse activation. The following steps can be used to generate accuracy-sparsity trade-off curves based on various metrics, including the proposed Corrected GxO metric.

Folder Creation

Create the folder for generated results as following code

python3 folder_creation.py

Label Generation

Generate the labels for sparse activation

python3 label_generation.py

Mganitude Generation

we can use the following code to generate the output magnitude of each attention head and MLP neurons

python3 Mag_attention.py

python3 Mag_mlp.py

Attribution scores Generation

we can use the following code to generate the various attribution-based scores of each attention head and MLP neurons

gradient

python3 attribution_attention.py --metric gradient

Gradient*Output (GxO)

python3 attribution_attention.py --metric gxo

Integrated gradients (IG) with 20 interpolations (The number of interpolations can be any positive integer)

python3 attribution_attention.py --metric ig --n_steps 20

Apply sparse activation based on output magnitude and different attribution scores and plot the accuracy-sparsity trade-off curves

magnitude

python3 main.py --metric_name magnitude

gradient

python3 main.py --metric_name gradient

GxO

python3 main.py --metric_name gxo

SNIP

python3 main.py --metric_name snip

Corrected GxO

python3 main.py --metric_name cor_gxo

We can then check the accuracy-sparsity trade-off curves in the path: result/truthfulqa/res/both.

Citation

@article{song2024achieving,
  title={Achieving Sparse Activation in Small Language Models},
  author={Song, Jifeng and Huang, Kai and Yin, Xiangyu and Yang, Boyuan and Gao, Wei},
  journal={arXiv e-prints},
  pages={arXiv--2406},
  year={2024}
}