TensorLLM

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[IJCNN 2025] This is the repo for paper "TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs" (https://arxiv.org/abs/2501.15674)

TensorLLM 최신버젼 다운로드

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<p align="center">
    
    <a href="https://arxiv.org/abs/2501.15674">
        <img src="https://img.shields.io/badge/arXiv-2501.15674-b31b1b?logo=arxiv&logoColor=white">
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    <a href="https://ieeexplore.ieee.org/document/11228585">
        <img src="https://img.shields.io/badge/IEEE%20Xplore-Paper-00629B?logo=ieee&logoColor=white">
    </a>
    
    <a href="https://medium.com/@guyuxuan9/tensorllm-tensorising-multi-head-attention-for-enhanced-reasoning-and-compression-in-llms-e5da445aacf3">
        <img src="https://img.shields.io/badge/Medium-Article-000000?logo=medium&logoColor=white">
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</p>
<h1 align="center">TensorLLM</h1>

<p align="center">
    Yuxuan Gu, Wuyang Zhou, Giorgos Iacovides, Danilo Mandic
</p>

This repository contains the implementation of **TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs**.

**Overview** : The reasoning abilities of Large Language Models (LLMs) can be improved by structurally denoising their weights, yet existing techniques primarily focus on denoising the feed-forward network (FFN) of the transformer block, and can not efficiently utilise the Multi-head Attention (MHA) block, which is the core of transformer architectures. To address this issue, we propose a novel intuitive framework that, at its very core, performs MHA compression through a multi-head tensorisation process and the Tucker decomposition. This enables both higher-dimensional structured denoising and compression of the MHA weights, by enforcing a shared higher-dimensional subspace across the weights of the multiple attention heads. We demonstrate that this approach consistently enhances the reasoning capabilities of LLMs across multiple benchmark datasets, and for both encoder-only and decoder-only architectures, while achieving compression rates of up to ∼250 times in the MHA weights, all without requiring any additional data, training, or fine-tuning. Furthermore, we show that the proposed method can be seamlessly combined with existing FFN-only-based denoising techniques to achieve further improvements in LLM reasoning performance.
![image](https://raw.githubusercontent.com/guyuxuan9/TensorLLM/main/methodology.png)

## Setting Up the Environment

To avoid version conflicts, it is recommended to use a separate Conda environment. Follow these steps to set up:
1. Modify the create_env.sh script:
    - Update the Conda path on **line 19**:
        ```bash
        eval "$(~/miniforge3/bin/conda shell.bash hook)"
        ```
        Replace ```~/miniforge3/bin/conda``` with the correct path to your Conda installation.
2. Run the installation script:
    ```bash
    chmod +x create_script.sh
    ./create_script.sh
    ```
    The setup takes approximately 3 minutes.
3. Initialize Conda and activate the environment:
    ```bash
    # Replace '~/miniforge3/bin/conda' with the path to your conda nstallation
    eval "$(~/miniforge3/bin/conda shell.bash hook)"
    conda activate TensorLLM
    ```

## Experiment Modes
You can run experiments in the following modes:
- **4D_Tucker (our method only)**: Tucker decomposition with shared factor matrices (applied to MHA block).
- 4D_Tucker_laser: (our method for MHA) + (LASER for FFN).
- laser: Original LASER intervention.
- 3D_Tucker: Separately compresses $\mathbf{W}_Q$, $\mathbf{W}_K$, $\mathbf{W}_V$, and $\mathbf{W}_O$ (for ablation studies).

## Running Experiments
Below are example commands to reproduce the results from the paper. The following bash commands uses GPT-J model as an example.

### 4D_Tucker Mode
```bash
python3 src/TensorLLM_intervention_gptj_bbh_qa.py --mode 4D_Tucker --lnum 27 --qkvo_rank 304 --head_dim_rank 19 --stack_rank 2 --single_experiment --device cuda

python3 src/TensorLLM_intervention_gptj_bios_profession.py --mode 4D_Tucker --lnum 18 --qkvo_rank 208 --head_dim_rank 13 --stack_rank 1 --single_experiment --device cuda

python3 src/TensorLLM_intervention_gptj_fever.py --mode 4D_Tucker --lnum 11 --qkvo_rank 800 --head_dim_rank 50 --stack_rank 2 --single_experiment --device cuda

python3 src/TensorLLM_intervention_gptj_hotpot.py --mode 4D_Tucker --lnum 27 --qkvo_rank 64 --head_dim_rank 4 --stack_rank 2 --single_experiment --device cuda
```

### Parameters
- ```lnum```: Specifies the layer number.
- ```Ranks```: (qkvo_rank, head_dim_rank, stack_rank): Controls decomposition granularity.

### Acknowledgement
We gratefully acknowledge the use of code from the following projects: [LASER](https://github.com/pratyushasharma/laser)

### Citation
If you find our paper or code useful, we will greatly appreacite it if you could consider citing our paper:

```
@INPROCEEDINGS{11228585,
  author={Gu, Yuxuan and Zhou, Wuyang and Iacovides, Giorgos and Mandic, Danilo},
  booktitle={2025 International Joint Conference on Neural Networks (IJCNN)}, 
  title={TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs}, 
  year={2025},
  pages={1-8},
  keywords={Training;Large language models;Noise reduction;Neural networks;Transformer cores;Performance gain;Benchmark testing;Transformers;Cognition;Complexity theory;Large Language Models;Multi-head Attention;Tensorisation;Tucker Decomposition;Reasoning;Compression},
  doi={10.1109/IJCNN64981.2025.11228585}}
```

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