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RoCoFT

This is the implementation for the paper "RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates".

[<a href="https://arxiv.org/">Paper</a>] [<a href="https://github.com/Kowsher/RoCoFT">Code</a>]

🙋 Please let us know if you find out a mistake or have any suggestions!

🌟 If you find this resource helpful, please consider to star this repository and cite our research:

@article{kowsher2024rocoft,
  title={RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates},
  author={Kowsher, Md and Esmaeilbeig, Tara and Yu, Chun-Nam and Soltanalian, Mojtaba and Yousefi, Niloofar},
  journal={arXiv preprint arXiv:2410.10075},
  year={2024}
}

Introduction

RoCoFT is a parameter-efficient fine-tuning (PEFT) method designed to optimize task-specific performance while drastically reducing computational overhead by training selective rows and columns from pre-trained weight matrix

Requirements

Use python 3.11 from MiniConda

• torch==2.3.0
• accelerate==0.33.0
• einops==0.7.0
• matplotlib==3.7.0
• numpy==1.23.5
• pandas==1.5.3
• scikit_learn==1.2.2
• scipy==1.12.0
• tqdm==4.65.0
• peft==0.12.0
• transformers==4.44.0
• deepspeed==0.15.1
• sentencepiece==0.2.0

To install all dependencies:

pip install -r requirements.txt

Datasets

You can access the datasets from hugginface

Quick Demos

To get started with RoCoFT, follow these simple steps:

1. Import the necessary modules:

   import RoCoFT
   from transformers import RobertaForSequenceClassification
   

2. Load a pre-trained model and apply PEFT:

   model = RobertaForSequenceClassification.from_pretrained('model_name')
   RoCoFT.PEFT(model, method='row', rank=1) 
   

3. For column

   RoCoFT.PEFT(model, method='column', rank=1) 
   

4. In order to choose row or column using pruning technique

   import RoCoFT
   
   # Example text input
   input_text = tokenized_datasets['train']['sentence'][0:200]
   
   input_text = []
   for i in tqdm(range(len(tokenized_datasets['train']['premise'][0:200]))):
       st = tokenized_datasets['train']['premise'][i] + tokenized_datasets['train']['hypothesis'][i]
       input_text.append(st)
      
   input_ids = tokenizer(input_text, return_tensors='pt', padding=True, truncation=True)['input_ids']
   
   
   # Get the embedding of the input text
   with torch.no_grad():
       embeddings = model.roberta.embeddings(input_ids)
   
   RoCoFT.PEFT_prunig(model, method='column', rank=3, input_data=embeddings, descending=False)
   

If descending=True, pruning method return the least weights.

🚩 Note: In the examples or notebook, we have used leader , feel free to replace it with RoCoFT