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clinicalml / co-llm

Co-LLM: Learning to Decode Collaboratively with Multiple Language Models
https://arxiv.org/abs/2403.03870
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Co-LLM: Learning to Decode Collaboratively with Multiple Language Models

🎉 Check our paper!

Overview

An explanation of the Co-LLM Method

Demo

Usage

Use the following code to create the environment:

conda create -n co-llm python=3.11 -y
conda activate co-llm
conda install \
    pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 \
    pytorch-cuda=12.1 -c pytorch -c nvidia -y

pip install -r requirements.txt

Overview

The diagram below shows all the steps needed to reproduce our results. Please check the bash scripts in the scripts folder for a quick start for running the code. For detailed instructions, please refer to the following sections:

Data Processing | Training | Inference

%%{init: {'theme': 'neutral'}}%%
flowchart LR
    style Data_Processing fill:#fff,stroke:#333,stroke-width:2px,stroke-dasharray: 5, 5
    style Training fill:#fff,stroke:#333,stroke-width:2px,stroke-dasharray: 5, 5
    style Inference fill:#fff,stroke:#333,stroke-width:2px,stroke-dasharray: 5, 5

    subgraph Data_Processing ["Data Processing"]
        formatting_tokenization[formatting/tokenization]:::rounded
        score_logits[score logits]:::rounded
        formatting_tokenization --> score_logits
    end

    subgraph Training ["Training"]
        initializing_Z[initializing Z]:::rounded
        deferral_training[deferral training]:::rounded
        initializing_Z --> deferral_training
    end

    subgraph Inference ["Inference"]
        formatting_data_with_prompts[formatting data with prompts]:::rounded
        start_vllm_server_generate[start the vllm server + generate]:::rounded
        formatting_data_with_prompts --> start_vllm_server_generate
    end

    Data_Processing --> Training --> Inference

    classDef rounded fill:#fff,stroke:#333,stroke-width:2px,rounded

Data Processing

For example, for gsm8k: 

## Step1: Format the dataset 
python scripts/train/gsm8k/create_train_data.py

## Step2: Create the training data for deferral training: 
bash scripts/train/gsm8k/process_dataset.sh

## Step3: Create the initialization dataset: 
bash scripts/train/gsm8k/create_init_dataset.sh

It will process the raw dataset (reformatting with the proper templates) and save it to data/processed/gsm8k/gsm8k_data.jsonl; then it will use the corresponding models to score the dataset and save it in checkpoints/dataset/gsm8k-completion/<model-name>. Finally it will generate the initialization dataset in checkpoints/dataset/gsm8k-completion/init-a-Llama-2-7b-hf+EleutherAI@llemma_7b.

Training

bash scripts/train/gsm8k/deferral_finetune_with_hf_trainer.sh

It firstly use the weak supervision labels to initialize the $Z$ vector, then uses the deferral training to fine-tune the model. The checkpoints will be saved in checkpoints/deferral_finetuned/gsm8k-completion_Llama-2-70b-hf/Llama-2-7b-hf~128bz -- indicating it is training a Llama-2-7b model to collaborate with Llama-2-70b-hf as the assistant model.

We assume you have a folder called weights/ with all the models downloaded, for example: 

weights
├── EleutherAI@llemma_34b
├── EleutherAI@llemma_7b
├── epfl-llm@meditron-70b
├── epfl-llm@meditron-7b
├── Llama-2-13b-hf
├── Llama-2-70b-hf
└── Llama-2-7b-hf

If you want to train models with other collaboration pairs, you can adjust the following arguments in the bash script: 

DATASET_NAME="gsm8k-completion/Llama-2-70b-hf"                               # <--- The static dataset for the assistant model 
DEFERRAL_INIT_DATASET="gsm8k-completion/init-a-Llama-2-7b-hf+Llama-2-70b-hf" # <--- The initialization dataset 
    # You might need to modify scripts/train/gsm8k/create_init_dataset.sh to create the initialization data 
MODEL_NAME="Llama-2-7b-hf"                                                   # <--- The base model being trained

Baseline training code

We also provide the scripts training other baseline models:

Baseline Name Script
Default supervised fine-tuning bash scripts/train/gsm8k/default_finetune_with_hf_trainer.sh
QLoRA training bash scripts/train/gsm8k/finetune_qlora_with_accelerate.sh
Weak supervision baseline bash scripts/train/gsm8k/deferral_finetune_with_hf_trainer_weak-supervision.sh

Inference

Since we need to orchestrate multiple models during inference time, we use vllm to separate services for a model, and then we use these service in an API-like manner to generate the results.

There are three different vllm service: Service Script
collm/inference/api_server_simple.py It's the regular LLM API Server.
collm/inference/api_server_deferral.py It separates the last vocab position prediction (deferral token) from the rest when calculating the token logprobs; it returns the raw logits so one can then apply a sigmoid function to obtain the deferral probability.
collm/inference/api_server_logits.py Different from the regular server, it returns the un-normalized token logits from the model, which will be used in baseline models like Proxy Tunning or Contrastive Decoding. However it is a bit tricky to use this code as you might need to manually modify the installed vllm code to ensure it returns the logits. A update on vllm will make this much easier.

Here is an example of how to use the scripts for deferral generation: 

bash scripts/evaluation/gsm8k/deferral_generate.sh

It uses the scripts/evaluation/generic/deferral_generate.sh script with three steps:

  1. Run the deferral search to generate on a small validation set under different deferral frequency
  2. Evaluate the accuracy and pick the best deferral threshold
  3. Run Co-LLM generation with the optimal threshold.

We also need to run the forward.js script to start a javascript-based batch request forwarding server: node forward.js. It will be terribly slow to batch send async requests to the vllm server in Python (during the threshold searching stage); so we made this javascript based forwarding server to speed up this process. The dependency is very minimal -- you only need to install express and axios with npm install express axios.

Acknowledgement

Our code is extensively inspired by allenai/open-instruct; we reused many code from the their library in our codebase.

Citation

If you find our work helpful, please cite it as: 

@article{shen2024learning,
  title={Learning to Decode Collaboratively with Multiple Language Models},
  author={Shen, Shannon Zejiang and Lang, Hunter and Wang, Bailin and Kim, Yoon and Sontag, David},
  journal={arXiv preprint arXiv:2403.03870},
  year={2024}
}