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OpenRLHF logo

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Open-source / Comprehensive / Lightweight / Easy-to-use

[ English | 中文 ]

OpenRLHF is a high-performance RLHF framework built on Ray, DeepSpeed and HF Transformers:

  • Simple and easy to use: OpenRLHF is one of the simplest high-performance RLHF libraries currently available, and compatible with Huggingface models and datasets.
  • High performance: RLHF training spends 80% of the time on the sample generation stage. Thanks to the ability to use a large inference batch size with Ray and Adam Offload (Pinned Memory), the performance of OpenRLHF with the 13B LLaMA2 model is 4x that of DeepSpeedChat. We also support vLLM generation acceleration to further improve the generation performance.
  • Distributed RLHF: OpenRLHF distribute the Actor, Reward, Reference, and Critic models onto separate GPUs using Ray, while placing the Adam optimizer on the CPU. This enables full-scale fine-tuning of 70B+ models with multiple A100 80G GPUs and vLLM (see architecture) and 7B models across multiple 24GB RTX 4090 GPUs.
  • PPO Implementation Tricks: We integrated the implementation tricks for PPO to improve the training stability, referencing https://arxiv.org/abs/2005.12729 and https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/.

Features

TODO

  • Allows saving and loading training checkpoints.
  • Support Hybrid vLLM inference engine.

PPO Support Matrix

Feature OpenRLHF DSChat CAIChat TRL NeMo-Aligner
70B+ Full Tuning with 16 A100 ✅ ❌ ❌ ❌ ✅ (32+ A100s)
7B Full Tuning with 4 RTX4090 ✅ ❌ ❌ ❌ ❌
34B DPO Full Tuning with 8 A100 ✅ ❌ ❌ ❌ ❌
PPO Implementation Tricks ✅ ❌ ❌ ✅ ✅
Support QLoRA ✅ ❌ ❌ ✅ ❌
Support Mixtral 8*7b ✅ ❌ ❌ ❌ ❌
Support Unmerged Actor-Critic ✅ ✅ ✅ ❌ ✅
Support Multiple Reward Models ✅ ❌ ❌ ❌ ❌
Support Huggingface Models ✅ ✅ ✅ ✅ ❌ (need to convert)
Easy-to-use ✅ ✅ ✅ ✅ ❌

Performance

Common Configuration

  • Ray: 4 A100 80G for Actor, 2 A100 80G for Critic, 1 A100 80G for RM, and 1 A100 80G for InitPolicy
  • DeepSpeed: ZeRO2 with Adam Offload
  • Max Sequence Length: 2048

Throughput

Model Micro Batch Size (rollout/train) Throughput Generation Length
7B llama2 16/8 0.136 samples/gpu/sec 100-300
13B llama2 8/4 0.05 samples/gpu/sec 200-400
34B codellama 2/1 0.009 samples/gpu/sec 300-800

samples/gpu/secs = Number of PPO Samples / Number of A100 GPUs / Seconds

OpenRLHF vs DSChat

7B llama2 PPO 13B llama2 PPO (50k samples)
OpenRLHF - 17 hours with 8 A100
DeepSpeedChat - 48 hours with 16 A100

Running Example

Important

You can build openrlhf from nvidia-docker(recommended) or from conda envs.

# Clone the repository: 
git clone https://github.com/openllmai/OpenRLHF.git

install nvidia-docker and OpenRLHF

cd examples/scripts

# install nvidia-docker (Optional)
./nvidia_docker_install.sh

# build nvidia container with vLLM (Recommended)
./docker_run.sh build

# run nvidia container
./docker_run.sh

# cd in nvidia container
cd /openrlhf/examples/scripts

# build OpenRLHF (i.e, pip install)
./build_openrlhf.sh

# huggingface login 
huggingface-cli login

# wandb login (Optional, also set --wandb True in script)
wandb.login()

Single-node training

# Supervised Finetuning
./train_sft_llama.sh

# Reward Model Tuning
./train_rm_llama.sh

# PPO Training
./train_ppo_llama.sh

# DPO
./train_dpo_llama.sh

# KTO
./train_kto_llama.sh

# Rejection Sampling with vLLM
./train_rejection_sampling_llama.sh

# Conditional SFT
./train_conditional_llama.sh

# Continue Pre-training
./train_continue_pretrain_llama.sh

PPO training with Ray

Tip

for >= 13B models on V100/A100/H100.. or 7B models on RTX4090

# launch the master node of ray in container
ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
# if you want to launch ray on more nodes, use
ray start --address {MASTER-NODE-ADDRESS}:6379  --num-gpus 8

# Ray PPO training, requires 8 GPUs in default config
./train_ppo_llama_ray.sh

# for 70B models
# Launch Ray PPO with vLLM, requires 16 A100s in default config
./train_ppo_llama_ray_70b.sh

Multi-nodes training on Slurm

cd examples/scripts

# Moidfy the Slurm Account/Nodes ... in `train_llama_slurm.sh`

# For SFT, RM, PPO, DPO, KTO training:
# Modify the variable `training_script` in `train_llama_slurm.sh` to
readonly training_script="train_sft_llama.sh"
readonly training_script="train_rm_llama.sh"
readonly training_script="train_ppo_llama.sh"
readonly training_script="train_dpo_llama.sh"
readonly training_script="train_kto_llama.sh"

# set `GPUS_PER_NODE` in `train_llama_slurm.sh`
readonly GPUS_PER_NODE=8

# run multi-nodes training script
# train_llama_slurm.sh will load the training args from `training_script`
sbatch ./train_llama_slurm.sh

# for Ray PPO training with Slurm
sbatch ./train_ppo_llama_ray_slurm.sh

Inference and Evaluation

After completing the training, you can evaluate your model by using the inference script:

# batch generate
# support vLLM acceleration (--eval_task generate_vllm)
python examples/batch_inference.py {args}

# interactive_chat
./interactive_chat_llama.sh { pretrain_model_path }

build openrlhf from conda envs

If you really don't want to use nvidia-docker, we also provide tutorials for building openrlhf from a conda environment. (We prefer nvidia-docker to avoid errors caused by the environment.)

# we need conda
conda create -n openrlhf python=3.10
# so, we need install some package manually: when installing torch, you may need to match the corresponding cuda version.
pip install packaging ninja
pip3 install torch
# check ninjia
ninja --version
echo $? # output: 0
# install flash-attn: may take some time.
# For network error: you can download specified version from https://github.com/Dao-AILab/flash-attention/releases.
pip install flash-attn==2.4.2
./build_openrlhf.sh
# enjoy it!

Join Us

How to Join?

  1. Email us at [email protected](official email) or [email protected]/[email protected](PIC). Please include the following details:
    • Your name
    • Your GitHub username
    • Your areas of interest
    • Your skills and experience related to NLP and/or AI
  2. You can also join us through the official GitHub OpenRLHF ↗ project page. Just create an issue about your interest to contribute and we will get back to you.

What can you do?

  1. Join the team and participate in the development of the OpenRLHF project.
  2. Contribute to the project by submitting pull requests.
  3. Help improve documentation, fix bugs, or create new features.
  4. Share the project and help us grow the community.

Sponsor Us

Your sponsorship can help us maintain and improve OpenRLHF. If you find this project useful, please consider sponsoring us. You can sponsor us on Open Collective ↗.

Starchart

Star History Chart

Contributors

A big thank you to all our contributors! If you want to contribute, feel free to make a pull request or create an issue.

References & Acknowledgements

We would like to express our gratitude to the following projects and organizations for their contributions to the field of AI and NLP:

Our project would also like to thank ColossalChat and DeepSpeedChat. In the early stages of the project, we referred to their code design.

Citation

@misc{hu23openrlhf,
   author = {Jian Hu and Xibin Wu and Xianyu and Chen Su and Leon Qiu and Daoning Jiang and Qing Wang and Weixun Wang},
   title = {OpenRLHF: A Ray-based High-performance RLHF framework},
   year={2023},
   publisher = {GitHub},
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/OpenLLMAI/OpenRLHF}}
}

OpenRLHF © 2024 OpenLLMAI. All Rights Reserved.