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• [2024/8] We officially released ChatLearn! Check out our documentation.

ChatLearn is a flexible and efficient training framework for large-scale alignment.

Chatlearn has the following advantages:

1. User-friendly programming interface: Users can focus on programming individual models by wrapping a few functions, while the system takes care of resource scheduling, data and control flow transmission, and distributed execution.
2. Highly Scalable Training Methodology: ChatLearn offers alignment training such as RLHF, DPO, OnlineDPO and GRPO, while also supporting user-defined execution flows for models, enabling a highly convenient and customizable training process.
3. Diverse Distributed Acceleration Engines: Users can leverage various computational backends for model construction, such as Megatron-LM, DeepSpeed, vLLM, and others. For instance, we can use Megatron-LM for training and vLLM to expedite inference.
4. Flexible Parallel Strategies and Resource Allocation: ChatLearn supports different parallel strategies for various model configurations, enabling the formulation of distinct parallel approaches tailored to each model's computational, memory, and communication characteristics. Additionally, ChatLearn features a flexible resource scheduling mechanism that accommodates exclusive or shared use of resources across models. Through its system scheduling policies, it facilitates efficient serial/parallel execution and optimized GPU memory sharing, enhancing overall performance and efficiency.
5. High performance: Compared to current state-of-the-art (SOTA) systems, ChatLearn achieves a 52% performance improvement at the 7B+7B(Policy+Reward) scale and a 137% improvement at the 70B+70B scale. Meanwhile, ChatLearn supports larger-scale alignment training, such as 300B+300B.

By providing a comprehensive and efficient framework, ChatLearn empowers researchers and practitioners to train large-scale alignment models with ease, scalability, and improved performance.

Please refer to the documentation for a quick start.

1. Environment and Code Setup
2. End-to-End Training Tutorial with LLaMA/LLaMA2 Model

We compared the RLHF training throughput of models with different parameter scales, adopting an N+N model configuration where both the Policy model and the Reward model have the same number of parameters. We benchmarked against DeepSpeed-Chat and OpenRLHF with 7B and 70B model configurations. For the 8 GPU setup with a 7B+7B scale, we achieved a 115% speedup; for the 32 GPU setup with a 70B+70B scale, the speedup was 208%. The larger the scale, the more pronounced the acceleration effect becomes. Additionally, ChatLearn can support even larger-scale alignment training, such as at a 300B+300B scale.

Note: The performance of DeepSpeed-Chat and OpenRLHF has already been optimized.

The upcoming features for ChatLearn include:

• Support models with Megatron-Core format
• Support the alignment training for MoE (Mixture of Experts) models
• Integration with DeepSpeed as a training backend
• Support for more models
• Performance Optimization
• Support for more alignment algorithms

We welcome community partners to collaborate and contribute to the development.