Summary of Changes

Hello @chenyushuo, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces initial support for integrating Megatron-LM into the training framework, specifically for Proximal Policy Optimization (PPO). It enables the use of Megatron's distributed training features, such as tensor and pipeline parallelism, for scaling large language models. This is a work-in-progress (WIP) and lays the groundwork for leveraging Megatron's capabilities within the system by adding new configurations, a dedicated PPO actor, a checkpoint manager, and worker implementations that leverage Megatron's distributed training capabilities.

Highlights

• Enhanced Megatron Configuration: New configuration classes (MegatronConfig, OverrideTransformerConfig, ProfileConfig) have been added to verl_config.py to define Megatron-specific parameters for distributed training, model overrides, and profiling. These configurations are now integrated into the Actor, Ref, and CriticModel settings, providing granular control over Megatron's behavior.
• Megatron-Specific PPO Actor: A dedicated MegatronPPOActor has been introduced to handle PPO training logic specifically tailored for Megatron's distributed environment. This includes advanced features like micro-batching, dynamic batch sizing, and efficient log probability/entropy computation using Megatron's parallel utilities.
• Distributed Checkpointing for Megatron: The new MegatronCheckpointManager facilitates robust distributed checkpointing for Megatron models, optimizers, and RNG states. It supports both Megatron's native distributed checkpointing mechanism and compatibility with HuggingFace model formats, ensuring flexible and reliable state persistence.
• Integrated Megatron Workers: The framework now includes ActorRolloutRefWorker and CriticWorker as specialized Ray workers. These workers are responsible for initializing and managing Megatron models and optimizers, enabling seamless distributed PPO training within the Ray ecosystem and leveraging Megatron's parallelism capabilities.
• Trainer Orchestration for Megatron: The verl_trainer.py has been updated to dynamically select the NVMegatronRayWorkerGroup when the 'megatron' strategy is chosen. This change orchestrates the new Megatron-compatible workers, providing a clear entry point for utilizing Megatron's distributed training features.
• Extended Optimizer and Checkpoint Configurations: The Optim and Checkpoint configurations have been extended with additional parameters, offering more fine-grained control over optimization schedules, gradient clipping, and enabling asynchronous checkpoint saving for improved performance and flexibility.

Code Review

This pull request introduces support for Megatron, a framework for training large transformer models. The changes include new configurations for Megatron-specific parameters, new worker implementations (ActorRolloutRefWorker, CriticWorker) tailored for Megatron's distributed training paradigm, and a dedicated checkpoint manager (MegatronCheckpointManager). The trainer logic in verl_trainer.py is updated to integrate this new strategy. My review focuses on ensuring code quality, maintainability, and consistency with existing practices, particularly regarding logging and potential side effects of implementation choices.