Summary of Changes

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This pull request refactors the core Trainer class to centralize control over key training lifecycle events. It introduces asynchronous operations for train_step and sync_weight to enhance performance and responsiveness, while also standardizing metric collection. Furthermore, checkpoint saving logic is consolidated within the Trainer, removing previous redundancies and preparing for more robust and configurable checkpointing.

Highlights

• Centralized Checkpoint Management: The save_checkpoint operation has been extracted from the trainer engine and is now managed directly by the Trainer class, allowing for more unified control over checkpointing frequency and logic.
• Asynchronous Training Operations: The train_step, sync_weight, and need_sync methods within the Trainer have been converted to asynchronous functions, enabling non-blocking execution and improved concurrency.
• Enhanced Metric Recording: Metric collection has been optimized across the training process, with Timer usage updated to use more descriptive keys (e.g., "time/read_experience") and train_step and sync_weight now returning metrics for consolidated logging.
• Streamlined verl_trainer Checkpointing: The verl_trainer's internal checkpointing logic has been simplified by removing redundant save calls from train_step and eliminating the direct saving of dataloader state, delegating these responsibilities to the main Trainer class.

Code Review

This pull request effectively refactors the Trainer class by making train_step and sync_weight asynchronous, centralizing checkpoint saving logic, and improving metric collection. The main training loop in train is now much cleaner and easier to follow. The changes to use await instead of ray.get are correctly implemented. I've found one issue with a redundant call which I've commented on. Overall, this is a great improvement to the codebase.

Pull Request Overview

This PR normalizes the Trainer implementation by extracting checkpoint saving operations from the trainer engine, making certain operations async, and optimizing metrics recording.

• Extract save_checkpoint operation from trainer engine to the main Trainer class
• Convert train_step and sync_weight methods to async and update timing metrics
• Add save interval control and optimize metrics collection

Reviewed Changes

Copilot reviewed 7 out of 7 changed files in this pull request and generated 1 comment.

    def _save_checkpoint(self, save_as_hf: bool = False):
        # path: given_path + `/global_step_{global_steps}` + `/actor`
        local_global_step_folder = os.path.join(
            self.config.trainer.default_local_dir, f"global_step_{self.global_steps}"
        )

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