[fsdp] feat: add validate process on trainer node when use_trainer_do_validate=True

[chenjiaoAngel]

What does this PR do?

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User Trainer node to do validate process when run mode on fully-async, It can save time for validate computing and reduce perf/time_of_step peak

• add new use_trainer_do_validate on fully_async async_training config to decide whether using trainer node to do validate process
• use_trainer_do_validate: default is false
• It can improve performance of validate, such as in dapo_7b_math_fsdp2_8_8.sh, it can improve about 1X speed

• optimized the process_validation_metrics() on _validate() process, when input datasets len=1444, it latency reduce from 150+s to 40+s

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Test

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• on test scripts such as dapo_7b_math_fsdp2_8_8.sh add async_training.use_trainer_do_validate=True command to do compute
• the result of this function on Qwen2.5-Math-7B model
• the baseline scripts is dapo_7b_math_fsdp2_8_8.sh
• the optimized scripts is dapo_7b_math_fsdp2_8_8.sh +async_training.use_trainer_do_validate=True
• the acc and perfomance is below:

• green: optimized case (async_training.use_trainer_do_validate=True )
• gray: baseline case (async_training.use_trainer_do_validate=False )

API and Usage Example

Demonstrate how the API changes if any, and provide usage example(s) if possible.

# Add code snippet or script demonstrating how to use this
async_training.use_trainer_do_validate=True \

Design & Code Changes

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[gemini-code-assist]

Code Review

This pull request introduces the functionality to perform validation on the trainer node, controlled by the use_trainer_do_validate flag. The changes span across configuration, worker logic, data handling, and synchronization. Key modifications include adding the use_trainer_do_validate flag, conditionally assigning the ActorRollout role to the trainer, splitting the validation dataset, and updating the trainer to merge validation metrics. The overall approach is sound, but I've identified a few critical issues related to robustness and potential runtime errors that should be addressed. Specifically, there are concerns with how unique actor names are generated, how validation results are merged, and how datasets are split. These are detailed in the comments.

[wuxibin89]

Please format code according to: https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md

[chenjiaoAngel]

Please format code according to: https://github.com/volcengine/verl/blob/main/CONTRIBUTING.md

已完成～

[wuxibin89]

Why we need split dataset?

[chenjiaoAngel]

Why we need split dataset?

We need to split dataset to rollout and trainer node according to ratio of trainer:rollout

[wuxibin89]

Is it better to split validate files directly instead of split RLDataset?

[chenjiaoAngel]

Is it better to split validate files directly instead of split RLDataset?

split validate file this way may be study, i know the easy way to split datasets; I will check this way whether it easy to work

[chenjiaoAngel]

If we split the data directly from the file, we need to save two temporary files and then use rl_dataset to get the data class; this seems to involve redundant read/write operations. If we split the data directly from the dataset object, my understanding is that we only need one round of I/O read/write, and there is no need to save intermediate files.

[wuxibin89]

/gemini review

[gemini-code-assist]

Code Review

This pull request introduces a valuable feature to perform validation on the trainer node, which can significantly improve performance by overlapping validation with training. The implementation is well-structured, touching upon configuration, documentation, worker logic, and metric collection to support this new validation strategy. Additionally, the performance optimization in process_validation_metrics is a great improvement. I've identified one critical issue in the dataset splitting logic that could prevent this feature from being used in many common scenarios.