InternBootcamp/examples/verl_usage

VERL USAGE

This directory contains examples and data processing scripts for training with bootcamp datasets in the VERL framework. Here is a brief overview of the file and its functions:

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Directory structure

/InternBootcamp/examples/verl_usage
├── run_bootcamp.sh
├── bootcamp_reward_for_verl.py
└── verl_data_preprocess.py

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1. run_bootcamp.sh

This is a Shell script for setting up and running VERL training experiments. The main functions include:

• Set basic parameters such as experiment name and data path.
• Install the necessary dependencies and configure the environment.
• Start the VERL trainer and configure the experiment parameters (such as model path, batch size, learning rate, etc.).

How to use

• Before starting, ensure that important parameters are correctly configured, such as internbootcamp_path, model.path, trainer.default_local_dir, etc

./run_bootcamp.sh

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2. bootcamp_reward_for_verl.py

This Python script is used to dynamically calculate the reward score of the bootcamp during training. Need to be embedded in the verl framework for use.

The script provides a function _default_compute_score to calculate a score based on 'data_source', 'model_output', and 'groud_truth' (here, a dictionary of parameters needed to validate model_output). Support for multiple data sources, including:

• Non-bootcamped data sets : Scores are calculated using modules such as gsm8k, math, prime_math, prime_code, etc.
• bootcamp dataset : Dynamically import external bootcamp modules to calculate scores.
• Raises NotImplementedError if the data source is not supported.

3. verl_data_preprocess.py

This Python script is used to convert raw data into a VerL-compatible format. The main functions include:

• Recursively process input directories or files : Converts the '.jsonl 'raw data file to the'.parquet 'format and preserves the directory structure.
• Data partition : According to the file path, the data belongs to the 'train' or 'test' partition.
• Random shuffle and merge : Combine multiple '.parquet 'files into one file and randomly shuffle the data.
• Metadata Add : Add 'split' and other necessary information for each piece of data.

Directory Structure of Outputs

examples/bootcamp_generator_outputs/<time_stamp>_for_verl_merged/
├── train/
│   └── bootcamps.parquet
└── test/
    └── bootcamps.parquet

examples/bootcamp_generator_outputs/<time_stamp>_for_verl/
├── train/
│   ├── bootcamp1.parquet
│   ├── bootcamp2.parquet
│   └── ...
└── test/
    ├── bootcamp1.parquet
    ├── bootcamp2.parquet
    └── ...    

Example Command

python examples/verl_usage/verl_preprocess.py --src examples/bootcamp_generator_outputs/2025-03-07-16:48:28

This command converts all.jsonl files in examples/bootcamp_generator_outputs/2025-03-07-16:48:28 to VerL-compatible.parquet files and outputs to the default directory:

examples/bootcamp_generator_outputs/2025-03-07-16:48:28_for_verl_merged/
examples/bootcamp_generator_outputs/2025-03-07-16:48:28_for_verl/

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4. Sumarizing the Workflow

1. Data Preprocessing :

• Convert raw.jsonL data to VerL-compatible.parquet format using verl_data_preprocess.py.
• merged output directory is' _for_verl_merged ', containing 'train/bootcamps.parquet' and 'test/bootcamps.parquet' files.

merged output directory _for_verl ', containing multiple.parquet files, each corresponding to a.jsonl file.

2. Embed bootcamp_reward_for_verl.py into the verl framework

This is the following code snippet

    elif data_source.startswith("bootcamp/"):
        try:
            import importlib
            import json
            bootcamp_name = data_source.split('/')[1]
            class_name = bootcamp_name[0].upper() + bootcamp_name[1:] + "bootcamp"
            module = importlib.import_module(f"internbootcamp")
            ground_truth = json.loads(ground_truth)
            return getattr(module, class_name).verify_score(solution_str, ground_truth, format_score=0)
        except Exception as e:
            print("Something woring with bootcamp reward because of ",e)
            return 0

Embed the relevant code in the VeRL framework as' /verl/utils/reward_score/init.py '

/verl
└── utils
    └── reward_score
        └── __init__.py

3. Start the training experiment :

• Complete the Settings in 'run_bootcamp.sh' experiment_name,internbootcamp_path,train_files,test_files,actor_rollout_ref.model.path,`trainer.default_local _dir 'and other important experimental parameters.

• Run 'run_bootcamp.sh' to start VERL training.

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With the above tools and processes, you can efficiently prepare data and run VERL experiments.