AReaL/_sources/eval.md

Evaluation

The evaluation code is located in the evaluation folder of the repository. Following the previous tutorial, trained checkpoints will be saved under /storage/ray/experiments/checkpoints/root/.

Setup Evaluation Environment

Start a new container to execute the evaluation script. Note: Evaluation requires updates to certain Python libraries, so avoid using the training container for this task.

docker run -d --name areal-eval --privileged --gpus all --network host --shm-size 700g -v /storage:/storage ghcr.io/inclusionai/areal-runtime:v0.3.0 /bin/bash -c "tail -f /dev/null"
docker exec -it areal-eval bash

Install Dependencies and Run Evaluation

Execute the following commands inside the Docker container:

cd /storage/codes/AReaL/evaluation
cd latex2sympy
pip install -e .
cd ..
pip install -r requirements.txt 
pip install vllm==0.8.5 --no-build-isolation
pip install transformers==4.51.1
pip install prettytable timeout_decorator
mkdir -p /storage/ray/eval_output/
nohup python eval_and_aggregate.py \
    --model_path /storage/ray/experiments/checkpoints/root/my-exp/my-trial/epoch1epochstep20globalstep20/ \
    --output_path /storage/ray/eval_output/ \
    --data_names "math_500,aime24,amc23" \
    --max_gen_tokens 32768 &> /storage/ray/eval_output/eval_and_aggregate_parallel.log &

Command Line Parameters

• --model_path: Path to the saved model parameters
• --output_path: Path to store generated answers and log files during evaluation
• --data_names: Dataset(s) to evaluate. Multiple datasets can be separated by commas. Available options: math_500, math, gsm8k, train_amc_aime, aime24, amc23
• --max_gen_tokens: Maximum length of generated answers (default: 32768)

Evaluation Results

The evaluation script will output a results table in the terminal:

+----------+---------------+---------------+---------------+------------+---------------+--------+---------+
| dataset  | num_questions | greedy_length | sample_length | greedy_acc | sample_pass@1 | pass@8 | pass@16 |
+----------+---------------+---------------+---------------+------------+---------------+--------+---------+
| math_500 |      500      |     6757.4    |     4139.5    |    84.4    |      92.7     |  97.3  |   97.7  |
|  aime24  |       30      |    19328.0    |    13663.5    |    50.0    |      50.4     |  77.3  |   80.0  |
|  amc23   |       40      |     8850.0    |     6526.2    |    80.0    |      90.5     |  96.8  |   98.8  |
+----------+---------------+---------------+---------------+------------+---------------+--------+---------+

Metrics Explanation

• {greedy|sample}_length: Average answer length under greedy or random sampling strategy
• greedy_acc: Average accuracy under greedy sampling
• sample_pass@{k}: Probability of generating a correct answer within k attempts under random sampling

Configuration Details

Sampling Parameters

• The evaluation script defaults to averaging 32 samples with temperature 0.6
• We observed that the enforce_eager parameter in vLLM significantly impacts evaluation performance
• When enforce_eager=True, we can reproduce the model performance reported in previous work
• Without this setting, evaluation results may fall below reported performance
• Therefore, we enforce enforce_eager=True during evaluation

Runtime Expectations

Due to the sampling requirements and enforce_eager setting, the evaluation process typically takes considerable time.

Runtime depends on several factors:

• Maximum generation length
• Number of questions in the dataset
• Model size

Performance benchmarks (on 8x H100 GPUs):

• AIME dataset: ~80 minutes
• MATH_500 dataset: ~160 minutes