EvalPlus(📖) => 📚

🔥Quick Start • 💻LLM code • 🔨Tools • 📜Citation • 🙏Acknowledgement

About

EvalPlus is a rigorous evaluation framework for LLM4Code, with:

• ✨ HumanEval+: 80x more tests than the original HumanEval!
• ✨ MBPP+: 35x more tests than the original MBPP!
• ✨ Evaluation framework: our packages/images/tools can easily and safely evaluate LLMs on above benchmarks.

Why EvalPlus? What does using EvalPlus datasets bring to you?

• ✨ Reliable ranking: See our leaderboard for the latest LLM ranking before and after rigorous evaluation.
• ✨ Coding rigorousness: Look at the score differences! esp. before and after using EvalPlus tests! Less drop is better as it means more rigorousness and less laxity in code generation; while a big drop means the generated code tends to be fragile.
• ✨Pre-generated samples: EvalPlus accelerates LLM4Code research by open-sourcing LLM-generated samples for vairous models -- no need to re-run the expensive benchmarks!

Want to know more details? Read our NeurIPS'23 paper as well as our Google Slides!

🔥 Quick Start

To get started, please first setup the environment:

pip install evalplus --upgrade

pip install "git+https://github.com/evalplus/evalplus.git" --upgrade

git clone https://github.com/evalplus/evalplus.git
cd evalplus
export PYTHONPATH=$PYTHONPATH:$(pwd)
pip install -r requirements.txt

Code generation

Implement the GEN_SOLUTION function by calling the LLM to produce the complete solution (include the code) and save the samples to samples.jsonl:

from evalplus.data import get_[human_eval|mbpp]_plus, write_jsonl

samples = [
    dict(task_id=task_id, solution=GEN_SOLUTION(problem["prompt"]))
    for task_id, problem in get_[human_eval|mbpp]_plus().items()
]
write_jsonl("samples.jsonl", samples)

Code evaluation

You are strongly recommended to use a sandbox such as docker:

docker run -v $(pwd):/app ganler/evalplus:latest --dataset [humaneval|mbpp] --samples samples.jsonl

...Or if you want to try it locally regardless of the risks ⚠️:

evalplus.evaluate --dataset [humaneval|mbpp] --samples samples.jsonl

export PYTHONPATH=$PYTHONPATH:$(pwd)
python evalplus/evaluate.py --dataset humaneval --samples samples.jsonl

The output should be like (below is GPT-4 greedy decoding example):

Computing expected output...
Expected outputs computed in 15.18s
Reading samples...
164it [00:04, 37.79it/s]
Evaluating samples...
100%|██████████████████████████████████████████| 164/164 [00:03<00:00, 44.75it/s]
Base
{'pass@1': 0.8841463414634146}
Base + Extra
{'pass@1': 0.768}

• Base is the pass@k for the original HumanEval
• Base + Extra is the pass@k for the our HumanEval+ (with extra tests)
• The "k" includes [1, 10, 100] where k values <= the sample size will be used
• A cache file named like samples_eval_results.jsonl will be cached. Remove it to re-run the evaluation

💻 LLM-generated code

We also share pre-generated code samples from LLMs we have evaluated:

• HumanEval+: See the attachment of our v0.1.0 release.
• MBPP+: See the attachment of our v0.2.0 release.

Each sample file is packaged in a zip file named like ${model_name}_temp_${temperature}.zip. You can unzip them to a folder named like ${model_name}_temp_${temperature} and run the evaluation from scratch with:

evalplus.evaluate --dataset humaneval --samples ${model_name}_temp_${temperature}

🔨 Useful tools

To use these tools, please first install the repository from GitHub:

git clone https://github.com/evalplus/evalplus.git
cd evalplus
pip install -r requirements-tools.txt

Syntax checker for LLM-generated code

Check LLM-produced code and answer the following questions:

1. Is the generation entirely done for all samples / all problems in the dataset?
2. Are LLM-generated code compilable? (if no, something could be wrong and you'd better check)

# Set PYTHONPATH to run local Python files
export PYTHONPATH=$PYTHONPATH:$(pwd)

python tools/checker.py --samples samples.jsonl --dataset [humaneval|mbpp]
# --samples can also be a directory organized as: ${SAMPLE_DIR}/${TASK_ID}/{SAMPLE_ID}.py

Post code sanitizer

LLM-generated code may contain some syntax errors. But some of them can be easily fixable by doing simple post-processing. This tool will make the LLM-generated code more clean/compilable by doing certain post-processing such as trimming with more magical EOFs and some garbage non-code tokens.

# Set PYTHONPATH to run local Python files
export PYTHONPATH=$PYTHONPATH:$(pwd)

# 💡 If you are storing codes in jsonl:
python tools/sanitize.py --samples samples.jsonl --dataset [humaneval|mbpp]
# Sanitized code will be produced to `samples-sanitized.jsonl`

# 💡 If you are storing codes in directories:
python tools/sanitize.py --samples /path/to/vicuna-[??]b_temp_[??] --dataset [humaneval|mbpp]
# Sanitized code will be produced to `/path/to/vicuna-[??]b_temp_[??]-sanitized`

You should now further check the validity of sanitized code with tools/checker.py. Sometimes (e.g., Chat models) there might be some natural language lines that impact the compilation. You might use --rm-prefix-lines to cut those NL lines with a prefix (e.g., --rm-prefix-lines "Here's").

Render pass@k results to rich and LaTeX tables

python tools/render.py --type /path/to/[model]-[??]b # NOTE: no `_temp_[??]`

Perform test input generation from scratch (TBD)

Name convention

• evalplus is the package name.
• ${DATASET}_plus is the name of dataset applied with evalplus.

📜 Citation

@inproceedings{evalplus,
  title = {Is Your Code Generated by Chat{GPT} Really Correct? Rigorous Evaluation of Large Language Models for Code Generation},
  author = {Liu, Jiawei and Xia, Chunqiu Steven and Wang, Yuyao and Zhang, Lingming},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
  year = {2023},
  url = {https://openreview.net/forum?id=1qvx610Cu7},
}

🙏 Acknowledgement

• HumanEval
• MBPP