den-run-ai / evalplus

EvalPlus for rigourous evaluation of LLM-synthesized code

EvalPlus(📖) => 📚

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

Important

📢 Who is the best LLM coder? Take a look at the EvalPlus leaderboard 🏆! 📢
🤗 Request for independent model evaluation is open!

About

Warning

🚨 Evaluating LLM-generated code over datasets with "3 test-cases" is **NOT** enough! 🚨

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

⏬ Install nightly version :: click to expand ::

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

⏬ Using EvalPlus as a local repo? :: click to expand ::

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)

🤔 Structure of `problem`? :: click to expand ::

• task_id is the identifier string for the task
• entry_point is name of the function
• prompt is the function signature with docstring

• canonical_solution is the ground-truth implementation (re-implemented to fix bugs in HumanEval)
• base_input is the test inputs in original HumanEval
• plus_input is the test inputs brought by EvalPlus

Note

Expected Schema of samples.jsonl

1. task_id: Task ID, which are the keys of get_[human_eval|mbpp]_plus()
2. solution (optional): Self-contained solution (usually including the prompt)
   • Example: {"task_id": "HumanEval/?", "solution": "def f():\n return 1"}
3. completion (optional): Function body without prompt
   • Example: {"task_id": "HumanEval/?", "completion": " return 1"}

Only one of solution and completion is required. If both are provided, solution will be used. We also accept solutions in the form of directory, i.e., --samples ${SAMPLE_DIR} where ${SAMPLE_DIR} is organized as: ${SAMPLE_DIR}/${TASK_ID}/{SAMPLE_ID}.py (${TASK_ID} = task_id.replace("/", "_")).

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

Warning

Do you use a very slow machine?

LLM solutions are regarded as failed on timeout (and OOM etc.). Specifically, we set the timeout $T=\max(T_{base}, T_{gt}\times k)$, where:

• $T_{base}$ is the minimal timeout (configurable by --min-time-limit; default to 1s);
• $T_{gt}$ is the runtime of the ground-truth solutions (achieved via profiling);
• $k$ is a configurable factor --gt-time-limit-factor (default to 4);

If your machine is too slow and you are getting high-variance results, try to use larger $k$ and $T_{base}$.

Additionally, you are NOT encouraged to make your test-bed over stressed while running evaluation. For example, using --parallel 64 on a 4-core machine or doing something else during evaluation are bad ideas...

🤔 Evaluate with local GitHub repo? :: click to expand ::

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

⌨️ More command-line flags :: click to expand ::

• --parallel: by default half of the cores
• --base-only (store_ture): only run base HumanEval tests
• --i-just-wanna-run: force a re-run

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

🤔 How long it would take? :: click to expand ::

If you do greedy decoding where there is only one sample for each task, the evaluation should take just a few seconds. When running 200 samples x 164 tasks x ~700+ tests, it can take around 2-10 minute by using --parallel 64 and --test-details. Here are some tips to speed up the evaluation:

• Use --parallel $(nproc)
• Do NOT use --test-details if you just want to quickly get pass@k as --test-details will run all tests (700+ on average for each task), while without --test-details the testing for a sample stops immediately when it fails the first test.
• Use our pre-evaluated results (see LLM-generated code)
• Use HumanEval+ Mini

Note

🚀 Try out HumanEvalPlus-Mini! which selects a minimal set of additional tests with the highest quality, achieving almost the same effectiveness of the full version. Just add a --mini flag, it can run 23+% faster! (even faster if you evaluate all tests without fail-stop with --test-details).

docker run -v $(pwd):/app ganler/evalplus:latest --dataset humaneval --samples samples.jsonl --mini
# ...Or locally ⚠️
# evalplus.evaluate --dataset humaneval --samples samples.jsonl --mini

💻 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