GitHub - Qubitium/evalplus: Rigourous evaluation of LLM-synthesized code

📙About • 🔥Quick Start • 🚀LLM Backends • 📚Documents • 📜Citation • 🙏Acknowledgement

📢 News

Who's using EvalPlus datasets? EvalPlus has been used by various LLM teams, including:

Below tracks the notable updates of EvalPlus:

[2024-10-20 v0.3.1]: EvalPlus v0.3.1 is officially released! Highlights: (i) Code efficiency evaluation via EvalPerf, (ii) one command to run all: generation + post-processing + evaluation, (iii) support for more inference backends such as Google Gemini & Anthropic, etc.
[2024-06-09 pre v0.3.0]: Improved ground-truth solutions for MBPP+ tasks (IDs: 459, 102, 559). Thanks to EvalArena.
[2024-04-17 pre v0.3.0]: MBPP+ is upgraded to v0.2.0 by removing some broken tasks (399 -> 378 tasks). ~4pp pass@1 improvement could be expected.

Earlier news :: click to expand ::

(v0.2.1) You can use EvalPlus datasets via bigcode-evaluation-harness! HumanEval+ oracle fixes (32).
(v0.2.0) MBPP+ is released! HumanEval contract & input fixes (0/3/9/148/114/1/2/99/28/32/35/160).
(v0.1.7) Leaderboard release; HumanEval+ contract and input fixes (32/166/126/6)
(v0.1.6) Configurable and by-default-conservative timeout settings; HumanEval+ contract & ground-truth fixes (129/148/75/53/0/3/9/140)
(v0.1.5) HumanEval+ mini is released for ultra-fast evaluation when you have too many samples!
(v0.1.1) Optimizing user experiences: evaluation speed, PyPI package, Docker, etc.
(v0.1.0) HumanEval+ is released!

📙 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!
✨ EvalPerf: evaluating the efficiency of LLM-generated code!
✨ Framework: our packages/images/tools can easily and safely evaluate LLMs on above benchmarks.

Why EvalPlus?

✨ Precise evaluation: See our leaderboard for latest LLM rankings before & after rigorous evaluation.
✨ Coding rigorousness: Look at the score differences! esp. before & after using EvalPlus tests! Less drop means more rigorousness in code generation; while a bigger drop means the generated code tends to be fragile.
✨ Code efficiency: Beyond correctness, our EvalPerf dataset evaluates the efficiency of LLM-generated code via performance-exercising coding tasks and test inputs.

Want to know more details? Read our papers & materials!

EvalPlus: NeurIPS'23 paper, Slides, Poster, Leaderboard
EvalPerf: COLM'24 paper, Poster, Documentation, Leaderboard

🔥 Quick Start

Code Correctness Evaluation: HumanEval(+) or MBPP(+)

pip install --upgrade "evalplus[vllm] @ git+https://github.com/evalplus/evalplus"
# Or `pip install "evalplus[vllm]" --upgrade` for the latest stable release

evalplus.evaluate --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                  --dataset [humaneval|mbpp]             \
                  --backend vllm                         \
                  --greedy

🛡️ Safe code execution within Docker :: click to expand ::

# Local generation
evalplus.codegen --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                 --dataset humaneval                    \
                 --backend vllm                         \
                 --greedy

# Code execution within Docker
docker run --rm --pull=always -v $(pwd)/evalplus_results:/app ganler/evalplus:latest \
           evalplus.evaluate --dataset humaneval                                     \
           --samples /app/humaneval/ise-uiuc--Magicoder-S-DS-6.7B_vllm_temp_0.0.jsonl

Code Efficiency Evaluation: EvalPerf (*nix only)

pip install --upgrade "evalplus[perf,vllm] @ git+https://github.com/evalplus/evalplus"
# Or `pip install "evalplus[perf,vllm]" --upgrade` for the latest stable release

sudo sh -c 'echo 0 > /proc/sys/kernel/perf_event_paranoid' # Enable perf
evalplus.evalperf --model "ise-uiuc/Magicoder-S-DS-6.7B" --backend vllm

🛡️ Safe code execution within Docker :: click to expand ::

# Local generation
evalplus.codegen --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                 --dataset evalperf                     \
                 --backend vllm                         \
                 --temperature 1.0                      \
                 --n-samples 100

# Code execution within Docker
sudo sh -c 'echo 0 > /proc/sys/kernel/perf_event_paranoid' # Enable perf
docker run --cap-add PERFMON --rm --pull=always -v $(pwd)/evalplus_results:/app ganler/evalplus:latest \
           evalplus.evalperf --samples /app/evalperf/ise-uiuc--Magicoder-S-DS-6.7B_vllm_temp_1.0.jsonl

🚀 LLM Backends

HuggingFace models

transformers backend:

evalplus.evaluate --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                  --dataset [humaneval|mbpp]             \
                  --backend hf                           \
                  --greedy

Note

EvalPlus uses different prompts for base and chat models. By default it is detected by tokenizer.chat_template when using hf/vllm as backend. For other backends, only chat mode is allowed.

Therefore, if your base models come with a tokenizer.chat_template, please add --force-base-prompt to avoid being evaluated in a chat mode.

Enable Flash Attention 2 :: click to expand ::

# Install Flash Attention 2
pip install packaging ninja
pip install flash-attn --no-build-isolation
# Note: if you have installation problem, consider using pre-built
# wheels from https://github.com/Dao-AILab/flash-attention/releases

# Run evaluation with FA2
evalplus.evaluate --model "ise-uiuc/Magicoder-S-DS-6.7B"         \
                  --dataset [humaneval|mbpp]                     \
                  --backend hf                                   \
                  --attn-implementation [flash_attention_2|sdpa] \
                  --greedy

vllm backend:

evalplus.evaluate --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                  --dataset [humaneval|mbpp]             \
                  --backend vllm                         \
                  --tp [TENSOR_PARALLEL_SIZE]            \
                  --greedy

openai compatible servers (e.g., vLLM):

# OpenAI models
export OPENAI_API_KEY="{KEY}" # https://platform.openai.com/settings/organization/api-keys
evalplus.evaluate --model "gpt-4o-2024-08-06"  \
                  --dataset [humaneval|mbpp]   \
                  --backend openai --greedy

# DeepSeek
export OPENAI_API_KEY="{KEY}" # https://platform.deepseek.com/api_keys
evalplus.evaluate --model "deepseek-chat"              \
                  --dataset [humaneval|mbpp]           \
                  --base-url https://api.deepseek.com  \
                  --backend openai --greedy

# Grok
export OPENAI_API_KEY="{KEY}" # https://console.x.ai/
evalplus.evaluate --model "grok-beta"             \
                  --dataset [humaneval|mbpp]      \
                  --base-url https://api.x.ai/v1  \
                  --backend openai --greedy

# vLLM server
# First, launch a vLLM server: https://docs.vllm.ai/en/latest/serving/deploying_with_docker.html
evalplus.evaluate --model "ise-uiuc/Magicoder-S-DS-6.7B" \
                  --dataset [humaneval|mbpp]             \
                  --base-url http://localhost:8000/v1    \
                  --backend openai --greedy

# GPTQModel
evalplus.evaluate --model "ModelCloud/Llama-3.2-1B-Instruct-gptqmodel-4bit-vortex-v1" \
                  --dataset [humaneval|mbpp]                                          \
                  --backend gptqmodel --greedy

OpenAI models

Access OpenAI APIs from OpenAI Console

export OPENAI_API_KEY="[YOUR_API_KEY]"
evalplus.evaluate --model "gpt-4o"            \
                  --dataset [humaneval|mbpp]  \
                  --backend openai            \
                  --greedy

Anthropic models

Access Anthropic APIs from Anthropic Console

export ANTHROPIC_API_KEY="[YOUR_API_KEY]"
evalplus.evaluate --model "claude-3-haiku-20240307" \
                  --dataset [humaneval|mbpp]        \
                  --backend anthropic               \
                  --greedy

Google Gemini models

Access Gemini APIs from Google AI Studio

export GOOGLE_API_KEY="[YOUR_API_KEY]"
evalplus.evaluate --model "gemini-1.5-pro"    \
                  --dataset [humaneval|mbpp]  \
                  --backend google            \
                  --greedy

Amazon Bedrock models

Amazon Bedrock

export BEDROCK_ROLE_ARN="[BEDROCK_ROLE_ARN]"
evalplus.evaluate --model "anthropic.claude-3-5-sonnet-20241022-v2:0" \
                  --dataset [humaneval|mbpp]                          \
                  --backend bedrock                                   \
                  --greedy

You can checkout the generation and results at evalplus_results/[humaneval|mbpp]/

⏬ 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

📚 Documents

To learn more about how to use EvalPlus, please refer to:

📜 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},
}

@inproceedings{evalperf,
  title = {Evaluating Language Models for Efficient Code Generation},
  author = {Liu, Jiawei and Xie, Songrun and Wang, Junhao and Wei, Yuxiang and Ding, Yifeng and Zhang, Lingming},
  booktitle = {First Conference on Language Modeling},
  year = {2024},
  url = {https://openreview.net/forum?id=IBCBMeAhmC},
}