ENAMEL

Getting Started | Library Usage | LLM Leaderboard | Acknowledgements

What is ENAMEL?

ENAMEL is a rigorous and high-standard benchmark for evaluating the capability of large language models (LLMs) in generating efficient code. We provide:

• A new metric $\text{eff}@k$ characterizing the relationship between code efficiency and sample size $k$;
• A problem set consisting of 142 high-quality problems selected from OpenAI HumanEval;
• Expert-written efficient reference solutions, setting a high-standard for efficiency evaluation;
• Expert-written strong test case generators, enabling a rigorous evaluation of both correctness and efficiency;
• A Python library enam for easily evaluating the efficiency of LLM-generated code.

If you are interested in our work, please feel free to check our paper for detail.

Getting Started

Dependencies

Before running the code, please ensure the following dependencies:

• Linux
• Python >= 3.10
• Tqdm >= 3.1.4
• NumPy >= 1.4.0
• Pandas >= 1.0

Using our generated test cases and LLM-generated code samples

To facilitate reproduction, we share on HuggingFace our generated test cases and LLM-generated code samples used in our evaluation. Please download eval~tests.pkl into the cache/ folder and download the code samples into the samples/ folder.

To reproduce our results, please run demo.py, where --load_name specifies the file name of code samples (without file extension), and --tests specifies the generated test cases. For example, to evaluate the HumanEval+ canonical solutions, please run:

python3 demo.py --load_name humanevalplus-canonical --tests cache/eval~tests.pkl

Evaluating zipped code samples provided by EvalPlus

Our demo also supports the zipped code samples provided by EvalPlus. Please put their .zip files into our samples/ folder without renaming the files. For example, to evaluate the GPT-4 code samples gpt-4_temp_0.0.zip from EvalPlus, please run:

python3 demo.py --load_name gpt-4_temp_0.0 --tests cache/eval~tests.pkl

Warning: It is known to us that our evaluator might be unable to kill a code sample if the code uses try ... except ... within an infinity loop because the killing signal will be caught. We have decided not to resolve this issue because resolving it with multiprocessing will significantly slow down the evaluation process. If you do encounter this issue, please consider removing such code samples. (This issue indeed happens for two code samples provided by EvalPlus, so our demo will automatically handle it if you use the zipped code samples from EvalPlus.)

Evaluating new code samples

If you want to evaluate your own code samples, please organize them as a .json file, put it in the samples/ folder, and run demo.py. For example, if the code samples are in the file samples/codes.json, please run:

python3 demo.py --load_name codes --tests cache/eval~tests.pkl

The .json file should be a dict of lists such that codes[str(i)][j] is the $j$-th code sample of problem $i$.

Library Usage

Our benchmark is also available as a Python library. Please see demo.py for an example usage of our library.

Notice: It is NOT recommended to use multiple threads or processes in efficiency evaluation. That can negatively affect efficiency results.

Installation

Our library enam can be installed via pip:

pip install enam --upgrade

Note: To distinguish from our benchmark ENAMEL, we name our library enam.

LLM Leaderboard

The following table is a leaderboard of 30 LLMs (under greedy decoding) as well as HumanEval/HumanEval+ canonical solutions. Results show that LLMs fall short of generating expert-level efficient code. For more results, please refer to our paper.

We welcome LLM developers to submit their results to enrich this leaderboard. If you would like to submit your results, please organize your generated code samples into a .json file as described above and contact Ruizhong Qiu (rq5 AT illinois DOT edu).

No.	Name	eff@1	pass@1
1	HumanEval+	0.517	0.958
2	GPT-4 Turbo (Nov 2023)	0.470	0.796
3	HumanEval	0.458	0.908
4	GPT-4 (Jun 2023)	0.454	0.831
5	Llama 3 70B Instruct	0.421	0.746
6	Mixtral 8x22B Instruct	0.408	0.746
7	Claude 3 Opus	0.401	0.789
8	Phind Code Llama V2	0.394	0.683
9	Claude 3 Haiku	0.386	0.739
10	ChatGPT	0.364	0.683
11	Claude 3 Sonnet	0.345	0.662
12	Llama 3 8B Instruct	0.344	0.592
13	Code Llama 34B Python	0.268	0.458
14	Mixtral 8x7B Instruct	0.266	0.444
15	Code Llama 70B Python	0.264	0.500
16	Code Llama 7B Python	0.247	0.373
17	Code Llama 13B Python	0.216	0.408
18	StarCoder	0.195	0.352
19	CodeGen 6B	0.193	0.296
20	CodeGen 16B	0.169	0.310
21	CodeT5+ 16B	0.160	0.317
22	CodeGen 2B	0.153	0.254
23	Mistral 7B	0.152	0.275
24	Vicuna 13B	0.123	0.176
25	SantaCoder	0.100	0.141
26	Incoder 6B	0.091	0.127
27	GPT-J	0.083	0.106
28	Incoder 1B	0.066	0.092
29	Vicuna 7B	0.061	0.099
30	GPT-Neo 2B	0.043	0.056
31	PolyCoder	0.037	0.049
32	StableLM 7B	0.020	0.021

Acknowledgements

• OpenAI HumanEval
• EvalPlus
• HuggingFace CodeEval