Code implementations for FunSearch, RandomSampling, (1+1)-EPS, EoH, ReEvo

This repository includes code for the following paper:

Towards Understanding the Effectiveness of Automatic Heuristic Design with Large Language Models

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Installation and requirements

Please note that the Python version must be larger or equal to Python 3.9, or the 'ast' package used in the implementations will fail to work.

You can run locally if enough GPU devices are available. Or you can try to use LLM interfaces to request responses online.

Please install the packages listed in requirements.txt.

LLMs

Not all LLMs can be used for an LLM-based EPS method.

LLMs	FunSearch	(1+1)-EPS (HillClimbing)	RandomSampling	EoH	ReEvo
UniXcoder	x	x	v	x	x
CodeLlama-7B-instruct	v	v	v	v	v
CodeLlama-34B-Instruct	v	v	v	v	v
StarCoder	v	v	v	x	x
DeepSeek-Coder-6.7B	v	v	v	v	v
DeepSeek-Coder-33B	v	v	v	v	v
GPT-3.5 (API)	v	v	v	v	v
GPT-4 (API)	v	v	v	v	v
Claude-3-Opus (API)	v	v	v	v	v

Project structure

funsearch folder includes FunSearch implementations.

    |----task_name (admissible_set, bin_packing_or, bin_packing_weibull, gls_tsp)

        |----task_name_completion.py for LLMs that support code completion generation.

        |----task_name_instruct.py for LLMs that support instruct generation.

        |----task_name_api.py using LLM api.

    |----funsearch_impl

rand_search folder includes RandomSampling implementations.

    |----task_name (admissible_set, bin_packing_or, bin_packing_weibull, gls_tsp)

        |----task_name_completion.py for LLMs that support code completion generation.

        |----task_name_instruct.py for LLMs that support instruct generation.

        |----task_name_api.py using LLM api.

    |----rand_search_impl

hill_climb folder includes (1+1)-EPS implementations.

    |----task_name (admissible_set, bin_packing_or, bin_packing_weibull, gls_tsp)

        |----task_name_completion.py for LLMs that support code completion generation.

        |----task_name_instruct.py for LLMs that support instruct generation.

        |----task_name_api.py using LLM api.

    |----hill_climb_impl

Run LLM-based EPS

Python script

Run differnet python script for different LLM.

LLMs	Run Python file
UniXcoder	task_name_completion.py
CodeLlama-7B-instruct	task_name_completion.py
CodeLlama-34B-Instruct	task_name_completion.py
StarCoder	task_name_completion.py
DeepSeek-Coder-6.7B	task_name_instruct.py
DeepSeek-Coder-33B	task_name_instruct.py
GPT-3.5 (API)	task_name_api.py
GPT-4 (API)	task_name_api.py
Claude-3-Opus (API)	task_name_api.py

Run with local LLM

Download LLM

Please refer to tutorials at Huggingface (https://huggingface.co) or Huggingface mirror (https://hf-mirror.com).

Deploy an LLM locally

Please note that it is suggested to deploy the LLM server in a 'screen' environment. Which can be created as below:

screen -R local_llm

The server scripts are in llm_server folder. Please find the right python file.

cd llm_server

python xxx.py \
  --path [your path to LLM] \
  --d [GPU ids, e.g., 0 1 2 3 4] \
  --host [the ip of the server, default is 127.0.0.1] \
  --port [the port of the server] \
  --quantization  # if you want to do quantization, please add this argument

Prepare a config file

If there is an issue with the LLM server, or you need to change the url of the LLM during runtime, you can simply modify the config file instead of re-running.

An template for a config file is shown below:

{
  "url": "http://127.0.0.1:11101/completions",  # the URL for the LLM server
  "samples_per_prompt": 4,                      # samples per prompt
  "batch_inference": true                       # whether to use batch inference to accelerate
}

Start LLM-based EPS

We suggest using 'nohup'. The instruction is shown below.

nohup python xxx.py --run 1 --config run1_runtime_llm_config.json > run1.out 2>&1 &

Run with LLM API

Choose the correct LLM model and set the API key

Please modify the code of _draw_sample function in task_name_api.py.

def _draw_sample(self, content: str) -> str:
    prompt = '\n'.join([content, self._additional_prompt])
    while True:
        try:
            conn = http.client.HTTPSConnection(
              "api.chatanywhere.com.cn",  # 1. Set API URL.
              timeout=self._timeout
            )  
            payload = json.dumps({
                "max_tokens": 512,
                "model": "gpt-3.5-turbo",  # 2. Set LLM model name. Please refer to your API provider.
                "messages": [
                    {
                        "role": "user",
                        "content": prompt
                    }
                ]
            })
            headers = {
                'Authorization': 'Bearer sk-your api key here',  # 3. Set your API key.
                'User-Agent': 'Apifox/1.0.0 (https://apifox.com)',
                'Content-Type': 'application/json'
            }
            conn.request("POST", "/v1/chat/completions", payload, headers)
            res = conn.getresponse()
            data = res.read().decode("utf-8")
            data = json.loads(data)
            response = data['choices'][0]['message']['content']
            # trim function
            if self._trim:
                response = _trim_preface_of_body(response)
            return response
        except Exception as e:
            # print(e)
            time.sleep(2)
            continue

Start LLM-based EPS

We suggest using 'nohup'. The instruction is shown below.

nohup python xxx.py --run 1 > run1.out 2>&1 &