""" OpenEvolve <-> lm-evaluation-harness adapter Implements generation only, no loglikelihood. Tasks such as GSM8K / BoolQ / MMLU-Math / AQUA-RAT and most code suites should work fine because they grade on the generated answer string. """ from __future__ import annotations import subprocess, tempfile, json, os, argparse, math, pathlib from pathlib import Path from typing import List, Dict, Tuple, Any, Iterable import lm_eval from lm_eval.tasks import TaskManager from lm_eval.evaluator import evaluate from lm_eval.api.model import LM from lm_eval.api.registry import register_model from datetime import datetime # cd to the parent parent directory of this file os.chdir(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) PIPELINE_CMD = ["python3", "openevolve-run.py"] @register_model("openevolve") class OpenEvolve(LM): def __init__( self, init_file: str = "initial_content_stub.txt", evaluator_file: str = "evaluator_stub.py", config_file: str = "config.yml", iterations: int = 5, extra_param: List[str] = [], **kwargs, ): super().__init__() self.init_file = init_file self.evaluator_file = evaluator_file self.iterations = iterations self.extra_param = extra_param self.config_file = config_file # folder must match prompt:template_dir in config.yml! self.prompt_path = "examples/lm_eval/prompts/system_message.txt" self.evaluator_prompt_path = "examples/lm_eval/prompts/evaluator_system_message.txt" self.best_path = "examples/lm_eval/openevolve_output/best/best_program.txt" self.base_system_message = "You are an expert task solver, with a lot of commonsense, math, language and coding knowledge.\n\nConsider this task:\n```{prompt}ยดยดยด" def generate(self, prompts: List[str], max_gen_toks: int = None, stop=None, **kwargs): outs = [] for prompt in prompts: # Task prompt becomes the system message. User prompt is the evolutionary logic. # We create temporary prompt files with the system message with Path(self.prompt_path).open("w") as f: f.write(self.base_system_message.format(prompt=prompt)) with Path(self.evaluator_prompt_path).open("w") as f: f.write(self.base_system_message.format(prompt=prompt)) cmd = ( PIPELINE_CMD + ["--config", self.config_file] + ["--iterations", str(self.iterations)] + self.extra_param + [self.init_file, self.evaluator_file] ) print(f"Running command: {' '.join(cmd)}") try: res = subprocess.run(cmd, capture_output=True, text=True, check=True) text = res.stdout.strip() print(f"Process output: {text}") except subprocess.CalledProcessError as e: print(f"Command failed with return code {e.returncode}") print(f"stderr: {e.stderr}") text = "" print(f"# Prompt: {prompt}") with Path(self.best_path).open("r") as f: best = f.read().strip() print(f"# Answer: {best}") # honour stop tokens if stop: for s in stop: idx = best.find(s) if idx != -1: best = best[:idx] break outs.append(best) return outs # for tasks that ask for log likelihood, indicate that it is unsupported def loglikelihood(self, requests: Iterable[Tuple[str, str]], **kw): # return [(-math.inf, False) for _ in requests] raise NotImplementedError def loglikelihood_rolling(self, requests: Iterable[str], **kw): # return [(-math.inf, False) for _ in requests] raise NotImplementedError def generate_until(self, requests: Iterable[Any], **kw) -> List[str]: ctxs, stops = [], [] for req in requests: # ---------------- old: plain tuple ---------------- if isinstance(req, tuple): ctx, until = req # -------------- new: Instance object -------------- else: ctx = req.args[0] # first positional arg until = [] # if a second positional arg exists and is list-like, # treat it as the stop sequence if len(req.args) > 1 and isinstance(req.args[1], (list, tuple)): until = list(req.args[1]) ctxs.append(ctx) stops.append(until) # 2) run your real generator once per context gens = self.generate(ctxs, stop=None) # 3) post-trim at the first stop sequence cleaned = [] for g, until in zip(gens, stops): for s in until: idx = g.find(s) if idx != -1: g = g[:idx] break cleaned.append(g) return cleaned if __name__ == "__main__": # cli arguments for primary model, secondary model, iterations, config and tasks p = argparse.ArgumentParser( description="OpenEvolve <-> lm-evaluation-harness adapter.", ) p.add_argument("--config", default="examples/lm_eval/config.yml", help="config file") p.add_argument( "--init_file", default="examples/lm_eval/initial_content_stub.txt", help="initial content file", ) p.add_argument( "--evaluator_file", default="examples/lm_eval/evaluator_stub.py", help="evaluator file" ) p.add_argument("--iterations", default=5, type=int, help="number of iterations") p.add_argument( "--limit", default=None, type=int, help="limit the number of examples per task that are executed", ) # p.add_argument("--tasks", default="boolq,gsm8k,mmlu", help="comma-list of tasks to evaluate") p.add_argument("--tasks", default="gsm8k", help="list of tasks to evaluate") p.add_argument("--output_path", default="results", help="output path for results") args = p.parse_args() lm_obj = OpenEvolve( init_file=args.init_file, evaluator_file=args.evaluator_file, iterations=args.iterations, config_file=args.config, ) task_dict = lm_eval.tasks.get_task_dict(args.tasks.split(",")) results = evaluate( lm=lm_obj, task_dict=task_dict, limit=args.limit, ) # write out the results pathlib.Path( args.output_path, ).mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") results_path = pathlib.Path( os.path.join( args.output_path, f"{timestamp}_iter{args.iterations}.json", ) ) with results_path.open("w") as f: json.dump(results, f, indent=2) # print result summary short = {} for task, metrics in results["results"].items(): # pick the first value that is a real number for key, val in metrics.items(): if isinstance(val, (int, float)): short[task] = (key, val) # store *both* name & value break print(f"Full results written to {results_path}\n") print("Headline metrics:") for task, (name, value) in short.items(): print(f" {task:<15} {name:<12} {value:.3%}") print("\nNote: Never cite the overall average when some components were skipped!")