from dataclasses import dataclass import glob import json import os from typing import Dict, List, Tuple import dateutil from src.utils_display import AutoEvalColumn, make_clickable_model import numpy as np METRICS = ["acc_norm", "acc_norm", "acc", "mc2"] BENCHMARKS = ["arc:challenge", "hellaswag", "hendrycksTest", "truthfulqa:mc"] BENCH_TO_NAME = { "arc:challenge": AutoEvalColumn.arc.name, "hellaswag": AutoEvalColumn.hellaswag.name, "hendrycksTest": AutoEvalColumn.mmlu.name, "truthfulqa:mc": AutoEvalColumn.truthfulqa.name, } @dataclass class EvalResult: eval_name: str org: str model: str revision: str results: dict precision: str = "16bit" model_type: str = "" weight_type: str = "" def to_dict(self): if self.org is not None: base_model = f"{self.org}/{self.model}" else: base_model = f"{self.model}" data_dict = {} data_dict["eval_name"] = self.eval_name # not a column, just a save name data_dict["weight_type"] = self.weight_type # not a column, just a save name data_dict[AutoEvalColumn.precision.name] = self.precision data_dict[AutoEvalColumn.model_type.name] = self.model_type data_dict[AutoEvalColumn.model.name] = make_clickable_model(base_model) data_dict[AutoEvalColumn.dummy.name] = base_model data_dict[AutoEvalColumn.revision.name] = self.revision data_dict[AutoEvalColumn.average.name] = round( sum([v for k, v in self.results.items()]) / 4.0, 1 ) for benchmark in BENCHMARKS: if benchmark not in self.results.keys(): self.results[benchmark] = None for k, v in BENCH_TO_NAME.items(): data_dict[v] = self.results[k] return data_dict def parse_eval_result(json_filepath: str) -> Tuple[str, list[dict]]: with open(json_filepath) as fp: data = json.load(fp) for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]: if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0: return None, [] # we skip models with the wrong version try: config = data["config"] except KeyError: config = data["config_general"] model = config.get("model_name", None) if model is None: model = config.get("model_args", None) model_sha = config.get("model_sha", "") eval_sha = config.get("lighteval_sha", "") model_split = model.split("/", 1) model = model_split[-1] if len(model_split) == 1: org = None model = model_split[0] result_key = f"{model}_{model_sha}_{eval_sha}" else: org = model_split[0] model = model_split[1] result_key = f"{org}_{model}_{model_sha}_{eval_sha}" eval_results = [] for benchmark, metric in zip(BENCHMARKS, METRICS): accs = np.array([v[metric] for k, v in data["results"].items() if benchmark in k]) if accs.size == 0: continue mean_acc = round(np.mean(accs) * 100.0, 1) eval_results.append(EvalResult( eval_name=result_key, org=org, model=model, revision=model_sha, results={benchmark: mean_acc}, #todo model_type=, weight_type= )) return result_key, eval_results def get_eval_results(is_public) -> List[EvalResult]: json_filepaths = [] for root, dir, files in os.walk("eval-results"): # We should only have json files in model results if len(files) == 0 or any([not f.endswith(".json") for f in files]): continue # Sort the files by date try: files.sort(key=lambda x: dateutil.parser.parse(x.split("_", 1)[-1][:-5])) except dateutil.parser._parser.ParserError: up_to_date = files[-1] up_to_date = files[-1] json_filepaths.append(os.path.join(root, up_to_date)) eval_results = {} for json_filepath in json_filepaths: result_key, results = parse_eval_result(json_filepath) for eval_result in results: if result_key in eval_results.keys(): eval_results[result_key].results.update(eval_result.results) else: eval_results[result_key] = eval_result eval_results = [v for v in eval_results.values()] return eval_results def get_eval_results_dicts(is_public=True) -> List[Dict]: eval_results = get_eval_results(is_public) return [e.to_dict() for e in eval_results]