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| from dataclasses import dataclass | |
| import glob | |
| import json | |
| from typing import Dict, List, Tuple | |
| 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, | |
| } | |
| class EvalResult: | |
| eval_name: str | |
| org: str | |
| model: str | |
| revision: str | |
| results: dict | |
| precision: str = "16bit" | |
| 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[AutoEvalColumn.precision.name] = self.precision | |
| 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 | |
| config = data["config"] | |
| 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( | |
| result_key, org, model, model_sha, {benchmark: mean_acc} | |
| )) | |
| return result_key, eval_results | |
| def get_eval_results(is_public) -> List[EvalResult]: | |
| json_filepaths = glob.glob( | |
| "eval-results/**/results*.json", recursive=True | |
| ) | |
| if not is_public: | |
| json_filepaths += glob.glob( | |
| "private-eval-results/**/results*.json", recursive=True | |
| ) | |
| 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] | |