backend / src /auto_leaderboard /load_results.py
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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,
}
@dataclass
class EvalResult:
eval_name: str
org: str
model: str
revision: str
results: dict
is_8bit: bool = False
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.is_8bit.name] = self.is_8bit
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)
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]