Nathan Habib
get model type info from request file
80f4eeb
raw
history blame
4.54 kB
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]