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import glob | |
import json | |
import math | |
import os | |
from dataclasses import dataclass | |
from typing import Dict, List, Tuple | |
import dateutil | |
import numpy as np | |
from src.display.formatting import make_clickable_model | |
from src.display.utils import AutoEvalColumn, ModelType, Tasks | |
from src.submission.check_validity import is_model_on_hub | |
class EvalResult: | |
eval_name: str | |
full_model: str | |
org: str | |
model: str | |
revision: str | |
results: dict | |
precision: str = "" | |
model_type: ModelType = ModelType.Unknown | |
weight_type: str = "Original" | |
architecture: str = "Unknown" | |
license: str = "?" | |
likes: int = 0 | |
num_params: int = 0 | |
date: str = "" | |
still_on_hub: bool = False | |
def init_from_json_file(self, json_filepath): | |
with open(json_filepath) as fp: | |
data = json.load(fp) | |
# We manage the legacy config format | |
config = data.get("config", data.get("config_general", None)) | |
# Precision | |
precision = config.get("model_dtype") | |
if precision == "None": | |
precision = "GPTQ" | |
# Get model and org | |
org_and_model = config.get("model_name", config.get("model_args", None)) | |
org_and_model = org_and_model.split("/", 1) | |
if len(org_and_model) == 1: | |
org = None | |
model = org_and_model[0] | |
result_key = f"{model}_{precision}" | |
else: | |
org = org_and_model[0] | |
model = org_and_model[1] | |
result_key = f"{org}_{model}_{precision}" | |
still_on_hub = is_model_on_hub( | |
"/".join(org_and_model), config.get("model_sha", "main"), trust_remote_code=True | |
)[0] | |
# Extract results available in this file (some results are split in several files) | |
results = {} | |
for task in Tasks: | |
task = task.value | |
# We skip old mmlu entries | |
wrong_mmlu_version = False | |
if task.benchmark == "hendrycksTest": | |
for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]: | |
if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0: | |
wrong_mmlu_version = True | |
if wrong_mmlu_version: | |
continue | |
# Some truthfulQA values are NaNs | |
if task.benchmark == "truthfulqa:mc" and "harness|truthfulqa:mc|0" in data["results"]: | |
if math.isnan(float(data["results"]["harness|truthfulqa:mc|0"][task.metric])): | |
results[task.benchmark] = 0.0 | |
continue | |
# We average all scores of a given metric (mostly for mmlu) | |
accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark in k]) | |
if accs.size == 0 or any([acc is None for acc in accs]): | |
continue | |
mean_acc = np.mean(accs) * 100.0 | |
results[task.benchmark] = mean_acc | |
return self( | |
eval_name=result_key, | |
full_model="/".join(org_and_model), | |
org=org, | |
model=model, | |
results=results, | |
precision=precision, # todo model_type=, weight_type= | |
revision=config.get("model_sha", ""), | |
date=config.get("submission_date", ""), | |
still_on_hub=still_on_hub, | |
) | |
def update_with_request_file(self): | |
request_file = get_request_file_for_model(self.full_model, self.precision) | |
try: | |
with open(request_file, "r") as f: | |
request = json.load(f) | |
self.model_type = ModelType.from_str(request.get("model_type", "")) | |
self.license = request.get("license", "?") | |
self.likes = request.get("likes", 0) | |
self.num_params = request.get("params", 0) | |
except Exception: | |
print(f"Could not find request file for {self.org}/{self.model}") | |
def to_dict(self): | |
average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) | |
data_dict = { | |
"eval_name": self.eval_name, # not a column, just a save name, | |
AutoEvalColumn.precision.name: self.precision, | |
AutoEvalColumn.model_type.name: self.model_type.value.name, | |
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol, | |
AutoEvalColumn.weight_type.name: self.weight_type, | |
AutoEvalColumn.model.name: make_clickable_model(self.full_model), | |
AutoEvalColumn.dummy.name: self.full_model, | |
AutoEvalColumn.revision.name: self.revision, | |
AutoEvalColumn.average.name: average, | |
AutoEvalColumn.license.name: self.license, | |
AutoEvalColumn.likes.name: self.likes, | |
AutoEvalColumn.params.name: self.num_params, | |
AutoEvalColumn.still_on_hub.name: self.still_on_hub, | |
} | |
for task in Tasks: | |
data_dict[task.value.col_name] = self.results[task.value.benchmark] | |
return data_dict | |
def get_request_file_for_model(model_name, precision): | |
request_files = os.path.join( | |
"eval-queue", | |
f"{model_name}_eval_request_*.json", | |
) | |
request_files = glob.glob(request_files) | |
# Select correct request file (precision) | |
request_file = "" | |
request_files = sorted(request_files, reverse=True) | |
for tmp_request_file in request_files: | |
with open(tmp_request_file, "r") as f: | |
req_content = json.load(f) | |
if ( | |
req_content["status"] in ["FINISHED", "PENDING_NEW_EVAL"] | |
and req_content["precision"] == precision.split(".")[-1] | |
): | |
request_file = tmp_request_file | |
return request_file | |
def get_eval_results(results_path: str) -> List[EvalResult]: | |
json_filepaths = [] | |
for root, _, files in os.walk(results_path): | |
# 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: x.removesuffix(".json").removeprefix("results_")[:-7]) | |
except dateutil.parser._parser.ParserError: | |
files = [files[-1]] | |
# up_to_date = files[-1] | |
for file in files: | |
json_filepaths.append(os.path.join(root, file)) | |
eval_results = {} | |
for json_filepath in json_filepaths: | |
# Creation of result | |
eval_result = EvalResult.init_from_json_file(json_filepath) | |
eval_result.update_with_request_file() | |
# Store results of same eval together | |
eval_name = eval_result.eval_name | |
if eval_name in eval_results.keys(): | |
eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) | |
else: | |
eval_results[eval_name] = eval_result | |
results = [] | |
for v in eval_results.values(): | |
try: | |
results.append(v.to_dict()) | |
except KeyError: # not all eval values present | |
continue | |
return results | |