open_pt_llm_leaderboard / src /scripts /update_all_request_files.py
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from huggingface_hub import ModelFilter, snapshot_download
from huggingface_hub import ModelCard
import os
import json
import time
from collections import defaultdict
from src.submission.check_validity import is_model_on_hub, check_model_card, get_model_tags
from src.leaderboard.read_evals import EvalResult
from src.envs import (
DYNAMIC_INFO_REPO,
DYNAMIC_INFO_PATH,
DYNAMIC_INFO_FILE_PATH,
API,
H4_TOKEN,
ORIGINAL_HF_LEADERBOARD_RESULTS_REPO,
ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS_PATH,
GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS
)
from src.display.utils import ORIGINAL_TASKS
def update_models(file_path, models, original_leaderboard_files=None):
"""
Search through all JSON files in the specified root folder and its subfolders,
and update the likes key in JSON dict from value of input dict
"""
with open(file_path, "r") as f:
model_infos = json.load(f)
for model_id, data in model_infos.items():
if model_id not in models:
data['still_on_hub'] = False
data['likes'] = 0
data['downloads'] = 0
data['created_at'] = ""
data['original_llm_scores'] = {}
continue
model_cfg = models[model_id]
data['likes'] = model_cfg.likes
data['downloads'] = model_cfg.downloads
data['created_at'] = str(model_cfg.created_at)
#data['params'] = get_model_size(model_cfg, data['precision'])
data['license'] = model_cfg.card_data.license if model_cfg.card_data is not None else ""
data['original_llm_scores'] = {}
# Is the model still on the hub?
model_name = model_id
if model_cfg.card_data is not None and hasattr(model_cfg.card_data, "base_model") and model_cfg.card_data.base_model is not None:
model_name = model_cfg.card_data.base_model # for adapters, we look at the parent model
still_on_hub, _, _ = is_model_on_hub(
model_name=model_name, revision=data.get("revision"), trust_remote_code=True, test_tokenizer=False, token=H4_TOKEN
)
data['still_on_hub'] = still_on_hub
tags = []
if still_on_hub:
status, _, model_card = check_model_card(model_id)
tags = get_model_tags(model_card, model_id)
if original_leaderboard_files is not None and model_id in original_leaderboard_files:
eval_results = {}
for filepath in original_leaderboard_files[model_id]:
eval_result = EvalResult.init_from_json_file(filepath, is_original=True)
# 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
for eval_result in eval_results.values():
precision = eval_result.precision.value.name
if len(eval_result.results) < len(ORIGINAL_TASKS):
continue
data['original_llm_scores'][precision] = sum([v for v in eval_result.results.values() if v is not None]) / len(ORIGINAL_TASKS)
data["tags"] = tags
with open(file_path, 'w') as f:
json.dump(model_infos, f, indent=2)
def update_dynamic_files():
""" This will only update metadata for models already linked in the repo, not add missing ones.
"""
snapshot_download(
repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
print("UPDATE_DYNAMIC: Loaded snapshot")
# Get models
start = time.time()
models = list(API.list_models(
filter=ModelFilter(task="text-generation"),
full=False,
cardData=True,
fetch_config=True,
))
id_to_model = {model.id : model for model in models}
id_to_leaderboard_files = defaultdict(list)
if GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS:
try:
print("UPDATE_DYNAMIC: Downloading Original HF Leaderboard results snapshot")
snapshot_download(
repo_id=ORIGINAL_HF_LEADERBOARD_RESULTS_REPO, local_dir=ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
)
#original_leaderboard_files = [] #API.list_repo_files(ORIGINAL_HF_LEADERBOARD_RESULTS_REPO, repo_type='dataset')
for dirpath,_,filenames in os.walk(ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS_PATH):
for f in filenames:
if not (f.startswith('results_') and f.endswith('.json')):
continue
filepath = os.path.join(dirpath[len(ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS_PATH)+1:], f)
model_id = filepath[:filepath.find('/results_')]
id_to_leaderboard_files[model_id].append(os.path.join(dirpath, f))
for model_id in id_to_leaderboard_files:
id_to_leaderboard_files[model_id].sort()
except Exception as e:
print(f"UPDATE_DYNAMIC: Could not download original results from : {e}")
id_to_leaderboard_files = None
print(f"UPDATE_DYNAMIC: Downloaded list of models in {time.time() - start:.2f} seconds")
start = time.time()
update_models(DYNAMIC_INFO_FILE_PATH, id_to_model, id_to_leaderboard_files)
print(f"UPDATE_DYNAMIC: updated in {time.time() - start:.2f} seconds")
API.upload_file(
path_or_fileobj=DYNAMIC_INFO_FILE_PATH,
path_in_repo=DYNAMIC_INFO_FILE_PATH.split("/")[-1],
repo_id=DYNAMIC_INFO_REPO,
repo_type="dataset",
commit_message=f"Daily request file update.",
)
print(f"UPDATE_DYNAMIC: pushed to hub")