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import glob
import json
from dataclasses import dataclass
from typing import Optional
from huggingface_hub import HfApi, snapshot_download
from src.utils import my_snapshot_download
@dataclass
class EvalRequest:
model: str
private: bool
status: str
json_filepath: str
weight_type: str = "Original"
model_type: str = "" # pretrained, finetuned, with RL
precision: str = "" # float16, bfloat16
base_model: Optional[str] = None # for adapter models
revision: str = "main" # commit
submitted_time: Optional[str] = "2022-05-18T11:40:22.519222" # random date just so that we can still order requests by date
model_type: Optional[str] = None
likes: Optional[int] = 0
params: Optional[int] = None
license: Optional[str] = ""
def get_model_args(self) -> str:
model_args = f"pretrained={self.model},revision={self.revision},parallelize=True"
if self.precision in ["float16", "float32", "bfloat16"]:
model_args += f",dtype={self.precision}"
# Quantized models need some added config, the install of bits and bytes, etc
#elif self.precision == "8bit":
# model_args += ",load_in_8bit=True"
#elif self.precision == "4bit":
# model_args += ",load_in_4bit=True"
#elif self.precision == "GPTQ":
# A GPTQ model does not need dtype to be specified,
# it will be inferred from the config
pass
else:
raise Exception(f"Unknown precision {self.precision}.")
return model_args
def set_eval_request(api: HfApi, eval_request: EvalRequest, set_to_status: str, hf_repo: str, local_dir: str):
"""Updates a given eval request with its new status on the hub (running, completed, failed, ...)"""
json_filepath = eval_request.json_filepath
with open(json_filepath) as fp:
data = json.load(fp)
data["status"] = set_to_status
with open(json_filepath, "w") as f:
f.write(json.dumps(data))
api.upload_file(path_or_fileobj=json_filepath, path_in_repo=json_filepath.replace(local_dir, ""),
repo_id=hf_repo, repo_type="dataset")
def get_eval_requests(job_status: list, local_dir: str, hf_repo: str) -> list[EvalRequest]:
"""Get all pending evaluation requests and return a list in which private
models appearing first, followed by public models sorted by the number of
likes.
Returns:
`list[EvalRequest]`: a list of model info dicts.
"""
my_snapshot_download(repo_id=hf_repo, revision="main", local_dir=local_dir, repo_type="dataset", max_workers=60)
json_files = glob.glob(f"{local_dir}/**/*.json", recursive=True)
eval_requests = []
for json_filepath in json_files:
with open(json_filepath) as fp:
data = json.load(fp)
if data["status"] in job_status:
# import pdb
# breakpoint()
data["json_filepath"] = json_filepath
if 'job_id' in data:
del data['job_id']
eval_request = EvalRequest(**data)
eval_requests.append(eval_request)
return eval_requests
def check_completed_evals(api: HfApi, hf_repo: str, local_dir: str, checked_status: str, completed_status: str,
failed_status: str, hf_repo_results: str, local_dir_results: str):
"""Checks if the currently running evals are completed, if yes, update their status on the hub."""
my_snapshot_download(repo_id=hf_repo_results, revision="main", local_dir=local_dir_results, repo_type="dataset", max_workers=60)
running_evals = get_eval_requests([checked_status], hf_repo=hf_repo, local_dir=local_dir)
for eval_request in running_evals:
model = eval_request.model
print("====================================")
print(f"Checking {model}")
output_path = model
output_file = f"{local_dir_results}/{output_path}/results*.json"
output_file_exists = len(glob.glob(output_file)) > 0
if output_file_exists:
print(f"EXISTS output file exists for {model} setting it to {completed_status}")
set_eval_request(api, eval_request, completed_status, hf_repo, local_dir)
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