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| #!/usr/bin/env python | |
| import json | |
| import os | |
| import time | |
| from datetime import datetime, timezone | |
| from src.envs import API, EVAL_REQUESTS_PATH, H4_TOKEN, QUEUE_REPO | |
| from src.submission.check_validity import already_submitted_models, get_model_size, is_model_on_hub | |
| from huggingface_hub import snapshot_download | |
| from src.backend.envs import EVAL_REQUESTS_PATH_BACKEND | |
| from src.backend.manage_requests import get_eval_requests | |
| from src.backend.manage_requests import EvalRequest | |
| def add_new_eval(model: str, base_model: str, revision: str, precision: str, private: bool, weight_type: str, model_type: str): | |
| REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH) | |
| user_name = "" | |
| model_path = model | |
| if "/" in model: | |
| tokens = model.split("/") | |
| user_name = tokens[0] | |
| model_path = tokens[1] | |
| precision = precision.split(" ")[0] | |
| current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| if model_type is None or model_type == "": | |
| return print("Please select a model type.") | |
| # Does the model actually exist? | |
| if revision == "": | |
| revision = "main" | |
| # Is the model on the hub? | |
| if weight_type in ["Delta", "Adapter"]: | |
| base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=H4_TOKEN, test_tokenizer=True) | |
| if not base_model_on_hub: | |
| print(f'Base model "{base_model}" {error}') | |
| return | |
| if not weight_type == "Adapter": | |
| model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True) | |
| if not model_on_hub: | |
| print(f'Model "{model}" {error}') | |
| return | |
| # Is the model info correctly filled? | |
| try: | |
| model_info = API.model_info(repo_id=model, revision=revision) | |
| except Exception: | |
| print("Could not get your model information. Please fill it up properly.") | |
| return | |
| model_size = get_model_size(model_info=model_info, precision=precision) | |
| license = 'none' | |
| try: | |
| license = model_info.cardData["license"] | |
| except Exception: | |
| print("Please select a license for your model") | |
| # return | |
| # modelcard_OK, error_msg = check_model_card(model) | |
| # if not modelcard_OK: | |
| # print(error_msg) | |
| # return | |
| # Seems good, creating the eval | |
| print("Adding new eval") | |
| eval_entry = { | |
| "model": model, | |
| "base_model": base_model, | |
| "revision": revision, | |
| "private": private, | |
| "precision": precision, | |
| "weight_type": weight_type, | |
| "status": "PENDING", | |
| "submitted_time": current_time, | |
| "model_type": model_type, | |
| "likes": model_info.likes, | |
| "params": model_size, | |
| "license": license, | |
| } | |
| # Check for duplicate submission | |
| if f"{model}_{revision}_{precision}" in REQUESTED_MODELS: | |
| print("This model has been already submitted.") | |
| return | |
| print("Creating eval file") | |
| OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}" | |
| os.makedirs(OUT_DIR, exist_ok=True) | |
| out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json" | |
| with open(out_path, "w") as f: | |
| f.write(json.dumps(eval_entry)) | |
| print("Uploading eval file") | |
| API.upload_file(path_or_fileobj=out_path, path_in_repo=out_path.split("eval-queue/")[1], | |
| repo_id=QUEUE_REPO, repo_type="dataset", commit_message=f"Add {model} to eval queue") | |
| # Remove the local file | |
| os.remove(out_path) | |
| print("Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list.") | |
| return | |
| def main(): | |
| from huggingface_hub import HfApi | |
| api = HfApi() | |
| model_lst = api.list_models() | |
| model_lst = [m for m in model_lst] | |
| def custom_filter(m) -> bool: | |
| # res = m.pipeline_tag in {'text-generation'} and 'en' in m.tags and m.private is False | |
| # res = m.pipeline_tag in {'text-generation'} and 'en' in m.tags and m.private is False and 'mistralai/' in m.id | |
| res = 'mistralai/' in m.id | |
| return res | |
| filtered_model_lst = sorted([m for m in model_lst if custom_filter(m)], key=lambda m: m.downloads, reverse=True) | |
| snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60) | |
| PENDING_STATUS = "PENDING" | |
| RUNNING_STATUS = "RUNNING" | |
| FINISHED_STATUS = "FINISHED" | |
| FAILED_STATUS = "FAILED" | |
| status = [PENDING_STATUS, RUNNING_STATUS, FINISHED_STATUS, FAILED_STATUS] | |
| # Get all eval requests | |
| eval_requests: list[EvalRequest] = get_eval_requests(job_status=status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND) | |
| requested_model_names = {e.model for e in eval_requests} | |
| # breakpoint() | |
| for i in range(min(200, len(filtered_model_lst))): | |
| model = filtered_model_lst[i] | |
| print(f'Considering {model.id} ..') | |
| is_finetuned = any(tag.startswith('base_model:') for tag in model.tags) | |
| model_type = 'pretrained' | |
| if is_finetuned: | |
| model_type = "fine-tuned" | |
| is_instruction_tuned = 'nstruct' in model.id | |
| if is_instruction_tuned: | |
| model_type = "instruction-tuned" | |
| if model.id not in requested_model_names: | |
| if 'mage' not in model.id: | |
| add_new_eval(model=model.id, base_model='', revision='main', precision='float32', private=False, weight_type='Original', model_type=model_type) | |
| time.sleep(10) | |
| else: | |
| print(f'Model {model.id} already added, not adding it to the queue again.') | |
| if __name__ == "__main__": | |
| main() | |