Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
| import json | |
| import os | |
| from datetime import datetime, timezone | |
| from src.display.formatting import styled_error, styled_message, styled_warning | |
| from src.envs import ( | |
| API, | |
| EVAL_REQUESTS_PATH, | |
| HF_TOKEN, | |
| QUEUE_REPO, | |
| RATE_LIMIT_PERIOD, | |
| RATE_LIMIT_QUOTA, | |
| ) | |
| from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS | |
| from src.submission.check_validity import ( | |
| already_submitted_models, | |
| check_model_card, | |
| get_model_size, | |
| get_model_tags, | |
| is_model_on_hub, | |
| user_submission_permission, | |
| ) | |
| REQUESTED_MODELS = None | |
| USERS_TO_SUBMISSION_DATES = None | |
| def add_new_eval( | |
| model: str, | |
| base_model: str, | |
| revision: str, | |
| precision: str, | |
| weight_type: str, | |
| model_type: str, | |
| ): | |
| global REQUESTED_MODELS | |
| global USERS_TO_SUBMISSION_DATES | |
| if not REQUESTED_MODELS: | |
| REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH) | |
| user_name = "" | |
| model_path = model | |
| if "/" in model: | |
| user_name = model.split("/")[0] | |
| model_path = model.split("/")[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 styled_error("Please select a model type.") | |
| # Is the user rate limited? | |
| if user_name != "": | |
| user_can_submit, error_msg = user_submission_permission( | |
| user_name, USERS_TO_SUBMISSION_DATES, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA | |
| ) | |
| if not user_can_submit: | |
| return styled_error(error_msg) | |
| # Did the model authors forbid its submission to the leaderboard? | |
| if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS: | |
| return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.") | |
| if model == "CohereForAI/c4ai-command-r-plus": | |
| return styled_warning( | |
| "This model cannot be submitted manually on the leaderboard before the transformers release." | |
| ) | |
| # 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=HF_TOKEN, test_tokenizer=True | |
| ) | |
| if not base_model_on_hub: | |
| return styled_error(f'Base model "{base_model}" {error}') | |
| architecture = "?" | |
| downloads = 0 | |
| created_at = "" | |
| if not weight_type == "Adapter": | |
| model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True) | |
| if not model_on_hub or model_config is None: | |
| return styled_error(f'Model "{model}" {error}') | |
| if model_config is not None: | |
| architectures = getattr(model_config, "architectures", None) | |
| if architectures: | |
| architecture = ";".join(architectures) | |
| downloads = getattr(model_config, "downloads", 0) | |
| created_at = getattr(model_config, "created_at", "") | |
| # Is the model info correctly filled? | |
| try: | |
| model_info = API.model_info(repo_id=model, revision=revision) | |
| except Exception: | |
| return styled_error("Could not get your model information. Please fill it up properly.") | |
| model_size = get_model_size(model_info=model_info, precision=precision) | |
| # Were the model card and license filled? | |
| try: | |
| license = model_info.cardData["license"] | |
| except Exception: | |
| return styled_error("Please select a license for your model") | |
| modelcard_OK, error_msg, model_card = check_model_card(model) | |
| if not modelcard_OK: | |
| return styled_error(error_msg) | |
| tags = get_model_tags(model_card, model) | |
| # Seems good, creating the eval | |
| print("Adding new eval") | |
| eval_entry = { | |
| "model": model, | |
| "base_model": base_model, | |
| "revision": model_info.sha, # force to use the exact model commit | |
| "precision": precision, | |
| "params": model_size, | |
| "architectures": architecture, | |
| "weight_type": weight_type, | |
| "status": "PENDING", | |
| "submitted_time": current_time, | |
| "model_type": model_type, | |
| "job_id": -1, | |
| "job_start_time": None, | |
| } | |
| supplementary_info = { | |
| "likes": model_info.likes, | |
| "license": license, | |
| "still_on_hub": True, | |
| "tags": tags, | |
| "downloads": downloads, | |
| "created_at": created_at, | |
| } | |
| # Check for duplicate submission | |
| if f"{model}_{revision}_{precision}" in REQUESTED_MODELS: | |
| return styled_warning("This model has been already submitted.") | |
| 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_False_{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) | |
| return styled_message( | |
| "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." | |
| ) | |