import json import os import gradio as gr from datetime import datetime, timezone from dataclasses import dataclass from transformers import AutoConfig 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, VOTES_REPO, VOTES_PATH, ) 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, is_model_on_hub, user_submission_permission, ) from src.voting.vote_system import VoteManager REQUESTED_MODELS = None USERS_TO_SUBMISSION_DATES = None vote_manager = VoteManager(VOTES_PATH, EVAL_REQUESTS_PATH, VOTES_REPO) @dataclass class ModelSizeChecker: model: str precision: str model_size_in_b: float def get_precision_factor(self): if self.precision in ["float16", "bfloat16"]: return 1 elif self.precision == "8bit": return 2 elif self.precision == "4bit": return 4 elif self.precision == "GPTQ": config = AutoConfig.from_pretrained(self.model) num_bits = int(config.quantization_config["bits"]) bits_to_precision_factor = {2: 8, 3: 6, 4: 4, 8: 2} return bits_to_precision_factor.get(num_bits, 1) else: raise Exception(f"Unknown precision {self.precision}.") def can_evaluate(self): precision_factor = self.get_precision_factor() return self.model_size_in_b <= 140 * precision_factor def add_new_eval( model: str, base_model: str, revision: str, precision: str, weight_type: str, model_type: str, use_chat_template: bool, profile: gr.OAuthProfile | None, requested_models: set[str] = None, users_to_submission_dates: dict[str, list[str]] = None, ): # Login required if profile is None: return styled_error("Hub Login Required") # Name of the actual user who sent the request username = profile.username # Initialize the requested_models and users_to_submission_dates variables # If the caller did not provide these values, fetch them from the EVAL_REQUESTS_PATH if requested_models is None or users_to_submission_dates is None: requested_models, users_to_submission_dates = already_submitted_models(EVAL_REQUESTS_PATH) org_or_user = "" model_path = model if "/" in model: org_or_user = 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 org_or_user != "": user_can_submit, error_msg = user_submission_permission( org_or_user, 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.") # Does the model actually exist? if revision == "": revision = "main" try: model_info = API.model_info(repo_id=model, revision=revision) except Exception as e: return styled_error("Could not get your model information. Please fill it up properly.") model_key = f"{model}_{model_info.sha}_{precision}" if model_key in requested_models: return styled_error(f"The model '{model}' with revision '{model_info.sha}' and precision '{precision}' has already been submitted.") # Check model size early model_size, error_text = get_model_size(model_info=model_info, precision=precision, base_model=base_model) if model_size is None: return styled_error(error_text) # First check: Absolute size limit for float16 and bfloat16 if precision in ["float16", "bfloat16"] and model_size > 100: return styled_error(f"Sadly, models larger than 100B parameters cannot be submitted in {precision} precision at this time. " f"Your model size: {model_size:.2f}B parameters.") # Second check: Precision-adjusted size limit for 8bit, 4bit, and GPTQ if precision in ["8bit", "4bit", "GPTQ"]: size_checker = ModelSizeChecker(model=model, precision=precision, model_size_in_b=model_size) if not size_checker.can_evaluate(): precision_factor = size_checker.get_precision_factor() max_size = 140 * precision_factor return styled_error(f"Sadly, models this big ({model_size:.2f}B parameters) cannot be evaluated automatically " f"at the moment on our cluster. The maximum size for {precision} precision is {max_size:.2f}B parameters.") architecture = "?" # 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="main", token=HF_TOKEN, test_tokenizer=True ) if not base_model_on_hub: return styled_error(f'Base model "{base_model}" {error}') if not weight_type == "Adapter": model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=model_info.sha, 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) # Were the model card and license filled? try: 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) # 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, "use_chat_template": use_chat_template, "sender": username } print("Creating eval file") OUT_DIR = f"{EVAL_REQUESTS_PATH}/{org_or_user}" 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") print(eval_entry) 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) # Always add a vote for the submitted model vote_manager.add_vote( selected_model=model, pending_models_df=None, profile=profile ) print(f"Automatically added a vote for {model} submitted by {username}") # Upload votes to the repository vote_manager.upload_votes() return styled_message( "Your request and vote has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list." )