import glob import json import math import os from dataclasses import dataclass import dateutil from datetime import datetime import numpy as np from src.display.formatting import make_clickable_model from src.display.utils import AutoEvalColumn, ModelType, Tasks from src.submission.check_validity import is_model_on_hub @dataclass class EvalResult: eval_name: str full_model: str org: str model: str revision: str results: dict precision: str = "" model_type: ModelType = ModelType.Unknown weight_type: str = "Original" architecture: str = "Unknown" license: str = "?" likes: int = 0 num_params: int = 0 date: str = "" still_on_hub: bool = False @classmethod def init_from_json_file(self, json_filepath): with open(json_filepath) as fp: data = json.load(fp) # We manage the legacy config format config = data.get("config", data.get("config_general", None)) # Precision precision = config.get("model_dtype") if precision == "None": precision = "GPTQ" # Get model and org org_and_model = config.get("model_name", config.get("model_args", None)) org_and_model = org_and_model.split("/", 1) if len(org_and_model) == 1: org = None model = org_and_model[0] result_key = f"{model}_{precision}" else: org = org_and_model[0] model = org_and_model[1] result_key = f"{org}_{model}_{precision}" full_model = "/".join(org_and_model) still_on_hub, error = is_model_on_hub( full_model, config.get("model_sha", "main"), trust_remote_code=True ) # Extract results available in this file (some results are split in several files) results = {} for task in Tasks: task = task.value # We skip old mmlu entries wrong_mmlu_version = False if task.benchmark == "hendrycksTest": for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]: if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0: wrong_mmlu_version = True if wrong_mmlu_version: continue # Some truthfulQA values are NaNs if task.benchmark == "truthfulqa:mc" and "harness|truthfulqa:mc|0" in data["results"]: if math.isnan(float(data["results"]["harness|truthfulqa:mc|0"][task.metric])): results[task.benchmark] = 0.0 continue # We average all scores of a given metric (mostly for mmlu) accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark in k]) if accs.size == 0 or any([acc is None for acc in accs]): continue mean_acc = np.mean(accs) * 100.0 results[task.benchmark] = mean_acc return self( eval_name=result_key, full_model=full_model, org=org, model=model, results=results, precision=precision, # todo model_type=, weight_type= revision=config.get("model_sha", ""), still_on_hub=still_on_hub, ) def update_with_request_file(self): request_file = get_request_file_for_model(self.full_model, self.precision) try: with open(request_file, "r") as f: request = json.load(f) self.model_type = ModelType.from_str(request.get("model_type", "")) self.license = request.get("license", "?") self.likes = request.get("likes", 0) self.num_params = request.get("params", 0) self.date = request.get("submitted_time", "") except Exception: print(f"Could not find request file for {self.org}/{self.model}") def to_dict(self): average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) data_dict = { "eval_name": self.eval_name, # not a column, just a save name, AutoEvalColumn.precision.name: self.precision, AutoEvalColumn.model_type.name: self.model_type.value.name, AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol, AutoEvalColumn.weight_type.name: self.weight_type, AutoEvalColumn.model.name: make_clickable_model(self.full_model), AutoEvalColumn.dummy.name: self.full_model, AutoEvalColumn.revision.name: self.revision, AutoEvalColumn.average.name: average, AutoEvalColumn.license.name: self.license, AutoEvalColumn.likes.name: self.likes, AutoEvalColumn.params.name: self.num_params, AutoEvalColumn.still_on_hub.name: self.still_on_hub, } for task in Tasks: data_dict[task.value.col_name] = self.results[task.value.benchmark] return data_dict def get_request_file_for_model(model_name, precision): request_files = os.path.join( "eval-queue", f"{model_name}_eval_request_*.json", ) request_files = glob.glob(request_files) # Select correct request file (precision) request_file = "" request_files = sorted(request_files, reverse=True) for tmp_request_file in request_files: with open(tmp_request_file, "r") as f: req_content = json.load(f) if ( req_content["status"] in ["FINISHED", "PENDING_NEW_EVAL"] and req_content["precision"] == precision.split(".")[-1] ): request_file = tmp_request_file return request_file def get_raw_eval_results(results_path: str) -> list[EvalResult]: json_filepaths = [] for root, _, files in os.walk(results_path): # We should only have json files in model results if len(files) == 0 or any([not f.endswith(".json") for f in files]): continue # Sort the files by date try: files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7]) except dateutil.parser._parser.ParserError: files = [files[-1]] # up_to_date = files[-1] for file in files: json_filepaths.append(os.path.join(root, file)) eval_results = {} for json_filepath in json_filepaths: # Creation of result eval_result = EvalResult.init_from_json_file(json_filepath) eval_result.update_with_request_file() # 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 results = [] for v in eval_results.values(): try: v.to_dict() # we test if the dict version is complete results.append(v) except KeyError: # not all eval values present continue return results