import glob import json import math import os from dataclasses import dataclass import dateutil import numpy as np from src.display.formatting import make_clickable_model from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType from src.submission.check_validity import is_model_on_hub @dataclass class EvalResult: """Represents one full evaluation. Built from a combination of the result and request file for a given run. """ eval_name: str # org_model_precision (uid) org: str generative_model: str retrieval_model: str # revision: str # commit hash, "" if main results: dict generative_model_link: str = "" # link to the model on the hub generative_model_args: dict = None retrieval_model_link: str = "" # link to the model on the hub retrieval_model_args: dict = None precision: Precision = Precision.Unknown model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... weight_type: WeightType = WeightType.Original # Original or Adapter architecture: str = "Unknown" license: str = "?" likes: int = 0 date: str = "" # submission date of request file still_on_hub: bool = False @classmethod def init_from_json_file(self, json_filepath): """Inits the result from the specific model result file""" with open(json_filepath) as fp: data = json.load(fp) config = data.get("config") # Precision # precision = Precision.from_str(config.get("model_dtype")) # Get model and org eval_name= config.get("eval_name", "") generative_model = config.get("generative_model", "") retrieval_model = config.get("retrieval_model", "") org= config.get("org", "") # 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.value.name}" # else: # org = org_and_model[0] # model = org_and_model[1] # result_key = f"{org}_{model}_{precision.value.name}" # full_model = "/".join(org_and_model) # still_on_hub, _, model_config = is_model_on_hub( # full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False # ) # if model_config is not None: # architectures = getattr(model_config, "architectures", None) # if architectures: # architecture = ";".join(architectures) # Extract results available in this file (some results are split in several files) results = {} for task in Tasks: task = task.value # We average all scores of a given metric (not all metrics are present in all files) accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k and task.col_name != "hallucination"]) 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 results[task.metric] = data["results"][task.benchmark].get(task.metric, None) if results[task.metric] is not None: results[task.metric] = results[task.metric] * 100.0 generative_model_args = config.get("generative_model_args", None) retrieval_model_args = config.get("retrieval_model_args", None) open_source= True if not generative_model_args or not generative_model_args.get("open_source", False): open_source = False if not retrieval_model_args or not retrieval_model_args.get("open_source", False): open_source = False return self( eval_name=eval_name, # full_model=full_model, org=org, generative_model=generative_model, retrieval_model=retrieval_model, results=results, generative_model_args=generative_model_args, retrieval_model_args=retrieval_model_args, model_type=ModelType.OpenSource if open_source else ModelType.ClosedSource, # precision=precision, # revision= config.get("model_sha", ""), # still_on_hub=still_on_hub, # architecture=architecture ) def update_with_request_file(self, requests_path): """Finds the relevant request file for the current model and updates info with it""" request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name) try: with open(request_file, "r") as f: request = json.load(f) self.model_type = ModelType.from_str(request.get("model_type", "")) self.weight_type = WeightType[request.get("weight_type", "Original")] 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} with precision {self.precision.value.name}") def to_dict(self): """Converts the Eval Result to a dict compatible with our dataframe display""" data_dict = { "eval_name": self.eval_name, # not a column, just a save name, # AutoEvalColumn.precision.name: self.precision.value.name, 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.value.name, # AutoEvalColumn.architecture.name: self.architecture, AutoEvalColumn.generative_model.name: self.generative_model, AutoEvalColumn.retrieval_model.name: self.retrieval_model, AutoEvalColumn.generative_model_link.name: make_clickable_model(self.generative_model, self.generative_model_link), AutoEvalColumn.retrieval_model_link.name: make_clickable_model(self.retrieval_model, self.retrieval_model_link), # AutoEvalColumn.revision.name: self.revision, AutoEvalColumn.ret_average.name: self.results["retrieval"], AutoEvalColumn.gen_average.name: self.results["generation"], # AutoEvalColumn.license.name: self.license, # AutoEvalColumn.likes.name: self.likes, # AutoEvalColumn.generative_model_params.name: self.num_params, # AutoEvalColumn.still_on_hub.name: self.still_on_hub, "Gen#Params (B)": self.generative_model_args.get("num_params", "Unknown"), "Ret#Params (B)": self.retrieval_model_args.get("num_params", "Unknown"), } for task in Tasks: data_dict[task.value.col_name] = self.results[task.value.metric] return data_dict def get_request_file_for_model(requests_path, model_name, precision): """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" request_files = os.path.join( requests_path, 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"] and req_content["precision"] == precision.split(".")[-1] ): request_file = tmp_request_file return request_file def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]: """From the path of the results folder root, extract all needed info for results""" model_result_filepaths = [] print(f"Reading results from {results_path}") 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]] for file in files: model_result_filepaths.append(os.path.join(root, file)) print(f"Adding {file}") eval_results = {} for model_result_filepath in model_result_filepaths: # Creation of result eval_result = EvalResult.init_from_json_file(model_result_filepath) # eval_result.update_with_request_file(requests_path) # 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 import traceback traceback.print_exc() continue return results