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, DisclosedType from src.submission.check_validity import is_model_on_hub import pdb @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) full_model: str # org/model (path on hub) org: str model: str revision: str # commit hash, "" if main results: dict 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 num_params: int = 0 date: str = "" # submission date of request file still_on_hub: bool = False base_model: str = None training_codebase: str = None training_data: str = None @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") additional_info = { "license": config.get("license", None), "num_params": config.get("params", None), "base_model": config.get("base_model", None), "model_type": ModelType.from_str(config.get("model_type", "")), "weight_type": WeightType.from_str(config.get("weight_type", "")), "training_codebase": DisclosedType.from_str(config.get("training_codebase", "")), "training_data": DisclosedType.from_str(config.get("training_data", "")), } # Precision precision = Precision.from_str(config.get("model_dtype")) # Get model and org org_and_model = config.get("model_name", data.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.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 ) architecture = "?" 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) # pdb.set_trace() 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) if task.higher_is_better else 1 - v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k ] ) if accs.size == 0 or any([acc is None for acc in accs]): continue mean_acc = np.mean(accs) if task.scale_by_100: mean_acc *= 100.0 results[task.benchmark] = {"value": mean_acc, "category": task.category} # pdb.set_trace() return self( eval_name=result_key, full_model=full_model, org=org, model=model, results=results, precision=precision, revision=config.get("model_sha", ""), still_on_hub=still_on_hub, architecture=architecture, **additional_info, ) 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""" # compute one average score per category def _get_score_category(category): filtered_scores = [v["value"] for _, v in self.results.items() if v["category"] == category] return sum(filtered_scores) / len(filtered_scores) average_NLU = _get_score_category("NLU") average_CFK = _get_score_category("CFK") average_BFS = _get_score_category("BFS") average = (average_NLU + average_CFK + average_BFS) / 3 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.model.name: make_clickable_model(self.full_model), AutoEvalColumn.average_NLU.name: average_NLU, AutoEvalColumn.average_CFK.name: average_CFK, AutoEvalColumn.average_BFS.name: average_BFS, AutoEvalColumn.average.name: average, AutoEvalColumn.license.name: self.license, AutoEvalColumn.params.name: self.num_params, AutoEvalColumn.revision.name: self.revision, AutoEvalColumn.likes.name: self.likes, AutoEvalColumn.still_on_hub.name: self.still_on_hub, AutoEvalColumn.training_codebase.name: self.training_codebase.value.symbol, AutoEvalColumn.training_data.name: self.training_data.value.symbol, } for task in Tasks: data_dict[task.value.col_name] = self.results[task.value.benchmark] 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 = [] 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)) # Exclude any "samples_* file" model_result_filepaths = [m for m in model_result_filepaths if not os.path.basename(m).startswith("samples_")] 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_table = list() for k, v in eval_results.items(): try: v.to_dict() # we test if the dict version is complete results_for_table.append(v) except RuntimeError as e: # not all eval values present print(f"Issue with results of: ", k) raise e # continue return results_for_table