import glob import json import math import os from dataclasses import dataclass import dateutil import numpy as np from decimal import Decimal 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) 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, ... precision: str = "Unknown" # model_type: str = "Unknown" 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 num_few_shots: str = "0" add_special_tokens: str = "" @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") metainfo = config.get("metainfo", {}) model_config = config.get("model", {}) # Get model type from metainfo # model_type_str = metainfo.get("model_type", "") # model_type = ModelType.from_str(model_type_str) # model_type = metainfo.get("model_type", "Unknown") # Get num_few_shots from metainfo num_few_shots = str(metainfo.get("num_few_shots", 0)) # Precision # precision = Precision.from_str(config.get("dtype")) precision = model_config.get("dtype", "Unknown") # Add Special Tokens add_special_tokens = str(config.get("pipeline_kwargs",{"add_special_tokens":"Unknown"}).get("add_special_tokens")) # Get model and org # org_and_model = config.get("model_name", config.get("offline_inference").get("model_name", None)) org_and_model = config.get("model_name", config.get("offline_inference", {}).get("model_name", "Unknown")) org_and_model = org_and_model.split("/", 1) # org_and_modelがリストの場合、"/"で結合 if isinstance(org_and_model, list): full_model = "/".join(org_and_model) else: full_model = org_and_model if len(org_and_model) == 1: org = None model = org_and_model[0] # result_key = f"{model}_{precision.value.name}" result_key = f"{model}_{precision}_({num_few_shots}shots)_{add_special_tokens}" else: org = org_and_model[0] model = org_and_model[1] # result_key = f"{org}_{model}_{precision.value.name}" result_key = f"{model}_{precision}_({num_few_shots}shots)_{add_special_tokens}" 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) if "scores" not in data: raise KeyError(f"'scores' key not found in JSON file: {json_filepath}") scores = data["scores"] results = {} for task in Tasks: task_value = task.value score = scores.get(task_value.metric) results[task_value.metric] = score 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, num_few_shots=num_few_shots, add_special_tokens=add_special_tokens, ) 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) if request_file: with open(request_file, "r") as f: request_data = json.load(f) else: print("No request file found.") 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}") def to_dict(self): """Converts the Eval Result to a dict compatible with our dataframe display""" # 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.value.name, AutoEvalColumn.architecture.name: self.architecture, AutoEvalColumn.model.name: make_clickable_model(self.full_model), AutoEvalColumn.revision.name: self.revision, # AutoEvalColumn.average.name: None, 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, AutoEvalColumn.num_few_shots.name: self.num_few_shots, AutoEvalColumn.add_special_tokens.name: self.add_special_tokens, } # for task in Tasks: # task_value = task.value # data_dict[task_value.col_name] = self.results.get(task_value.benchmark, None) for task in Tasks: task_value = task.value value = self.results.get(task_value.metric) data_dict[task_value.col_name] = Decimal(value) 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)) 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 data_dict = eval_result.to_dict() 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 # print(f"Processing file: {model_result_filepath}") # print(f"Eval result: {eval_result.to_dict()}") return results