import glob import json import math import os from dataclasses import dataclass import dateutil import numpy as np from huggingface_hub import ModelCard from src.display.formatting import make_clickable_model from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType @dataclass class EvalResult: # Also see src.display.utils.AutoEvalColumn for what will be displayed. 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 weight_precision: Precision = Precision.Unknown activation_precision: Precision = Precision.Unknown model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... weight_type: WeightType = WeightType.Original # Original or Adapter architecture: str = "Unknown" # From config file license: str = "?" likes: int = 0 num_params: int = 0 date: str = "" # submission date of request file still_on_hub: bool = True is_merge: bool = False flagged: bool = False status: str = "FINISHED" tags: list = 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) # We manage the legacy config format config = data.get("config_general") try: model_type = ModelType.from_str(config.get("model_type", "Unknown")) weight_type = WeightType[config.get("weight_type", "Original")] num_params = config.get("params", 0) date = os.path.basename(json_filepath).removesuffix(".json").removeprefix("result_") architecture = config.get("architectures", "Unknown") tags = config.get("model_tag", None) except Exception as e: self.status = "FAILED" print(f"Could not find request file for {self.org}/{self.model}") # Precision weight_precision = Precision.from_str(config.get("weight_precision")) activation_precision = Precision.from_str(config.get("activation_precision")) # Get model and org org_and_model = config.get("model") 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}_W{weight_precision.value.name}A{activation_precision.value.name}" else: org = org_and_model[0] model = org_and_model[1] result_key = f"{org}_{model}_W{weight_precision.value.name}A{activation_precision.value.name}" full_model = "/".join(org_and_model) # 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 # Some truthfulQA values are NaNs if task.benchmark == "truthfulqa_mc2" and "truthfulqa_mc2|0" in data["results"]: if math.isnan(float(data["results"]["truthfulqa_mc2|0"][task.metric])): results[task.benchmark] = 0.0 continue # We average all scores of a given metric (mostly for mmlu) if task.benchmark == "mmlu": accs = np.array([data["results"].get(task.benchmark).get(task.metric, None)]) else: 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, weight_precision=weight_precision, activation_precision=activation_precision, revision=config.get("model_sha", ""), model_type=model_type, weight_type=weight_type, num_params=num_params, date=date, architecture=architecture, tags=tags, ) # 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", "Unknown")) # self.weight_type = WeightType[request.get("weight_type", "Original")] # self.num_params = request.get("params", 0) # self.date = request.get("submitted_time", "") # self.architecture = request.get("architectures", "Unknown") # self.status = request.get("status", "FAILED") # except Exception as e: # self.status = "FAILED" # print(f"Could not find request file for {self.org}/{self.model}") # def update_with_dynamic_file_dict(self, file_dict): # self.license = file_dict.get("license", "?") # self.likes = file_dict.get("likes", 0) # self.still_on_hub = file_dict["still_on_hub"] # self.flagged = any("flagged" in tag for tag in file_dict["tags"]) # self.tags = file_dict["tags"] 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.weight_precision.name: self.weight_precision.value.name, AutoEvalColumn.activation_precision.name: self.activation_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.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, AutoEvalColumn.merged.name: "merge" in self.tags if self.tags else False, AutoEvalColumn.moe.name: ("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower(), AutoEvalColumn.flagged.name: self.flagged, } 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) -> 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("result_")[:-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) # 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: if v.status == "FINISHED": v.to_dict() # we test if the dict version is complete results.append(v) except KeyError: # not all eval values present continue return results