import glob import json import math import os from dataclasses import dataclass from typing import List import traceback 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, Language, WeightType, ORIGINAL_TASKS from src.envs import GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS, SHOW_INCOMPLETE_EVALS @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 results: dict model_sha: str = "" # commit hash revision: str = "main" precision: Precision = Precision.Unknown model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... weight_type: WeightType = WeightType.Original # Original or Adapter main_language: Language = Language.Unknown 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 json_filename: str = None eval_time: float = 0.0 original_benchmark_average: float = None hidden: bool = False # Do not show on the leaderboard num_evals_model_rev: int = 1 @classmethod def init_from_json_file(self, json_filepath, is_original=False): """Inits the result from the specific model result file""" with open(json_filepath) as fp: data = json.load(fp) json_filename = os.path.basename(json_filepath) # We manage the legacy config format config = data.get("config_general") # Precision precision = Precision.from_str(config.get("model_dtype")) num_params = round(config.get("model_num_parameters", 0) / 1_000_000_000, 2) revision = config.get("model_revision", "main") model_sha = config.get("model_sha", "") # Get model and org org_and_model = config.get("model_name") org_and_model = org_and_model.split("/", 1) prefix = f"{precision.value.name}" if revision != "main": prefix = f"{revision}_{prefix}" if len(org_and_model) == 1: org = None model = org_and_model[0] result_key = f"{model}_{prefix}" else: org = org_and_model[0] model = org_and_model[1] result_key = f"{org}_{model}_{prefix}" full_model = "/".join(org_and_model) # Extract results available in this file (some results are split in several files) results = {} tasks = [(task.value.benchmark, task.value.metric) for task in Tasks] if is_original: tasks = ORIGINAL_TASKS for task in tasks: benchmark, metric = task # We skip old mmlu entries wrong_mmlu_version = False if 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 benchmark == "truthfulqa:mc" and "harness|truthfulqa:mc|0" in data["results"]: if math.isnan(float(data["results"]["harness|truthfulqa:mc|0"][metric])): results[benchmark] = 0.0 continue def get_metric(v): res = v.get(metric, None) if res is None: res = v.get(metric + ',all', None) if res is None: res = v.get(metric + ',None', None) if res is None: res = v.get('main_score', None) return res # We average all scores of a given metric (mostly for mmlu) accs = np.array([get_metric(v) for k, v in data["results"].items() if 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[benchmark] = mean_acc return self( eval_name=result_key, full_model=full_model, org=org, model=model, results=results, model_sha=model_sha, revision=revision, precision=precision, json_filename=json_filename, eval_time=config.get("total_evaluation_time_seconds", 0.0), num_params=num_params ) 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, self.revision) 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 = max(request.get("params", 0), self.num_params) self.date = request.get("submitted_time", "") self.architecture = request.get("architectures", "Unknown") self.status = request.get("status", "FAILED") self.hidden = request.get("hidden", False) self.main_language = request.get("main_language", "?") except Exception as e: self.status = "FAILED" print(f"Could not find request file for {self.org}/{self.model}, precision {self.precision.value.name}, revision {self.revision}") 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"] if 'original_llm_scores' in file_dict: if len(file_dict['original_llm_scores']) > 0: if self.precision.value.name in file_dict['original_llm_scores']: self.original_benchmark_average = file_dict['original_llm_scores'][self.precision.value.name] else: self.original_benchmark_average = max(list(file_dict['original_llm_scores'].values())) def to_dict(self): """Converts the Eval Result to a dict compatible with our dataframe display""" average = [] npm = [] for task in Tasks: if task.value.benchmark not in self.results: continue res = self.results[task.value.benchmark] if res is None or np.isnan(res) or not (isinstance(res, float) or isinstance(res, int)): continue average.append(res) npm.append((res-task.value.baseline)*100.0 / (100.0-task.value.baseline)) average = round(sum(average)/len(average), 2) npm = round(sum(npm)/len(npm), 2) 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, self.json_filename, revision=self.revision, precision=self.precision.value.name, num_evals_same_model=self.num_evals_model_rev), 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, AutoEvalColumn.eval_time.name: self.eval_time, AutoEvalColumn.npm.name: npm, AutoEvalColumn.main_language.name: self.main_language } for task in Tasks: if task.value.benchmark in self.results: data_dict[task.value.col_name] = self.results[task.value.benchmark] if GET_ORIGINAL_HF_LEADERBOARD_EVAL_RESULTS: data_dict[AutoEvalColumn.original_benchmark_average.name] = self.original_benchmark_average return data_dict def get_request_file_for_model(requests_path, model_name, precision, revision): """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) if revision is None or revision == "": revision = "main" # 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["revision"] is None or req_content["revision"] == "": req_content["revision"] = "main" if ( req_content["status"] in ["FINISHED", "PENDING_NEW_EVAL" if SHOW_INCOMPLETE_EVALS else "FINISHED"] and req_content["precision"] == precision.split(".")[-1] and req_content["revision"] == revision ): request_file = tmp_request_file return request_file def get_raw_eval_results(results_path: str, requests_path: str, dynamic_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)) with open(dynamic_path) as f: dynamic_data = json.load(f) count_model_rev = {} 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) if eval_result.full_model in dynamic_data: eval_result.update_with_dynamic_file_dict(dynamic_data[eval_result.full_model]) # 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}) eval_results[eval_name].json_filename = eval_result.json_filename else: eval_results[eval_name] = eval_result #count model_revision to display precision if duplicate if eval_result.status in ["FINISHED", "PENDING_NEW_EVAL" if SHOW_INCOMPLETE_EVALS else "FINISHED"] and not eval_result.hidden: model_rev_key = f"{eval_result.full_model}_{eval_result.revision}" if model_rev_key not in count_model_rev: count_model_rev[model_rev_key] = 1 else: count_model_rev[model_rev_key] += 1 results = [] for v in eval_results.values(): try: if v.status in ["FINISHED", "PENDING_NEW_EVAL" if SHOW_INCOMPLETE_EVALS else "FINISHED"] and not v.hidden: model_rev_key = f"{v.full_model}_{v.revision}" v.num_evals_model_rev = count_model_rev[model_rev_key] v.to_dict() # we test if the dict version is complete results.append(v) except KeyError as e: # not all eval values present continue return results