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, Domains from src.submission.check_validity import is_model_on_hub @dataclass class RankResult: """Represents one the overall ranking table """ eval_name: str full_model: str org: str model: str results: dict license: str = "?" knowledge_cutoff: str = "" @classmethod def init_from_json_dict(self, data): config = data.get("config") # Get model and org model = config.get("model_name") org = config.get("organization") license = config.get("license") knowledge_cutoff = config.get("knowledge_cutoff") model_results = data.get("results") # Extract results available in this file (some results are split in several files) results = {} for domain in Domains: domain = domain.value results[domain.dimension] = model_results.get(domain.dimension).get(domain.metric, None) return self( eval_name=f"{org}_{model}", full_model=f"{org}/{model}", org=org, model=model, results=results, license=license, knowledge_cutoff=knowledge_cutoff ) def to_dict(self): """Converts the Eval Result to a dict compatible with our dataframe display""" # score = 1 / self.results[Domains.dim0.dimension] if self.results[Domains.dim0.dimension] != 0 else 0 # 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.model.name: make_clickable_model(self.full_model), AutoEvalColumn.rank.name: None, # placeholder for the rank AutoEvalColumn.model.name: self.model, AutoEvalColumn.score.name: self.results[Domains.dim0.value.dimension], AutoEvalColumn.score_sd.name: None, # placeholder for the score sd AutoEvalColumn.license.name: self.license, AutoEvalColumn.organization.name: self.org, AutoEvalColumn.knowledge_cutoff.name: self.knowledge_cutoff, # AutoEvalColumn.precision.name: self.precision.value.name, # AutoEvalColumn.model_type.name: self.model_type.value.name, # AutoEvalColumn.model_type_symbol.name # AutoEvalColumn.weight_type.name: self.weight_type.value.name, # AutoEvalColumn.architecture.name: self.architecture, # AutoEvalColumn.revision.name: self.revision, # AutoEvalColumn.average.name: average, # AutoEvalColumn.likes.name: self.likes, # AutoEvalColumn.params.name: self.num_params, # AutoEvalColumn.still_on_hub.name: self.still_on_hub, } @dataclass class ModelResult: """Represents one full evaluation. Built from a combination of the result and request file for a given run. """ eval_name: str full_model: str org: str model: str results: dict license: str = "?" knowledge_cutoff: str = "" @classmethod def init_from_json_dict(self, data): config = data.get("config") # Get model and org model = config.get("model_name") org = config.get("organization") license = config.get("license") knowledge_cutoff = config.get("knowledge_cutoff") model_results = data.get("results") new_results = {} for k, v in model_results.items(): new_v = {} for kk, vv in v.items(): if vv == 'N/A': new_v[kk] = None else: new_v[kk] = vv new_results[k] = new_v # Extract results available in this file (some results are split in several files) # results = {} # for domain in Domains: # domain = domain.value # results[domain.dimension] = model_results.get(domain.dimension).get(domain.metric, None) return self( eval_name=f"{org}_{model}", full_model=f"{org}/{model}", org=org, model=model, results=new_results, license=license, knowledge_cutoff=knowledge_cutoff ) 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.model.name: make_clickable_model(self.full_model), # AutoEvalColumn.rank.name: None, # placeholder for the rank AutoEvalColumn.model.name: self.model, # AutoEvalColumn.score.name: self.results[Domains.dim0.value.dimension], # AutoEvalColumn.score_sd.name: None, # placeholder for the score sd # AutoEvalColumn.score_overall.name: float(self.results.get("OVERALL").get("Average Score", None)), # AutoEvalColumn.score_math_algebra.name: float(self.results.get("Algebra").get("Average Score", None)), # AutoEvalColumn.score_math_geometry.name: float(self.results.get("Geometry").get("Average Score", None)), # AutoEvalColumn.score_math_probability.name: float(self.results.get("Probability").get("Average Score", None)), # AutoEvalColumn.score_reason_logical.name: float(self.results.get("Logical").get("Average Score", None)), # AutoEvalColumn.score_reason_social.name: float(self.results.get("Social").get("Average Score", None)), # AutoEvalColumn.sd_overall.name: float(self.results.get("OVERALL").get("Standard Deviation", None)), # AutoEvalColumn.sd_math_algebra.name: float(self.results.get("Algebra").get("Standard Deviation", None)), # AutoEvalColumn.sd_math_geometry.name: float(self.results.get("Geometry").get("Standard Deviation", None)), # AutoEvalColumn.sd_math_probability.name: float(self.results.get("Probability").get("Standard Deviation", None)), # AutoEvalColumn.sd_reason_logical.name: float(self.results.get("Logical").get("Standard Deviation", None)), # AutoEvalColumn.sd_reason_social.name: float(self.results.get("Social").get("Standard Deviation", None)), # AutoEvalColumn.rank_overall.name: int(self.results.get("OVERALL").get("Rank", None)), # AutoEvalColumn.rank_math_algebra.name: int(self.results.get("Algebra").get("Rank", None)), # AutoEvalColumn.rank_math_geometry.name: int(self.results.get("Geometry").get("Rank", None)), # AutoEvalColumn.rank_math_probability.name: int(self.results.get("Probability").get("Rank", None)), # AutoEvalColumn.rank_reason_logical.name: int(self.results.get("Logical").get("Rank", None)), # AutoEvalColumn.rank_reason_social.name: int(self.results.get("Social").get("Rank", None)), AutoEvalColumn.score_overall.name: self.results.get("OVERALL").get("Average Score", None) if self.results.get("OVERALL") else None, AutoEvalColumn.score_math_algebra.name: self.results.get("Algebra").get("Average Score", None) if self.results.get("Algebra") else None, AutoEvalColumn.score_math_geometry.name: self.results.get("Geometry").get("Average Score", None) if self.results.get("Geometry") else None, AutoEvalColumn.score_math_probability.name: self.results.get("Probability").get("Average Score", None) if self.results.get("Probability") else None, AutoEvalColumn.score_reason_logical.name: self.results.get("Logical").get("Average Score", None) if self.results.get("Logical") else None, AutoEvalColumn.score_reason_social.name: self.results.get("Social").get("Average Score", None) if self.results.get("Social") else None, AutoEvalColumn.sd_overall.name: self.results.get("OVERALL").get("Standard Deviation", None) if self.results.get("OVERALL") else None, AutoEvalColumn.sd_math_algebra.name: self.results.get("Algebra").get("Standard Deviation", None) if self.results.get("Algebra") else None, AutoEvalColumn.sd_math_geometry.name: self.results.get("Geometry").get("Standard Deviation", None) if self.results.get("Geometry") else None, AutoEvalColumn.sd_math_probability.name: self.results.get("Probability").get("Standard Deviation", None) if self.results.get("Probability") else None, AutoEvalColumn.sd_reason_logical.name: self.results.get("Logical").get("Standard Deviation", None) if self.results.get("Logical") else None, AutoEvalColumn.sd_reason_social.name: self.results.get("Social").get("Standard Deviation", None) if self.results.get("Social") else None, AutoEvalColumn.rank_overall.name: self.results.get("OVERALL").get("Rank", None) if self.results.get("OVERALL") else None, AutoEvalColumn.rank_math_algebra.name: self.results.get("Algebra").get("Rank", None) if self.results.get("Algebra") else None, AutoEvalColumn.rank_math_geometry.name: self.results.get("Geometry").get("Rank", None) if self.results.get("Geometry") else None, AutoEvalColumn.rank_math_probability.name: self.results.get("Probability").get("Rank", None) if self.results.get("Probability") else None, AutoEvalColumn.rank_reason_logical.name: self.results.get("Logical").get("Rank", None) if self.results.get("Logical") else None, AutoEvalColumn.rank_reason_social.name: self.results.get("Social").get("Rank", None) if self.results.get("Social") else None, AutoEvalColumn.score_chemistry.name: self.results.get("Chemistry").get("Average Score", None) if self.results.get("Chemistry") else None, AutoEvalColumn.sd_chemistry.name: self.results.get("Chemistry").get("Standard Deviation", None) if self.results.get("Chemistry") else None, AutoEvalColumn.rank_chemistry.name: self.results.get("Chemistry").get("Rank", None) if self.results.get("Chemistry") else None, AutoEvalColumn.score_cpp.name: self.results.get("CPP").get("Average Score", None) if self.results.get("CPP") else None, AutoEvalColumn.sd_cpp.name: self.results.get("CPP").get("Standard Deviation", None) if self.results.get("CPP") else None, AutoEvalColumn.rank_cpp.name: self.results.get("CPP").get("Rank", None) if self.results.get("CPP") else None, AutoEvalColumn.license.name: self.license, AutoEvalColumn.organization.name: self.org, AutoEvalColumn.knowledge_cutoff.name: self.knowledge_cutoff, } # for task in Tasks: # data_dict[task.value.col_name] = self.results[task.value.benchmark] # for domain in Domains: # data_dict[domain.value.col_name] = self.results[domain.value.dimension] return data_dict @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 @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 org_and_model = config.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) 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]) 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, 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""" average = sum([v for v in self.results.values() if v is not None]) / len(Tasks) # print(AutoEvalColumn.precision.name, self.precision.value.name) 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.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, } 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)) 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 continue return results def get_raw_model_results(results_path: str) -> list[EvalResult]: """From the path of the results folder root, extract all needed info for results""" try: with open(results_path) as fp: data = json.load(fp) except: data = eval(open(results_path).read()) # a list of dicts # print("data", len(data)) # print(data[0]) # {'config': {'model_name': 'ChatGPT-4o-latest (2024-09-03)', # 'organization': 'OpenAI', 'license': 'Proprietary', # 'knowledge_cutoff': '2023/10'}, # 'results': {'math-algebra': # {'Score': 99.19484702, 'Avg Rank': 1.666666667, 'Min Rank': 1, 'Max Rank': 3}, # 'math-probability': {'Score': 100, 'Avg Rank': 1, 'Min Rank': 1, 'Max Rank': 1}, # 'reasoning-logical': {'Avg Rank': 1, 'Min Rank': 1, 'Max Rank': 1}, # 'overall': {'Avg Rank': 2, 'Min Rank': 2, 'Max Rank': 2}}} eval_results = {} for result in data: # Creation of result eval_result = ModelResult.init_from_json_dict(result) # print(eval_result) # ModelResult(eval_name='OpenAI_ChatGPT-4o-latest (2024-09-03)', # full_model='OpenAI/ChatGPT-4o-latest (2024-09-03)', # org='OpenAI', model='ChatGPT-4o-latest (2024-09-03)', # results={'overall': None}, license='Proprietary', knowledge_cutoff='2023/10') # all_num_results = eval_result.results # def get_terminal_values(data): # terminal_values = [] # for key, value in data.items(): # if isinstance(value, dict): # terminal_values.extend(get_terminal_values(value)) # else: # terminal_values.append(value) # return terminal_values # all_values = get_terminal_values(all_num_results) # if 'N/A' in all_values: # continue eval_name = eval_result.eval_name eval_results[eval_name] = eval_result # # Store results of same eval together # 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(): # print(v.to_dict()) # exit() # {'eval_name': 'OpenAI_ChatGPT-4o-latest (2024-09-03)', # 'Model': 'OpenAI/ChatGPT-4o-latest (2024-09-03)', # 'Hub License': 'Proprietary', 'Organization': 'OpenAI', 'Knowledge cutoff': '2023/10', 'Overall': None} try: v.to_dict() # we test if the dict version is complete results.append(v) except KeyError: # not all eval values present continue return results