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 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, ... weight_type: WeightType = WeightType.PyTorch # Default to PyTorch if not specified 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 if task.benchmark in data["results"]: result = data["results"][task.benchmark] metric_value = result.get(task.metric) if metric_value is not None: if isinstance(metric_value, bool): results[task.col_name] = metric_value else: results[task.col_name] = metric_value * 100.0 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) if request_file: with open(request_file, "r") as f: request = json.load(f) self.model_type = ModelType.from_str(request.get("model_type", "")) # Handle weight type conversion from old format weight_type = request.get("weight_type", "PyTorch") if weight_type == "Original": weight_type = "PyTorch" elif weight_type == "Adapter": weight_type = "Other" self.weight_type = WeightType[weight_type] self.license = request.get("license", "?") self.likes = request.get("likes", 0) self.num_params = request.get("params", 0) self.date = request.get("submitted_time", "") else: 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""" data_dict = { "eval_name": self.eval_name, # not a column, just a save name "Precision": self.precision.value.name, "Type": self.model_type.value.name, "T": self.model_type.value.symbol, "Weight Format": self.weight_type.value.name, "Architecture": self.architecture, "Model": make_clickable_model(self.full_model), "Model SHA": self.revision, "Hub License": self.license, "Hub ❤️": self.likes, "#Params (B)": self.num_params, "Available on Hub": self.still_on_hub, } # Add benchmark results data_dict.update(self.results) 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 break 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(eval_result.results) else: eval_results[eval_name] = eval_result results = [] for v in eval_results.values(): # Only include results that have all required benchmark values if all(task.value.col_name in v.results for task in Tasks): results.append(v) return results