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, QuantType, WeightDtype, ComputeDtype @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 quant_type: QuantType = QuantType.Unknown precision: Precision = Precision.Unknown weight_dtype: WeightDtype = WeightDtype.Unknown compute_dtype: ComputeDtype = ComputeDtype.Unknown double_quant: bool = False 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 model_size: int = 0 group_size: int = -1 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 result_file: str = "" @classmethod def init_from_json_file(self, json_filepath): """Inits the result from the specific model result file""" result_file = "/".join(json_filepath.split("/")[2:]) with open(json_filepath) as fp: data = json.load(fp) # We manage the legacy config format config = data.get("config_general") # Precision precision = Precision.from_str(config.get("precision", "4bit")) quant_type = QuantType.from_str(str(config.get("quant_type", "GPTQ"))) weight_dtype = WeightDtype.from_str(data["task_info"].get("weight_dtype", "int4")) compute_dtype = ComputeDtype.from_str(data["task_info"].get("compute_dtype", "bfloat16")) # double_quant = data["quantization_config"].get("bnb_4bit_use_double_quant", False) model_params = config["model_params"] model_size = config["model_size"] # group_size = data["quantization_config"].get("group_size", -1) if data.get("quantization_config", None): double_quant = data["quantization_config"].get("bnb_4bit_use_double_quant", False) group_size = data["quantization_config"].get("group_size", -1) else: double_quant = False group_size = -1 local = config.get("local", False) if not local: local = data["task_info"].get("local", False) # Get model and org org_and_model = config.get("model_name") org_and_model = org_and_model.split("/", 1) if local and org_and_model[0] != "Intel": org_and_model = config.get("model_name").split("/") # temporary "local" org_and_model = ["local", org_and_model[-1]] quant_type = QuantType.autoround 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) # Extract results available in this file (some results are split in several files) results = {} for task in Tasks: task = task.value if task.benchmark == "mmlu": accs = np.array([data["results"]["harness|mmlu|0"][task.metric]]) 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 mean_acc = round(mean_acc, 2) results[task.benchmark] = mean_acc return self( eval_name=result_key, full_model=full_model, org=org, model=model, results=results, precision=precision, quant_type=quant_type, weight_dtype=weight_dtype, compute_dtype=compute_dtype, double_quant=double_quant, revision=config.get("model_sha", "main"), num_params=model_params, model_size=model_size, group_size=group_size, result_file=result_file ) 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.quant_type.value.name, self.precision.value.name, self.weight_dtype.value.name, self.compute_dtype.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.precision = WeightType[request.get("weight_type", "Original")] # self.num_params = request.get("model_size", 0) / 2 # need fix 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.tags = file_dict.get("tags", []) self.flagged = any("flagged" in tag for tag in self.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.precision.name: self.precision.value.name, AutoEvalColumn.quant_type.name: self.quant_type.value.name, AutoEvalColumn.model_type_symbol.name: self.quant_type.value.symbol, AutoEvalColumn.weight_dtype.name: self.weight_dtype.value.name, AutoEvalColumn.compute_dtype.name: self.compute_dtype.value.name, AutoEvalColumn.double_quant.name: self.double_quant, AutoEvalColumn.model_type.name: self.model_type.value.name, AutoEvalColumn.weight_type.name: self.weight_type.value.name, AutoEvalColumn.architecture.name: self.architecture, AutoEvalColumn.model.name: make_clickable_model(self.full_model, self.result_file), 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.model_size.name: self.model_size, AutoEvalColumn.group_size.name: self.group_size, 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, quant_type, precision, weight_dtype, compute_dtype): """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" # {model_path}_eval_request_{private}_{quant_type}_{precision}_{weight_dtype}_{compute_dtype}.json 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] and str(req_content["quant_type"]) == quant_type and req_content["weight_dtype"] == weight_dtype.split(".")[-1] and req_content["compute_dtype"] == compute_dtype.split(".")[-1] ): request_file = tmp_request_file elif ( req_content["status"] in ["Finished"] and req_content["precision"] == precision.split(".")[-1] and quant_type == "AutoRound" and req_content["weight_dtype"] == weight_dtype.split(".")[-1] and req_content["compute_dtype"] == compute_dtype.split(".")[-1] ): 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) 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]) # Hardcoding because of gating problem if "meta-llama" in eval_result.full_model: eval_result.still_on_hub = True # 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