import glob import json import os from tqdm import tqdm 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 from typing import Optional def is_float(string): try: float(string) return True except ValueError: return False @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 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 = False inference_framework: str = "Unknown" @staticmethod def init_from_json_file(json_filepath, is_backend: bool = False): """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", data.get("config_general", None)) # 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) # Get inference framework inference_framework = config.get("inference_framework", "Unknown") 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, error, 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) # data['results'] is {'nq_open': {'em': 0.24293628808864265, 'em_stderr': 0.007138697341112125}} results = {} for benchmark, benchmark_results in data["results"].items(): if benchmark not in results: results[benchmark] = {} for metric, value in benchmark_results.items(): to_add = True if "_stderr" in metric: to_add = False if "alias" in metric: to_add = False if "," in metric: metric = metric.split(",")[0] metric = metric.replace("exact_match", "em") if to_add is True: multiplier = 100.0 if "GPU" in metric: results[benchmark][metric] = value continue if "precision" in metric: results[benchmark][metric] = value continue if "rouge" in metric and "truthful" not in benchmark: multiplier = 1.0 if "squad" in benchmark: multiplier = 1.0 if "time" in metric: multiplier = 1.0 if "throughput" in metric: multiplier = 1.0 if "batch_" in metric or "Mem" in metric or "Util" in metric: multiplier = 1 # print('RESULTS', data['results']) # print('XXX', benchmark, metric, value, multiplier) results[benchmark][metric] = value * multiplier res = EvalResult( 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, inference_framework=inference_framework, ) return res 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", "") self.inference_framework = request.get("inference_framework", "Unknown") except Exception as e: print(f"Could not find request file for {self.org}/{self.model} -- path: {requests_path} -- {e}") def is_complete(self) -> bool: for task in Tasks: if task.value.benchmark not in self.results: return False return True def to_dict(self): """Converts the Eval Result to a dict compatible with our dataframe display""" # breakpoint() # 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.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.inference_framework.name: self.inference_framework, } for task in Tasks: if task.value.benchmark in self.results: 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 and RUNNING""" 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["precision"] == precision.split(".")[-1]: request_file = tmp_request_file return request_file def get_request_file_for_model_open_llm(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 update_model_type_with_open_llm_request_file(result, open_llm_requests_path): """Finds the relevant request file for the current model and updates info with it""" request_file = get_request_file_for_model_open_llm( open_llm_requests_path, result.full_model, result.precision.value.name ) if request_file: try: with open(request_file, "r") as f: request = json.load(f) open_llm_model_type = request.get("model_type", "Unknown") if open_llm_model_type != "Unknown": result.model_type = ModelType.from_str(open_llm_model_type) except Exception as e: pass return result def get_raw_eval_results(results_path: str, requests_path: str, is_backend: bool = False) -> 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 tqdm(model_result_filepaths, desc="reading model_result_filepaths"): # Creation of result eval_result = EvalResult.init_from_json_file(model_result_filepath, is_backend=is_backend) 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(): results.append(v) return results