import glob import json import math import os import re 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, NShotType from src.submission.check_validity import is_model_on_hub NUM_FEWSHOT = 0 @dataclass class EvalResult: 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 = "?" lang: str = "?" likes: int = 0 num_params: int = 0 date: str = "" # submission date of request file still_on_hub: bool = False n_shot: NShotType = NShotType.n0 org_and_model: str = "" start_date: float = 0 @classmethod def init_from_json_file(self, json_filepath, n_shot_num): """Inits the result from the specific model result file""" with open(json_filepath) as fp: data = json.load(fp) config = data.get("config") n_shot = data.get("n-shot") start_date = data.get("date", 0) # 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)) orig_org_and_model = org_and_model SPICHLERZ_ORG = "speakleash/" if re.match(r"^pretrained=(.*/(plgkwrobel|plggspkl)/)(models/)?", org_and_model): org_and_model = re.sub(r"^pretrained=(.*/(plgkwrobel|plggspkl)/)(models/)?", SPICHLERZ_ORG, org_and_model) org_and_model = org_and_model.replace(",dtype=bfloat16", "") org_and_model = org_and_model.replace(",dtype=float16", "") org_and_model = org_and_model.replace("models/hf_v7_e1", "APT3-1B-Instruct-e1") org_and_model = org_and_model.replace("models/hf_v7_e2", "APT3-1B-Instruct-e2") org_and_model = re.sub(r"^pretrained=", "", org_and_model) org_and_model = org_and_model.replace(",trust_remote_code=True", "") org_and_model = org_and_model.replace(",parallelize=True", "") org_and_model = re.sub(",prefix_token_id=\d+", "", org_and_model) org_and_model = re.sub("/$", "", org_and_model) if org_and_model=='speakleash/mistral_7B-v2/spkl-only-e1_333887a5': org_and_model='speakleash/Bielik-7B-v0.1' elif org_and_model=='speakleash/mistral_7B-v2/spkl-only_sft_v2/e1_base/spkl-only_v10wa_7e6-e2_bbc67e89': org_and_model='speakleash/Bielik-7B-Instruct-v0.1' 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, err, model_config = is_model_on_hub( full_model.split(',')[0], config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False ) if err: print(full_model, err) 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 task_n_shot_num = n_shot_num if 'perplexity' in task.metric or task.benchmark=='polish_eq_bench': # perplexity is the same for 0-shot and 5-shot and is calculated only with 0-shot task_n_shot_num = 0 # 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 and n_shot.get(k, -1) == task_n_shot_num]) if accs.size == 0 or any([acc is None for acc in accs]): continue if 'perplexity' in task.metric or 'eqbench' in task.metric: mean_acc = np.mean(accs) else: mean_acc = np.mean(accs) * 100.0 results[task.benchmark] = (mean_acc, start_date) # 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, n_shot=NShotType.from_str(n_shot_num), org_and_model=orig_org_and_model, start_date=start_date ) def update_with_metadata(self, metadata): # print('UPDATE', self.full_model, self.model, self.eval_name) try: meta = metadata[self.full_model] self.model_type = ModelType.from_str(meta.get("type", "?")) self.num_params = meta.get("params", 0) self.license = meta.get("license", "?") self.lang = meta.get("lang", "?") # TODO desc name except KeyError: print(f"Could not find metadata for {self.full_model}") def update_with_request_file(self, requests_path): """Finds the relevant request file for the current model and updates info with it""" return 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""" g_tasks = [task.value.benchmark for task in Tasks if task.value.type == "generate_until"] mc_tasks = [task.value.benchmark for task in Tasks if task.value.type == "multiple_choice"] rag_tasks = ['polish_polqa_reranking_multiple_choice', 'polish_polqa_open_book'] all_tasks = g_tasks + mc_tasks all_tasks_wo_polqa = [task for task in all_tasks if 'polqa' not in task] baselines = {task.value.benchmark: task.value.baseline*100 for task in Tasks} average_old = sum([v for task, v in self.results.items() if v is not None and task in all_tasks_wo_polqa]) / len(all_tasks_wo_polqa) # average_g = sum([v for task, v in self.results.items() if v is not None and task in g_tasks]) / len(g_tasks) # average_mc = sum([v for task, v in self.results.items() if v is not None and task in mc_tasks]) / len(mc_tasks) # print('XXXXXXXXXXXX') # print(self.eval_name) # print(all_tasks) # print(baselines) # print(self.results) # print('XXXXXXXXXXXX') # average = sum([((v if v is not None else 0)-baselines.get(task,0))/(100-baselines.get(task,0))*100 for task, v in self.results.items() if task in all_tasks]) / len(all_tasks) # average_g = sum([((v if v is not None else 0)-baselines.get(task,0))/(100-baselines.get(task,0))*100 for task, v in self.results.items() if task in g_tasks]) / len(g_tasks) # average_mc = sum([((v if v is not None else 0)-baselines.get(task,0))/(100-baselines.get(task,0))*100 for task, v in self.results.items() if task in mc_tasks]) / len(mc_tasks) average = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in all_tasks]) / len(all_tasks) average_g = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in g_tasks]) / len(g_tasks) average_mc = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in mc_tasks]) / len(mc_tasks) average_rag = sum([(self.results.get(task,0) - baselines.get(task, 0)) / (100 - baselines.get(task, 0)) * 100 for task in rag_tasks]) / len(rag_tasks) data_dict = {} # 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, # } try: data_dict["eval_name"] = self.eval_name except KeyError: print(f"Could not find eval name") try: data_dict[AutoEvalColumn.precision.name] = self.precision.value.name except KeyError: print(f"Could not find precision") except AttributeError: print(f"AttributeError precision") try: data_dict[AutoEvalColumn.model_type.name] = self.model_type.value.name except KeyError: print(f"Could not find model type") try: data_dict[AutoEvalColumn.model_type_symbol.name] = self.model_type.value.symbol except KeyError: print(f"Could not find model type symbol") except AttributeError: print(f"AttributeError model_type") try: data_dict[AutoEvalColumn.weight_type.name] = self.weight_type.value.name except KeyError: print(f"Could not find weight type") try: data_dict[AutoEvalColumn.architecture.name] = self.architecture except KeyError: print(f"Could not find architecture") except AttributeError: print(f"AttributeError architecture") try: data_dict[AutoEvalColumn.model.name] = make_clickable_model( self.full_model) if self.still_on_hub else self.full_model except KeyError: print(f"Could not find model") try: data_dict[AutoEvalColumn.dummy.name] = self.full_model except KeyError: print(f"Could not find dummy") try: data_dict[AutoEvalColumn.revision.name] = self.revision except KeyError: print(f"Could not find revision") except AttributeError: print(f"AttributeError revision") try: data_dict[AutoEvalColumn.average_old.name] = average_old except KeyError: print(f"Could not find average_old") try: data_dict[AutoEvalColumn.average.name] = average except KeyError: print(f"Could not find average") try: data_dict[AutoEvalColumn.average_g.name] = average_g except KeyError: print(f"Could not find average_g") try: data_dict[AutoEvalColumn.average_mc.name] = average_mc except KeyError: print(f"Could not find average_mc") try: data_dict[AutoEvalColumn.average_rag.name] = average_rag except KeyError: print(f"Could not find average_rag") try: data_dict[AutoEvalColumn.license.name] = self.license except KeyError: print(f"Could not find license") except AttributeError: print(f"AttributeError license") try: data_dict[AutoEvalColumn.lang.name] = self.lang except KeyError: print(f"Could not find lang") except AttributeError: print(f"AttributeError lang") try: data_dict[AutoEvalColumn.likes.name] = self.likes except KeyError: print(f"Could not find likes") except AttributeError: print(f"AttributeError likes") try: data_dict[AutoEvalColumn.params.name] = self.num_params except KeyError: print(f"Could not find params") except AttributeError: print(f"AttributeError params") try: data_dict[AutoEvalColumn.still_on_hub.name] = self.still_on_hub except KeyError: print(f"Could not find still on hub") except AttributeError: print(f"AttributeError stillonhub") try: data_dict[AutoEvalColumn.n_shot.name] = self.n_shot.value.name except KeyError: print(f"Could not find still on hub") for task in Tasks: try: data_dict[task.value.col_name] = self.results[task.value.benchmark] except KeyError: print(f"Could not find {task.value.col_name}") data_dict[task.value.col_name] = None 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, metadata) -> 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)) # print('PATHS:', model_result_filepaths) eval_results = {} for n_shot in [0, 5]: for model_result_filepath in model_result_filepaths: # Creation of result eval_result = EvalResult.init_from_json_file(model_result_filepath, n_shot_num=n_shot) eval_result.update_with_request_file(requests_path) # update with metadata eval_result.update_with_metadata(metadata) # Store results of same eval together eval_name = f"{eval_result.eval_name}_{n_shot}-shot" if eval_name in eval_results.keys(): for k, (v, start_date) in eval_result.results.items(): if v is not None: if k in eval_results[eval_name].results: if start_date > eval_results[eval_name].results[k][1]: print( f"Overwriting {eval_name}.results {k} {eval_results[eval_name].results[k]} with {v}: {model_result_filepath} {n_shot} {eval_result.start_date} {eval_results[eval_name].start_date}") eval_results[eval_name].results[k] = (v, start_date) else: print( f"Skipping {eval_name} {eval_result.start_date} {eval_results[eval_name].start_date}: {model_result_filepath} {n_shot}") else: eval_results[eval_name].results[k] = (v, start_date) # eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None}) # TODO: log updated else: eval_results[eval_name] = eval_result for k,v in eval_results.items(): v.results = {k: v for k, (v, start_date) in v.results.items()} results = [] for v in eval_results.values(): try: print(v) v.to_dict() # we test if the dict version is complete # if v.results: results.append(v) except KeyError: # not all eval values present print(f"not all eval values present {v.eval_name} {v.full_model}") continue all_models = [] missing_results_for_task = {} missing_metadata = [] for v in eval_results.values(): r = v.to_dict() for task in Tasks: if r[task.value.col_name] is None: task_name = f"{r['n_shot']}|{task.value.benchmark}" if task_name in missing_results_for_task: missing_results_for_task[task_name].append(f"{v.full_model}|{v.org_and_model}") else: missing_results_for_task[task_name] = [f"{v.full_model}|{v.org_and_model}"] if r[AutoEvalColumn.lang.name] is None or r[AutoEvalColumn.lang.name] == "?": missing_metadata.append(f"{v.full_model}") all_models.append((v.full_model, v.num_params, v.still_on_hub)) # print('missing_results_for_task', missing_results_for_task) for task, models in missing_results_for_task.items(): print(f"Missing results for {task} for {len(models)} models") # print(" ".join(models)) for model in models: print(f'"{model}"') print() print(f"Missing metadata for {len(missing_metadata)} models") for model in missing_metadata: print(model) print() print(f"All models:") for model in all_models: print(model) print() return results