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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # flake8: noqa E501 | |
| import glob | |
| import json | |
| 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, Precision, Tasks, WeightType | |
| from src.submission.check_validity import is_model_on_hub | |
| from src.utils import get_model_name_from_filepath, get_org_and_model_names_from_filepath, get_request_hash | |
| 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) | |
| model_name: 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 = "Unknown" | |
| likes: int = 0 | |
| num_params: int = 0 | |
| date: str = "" # submission date of request file | |
| still_on_hub: bool = False | |
| def init_from_json_file(cls, json_filepath): | |
| """Inits the result from the specific model result file""" | |
| with open(json_filepath) as fp: | |
| data = json.load(fp) | |
| if 'human_eval_solidity_pass_1' not in data['results']: | |
| data['results']['human_eval_solidity_pass_1'] = {'score': 0} | |
| if 'human_eval_solidity_pass_3' not in data['results']: | |
| data['results']['human_eval_solidity_pass_3'] = {'score': 0} | |
| org, model = get_org_and_model_names_from_filepath(json_filepath) | |
| config = data.get("config") | |
| # Precision | |
| precision = Precision.from_str(config.get("model_dtype")) | |
| result_key = f"{org}_{model}_{precision.value.name}" | |
| model_name = get_model_name_from_filepath(json_filepath) | |
| still_on_hub, _, model_config = is_model_on_hub( | |
| model_name, | |
| config.get("model_sha", "main"), | |
| trust_remote_code=True, | |
| test_tokenizer=False, | |
| ) | |
| architecture = "Unknown" | |
| 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 cls( | |
| eval_name=result_key, | |
| model_name=model_name, | |
| 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.model_name, | |
| self.revision, | |
| 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", "Unknown") | |
| self.likes = request.get("likes", 0) | |
| self.num_params = request.get("params", 0) | |
| self.date = request.get("submitted_time", "") | |
| except Exception as error: | |
| print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}") | |
| print(f"Error: {error}") | |
| 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) | |
| scores = { | |
| 'naive_judge': self.results.get('naive_judge', 0), | |
| 'human_eval_solidity_pass_1': self.results.get('human_eval_solidity_pass_1', 0), | |
| 'human_eval_solidity_pass_3': self.results.get('human_eval_solidity_pass_3', 0) | |
| } | |
| soliditybench = 0 | |
| non_zero_scores = {k: v for k, v in scores.items() if v != 0} | |
| if non_zero_scores: | |
| weights = { | |
| 'naive_judge': 0.1, | |
| 'human_eval_solidity_pass_1': 0.5, | |
| 'human_eval_solidity_pass_3': 0.4 | |
| } | |
| total_weight = sum(weights[k] for k in non_zero_scores) | |
| soliditybench = sum(scores[k] * weights[k] / total_weight for k in non_zero_scores) | |
| 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.model_name), | |
| AutoEvalColumn.revision.name: self.revision, | |
| # AutoEvalColumn.average.name: average, | |
| AutoEvalColumn.soliditybench.name: soliditybench, | |
| 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: str, | |
| model_name: str, | |
| revision: str, | |
| precision: str, | |
| ): | |
| request_hash = get_request_hash(model_name, revision, precision) | |
| filepath = os.path.join(requests_path, model_name, '{}.json'.format(request_hash)) | |
| print(f'Loading {filepath}...') | |
| filepath = glob.glob(filepath)[0] | |
| return filepath | |
| 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 | |