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| import json | |
| from pathlib import Path | |
| from json import JSONDecodeError | |
| import logging | |
| import math | |
| from dataclasses import dataclass, field | |
| from typing import Optional, Dict, List | |
| from tqdm import tqdm | |
| from tqdm.contrib.logging import logging_redirect_tqdm | |
| import numpy as np | |
| from src.display.formatting import make_clickable_model | |
| from src.display.utils import AutoEvalColumn, ModelType, Precision, Tasks, WeightType, parse_datetime | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| 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: Optional[str] | |
| model: str | |
| revision: str # commit hash, "" if main | |
| results: Dict[str, float] | |
| precision: Precision = Precision.Unknown | |
| model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ... | |
| weight_type: WeightType = WeightType.Original | |
| 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 = True | |
| is_merge: bool = False | |
| not_flagged: bool = False | |
| status: str = "FINISHED" | |
| # List of tags, initialized to a new empty list for each instance to avoid the pitfalls of mutable default arguments. | |
| tags: List[str] = field(default_factory=list) | |
| def init_from_json_file(cls, json_filepath: str) -> "EvalResult": | |
| with open(json_filepath, "r") as fp: | |
| data = json.load(fp) | |
| config = data.get("config_general", {}) | |
| precision = Precision.from_str(config.get("model_dtype", "unknown")) | |
| org_and_model = config.get("model_name", "").split("/", 1) | |
| org = org_and_model[0] if len(org_and_model) > 1 else None | |
| model = org_and_model[-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) | |
| results = cls.extract_results(data) # Properly call the method to extract results | |
| return cls( | |
| eval_name=result_key, | |
| full_model=full_model, | |
| org=org, | |
| model=model, | |
| results=results, | |
| precision=precision, | |
| revision=config.get("model_sha", ""), | |
| ) | |
| def extract_results(data: Dict) -> Dict[str, float]: | |
| """ | |
| Extract and process benchmark results from a given dict. | |
| Parameters: | |
| - data (Dict): A dictionary containing benchmark data. This dictionary must | |
| include 'versions' and 'results' keys with respective sub-data. | |
| Returns: | |
| - Dict[str, float]: A dictionary where keys are benchmark names and values | |
| are the processed average scores as percentages. | |
| Notes: | |
| - The method specifically checks for certain benchmark names to skip outdated entries. | |
| - Handles NaN values by setting the corresponding benchmark result to 0.0. | |
| - Averages scores across metrics for benchmarks found in the data, in a percentage format. | |
| """ | |
| results = {} | |
| for task in Tasks: | |
| task = task.value | |
| # We skip old mmlu entries | |
| if task.benchmark == "hendrycksTest": | |
| for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]: | |
| if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0: | |
| continue | |
| # Some benchamrk values are NaNs, mostly truthfulQA | |
| # Would be more optimal (without the whole dict itertion) if benchmark name was same as key in results | |
| # e.g. not harness|truthfulqa:mc|0 but truthfulqa:mc | |
| for k, v in data["results"].items(): | |
| if task.benchmark in k: | |
| if math.isnan(float(v[task.metric])): | |
| results[task.benchmark] = 0.0 | |
| continue | |
| # We average all scores of a given metric (mostly for mmlu) | |
| 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 | |
| results[task.benchmark] = mean_acc | |
| return results | |
| def update_with_request_file(self, requests_path): | |
| """Finds the relevant request file for the current model and updates info with it.""" | |
| try: | |
| request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name) | |
| if request_file is None: | |
| logging.warning(f"No request file for {self.org}/{self.model}") | |
| self.status = "FAILED" | |
| return | |
| with open(request_file, "r") as f: | |
| request = json.load(f) | |
| self.model_type = ModelType.from_str(request.get("model_type", "Unknown")) | |
| self.weight_type = WeightType[request.get("weight_type", "Original")] | |
| self.num_params = int(request.get("params", 0)) # Ensuring type safety | |
| self.date = request.get("submitted_time", "") | |
| self.architecture = request.get("architectures", "Unknown") | |
| self.status = request.get("status", "FAILED") | |
| except FileNotFoundError: | |
| self.status = "FAILED" | |
| logging.error(f"Request file: {request_file} not found for {self.org}/{self.model}") | |
| except JSONDecodeError: | |
| self.status = "FAILED" | |
| logging.error(f"Error decoding JSON from the request file for {self.org}/{self.model}") | |
| except KeyError as e: | |
| self.status = "FAILED" | |
| logging.error(f"Key error {e} in processing request file for {self.org}/{self.model}") | |
| except Exception as e: # Catch-all for any other unexpected exceptions | |
| self.status = "FAILED" | |
| logging.error(f"Unexpected error {e} for {self.org}/{self.model}") | |
| def update_with_dynamic_file_dict(self, file_dict): | |
| """Update object attributes based on the provided dictionary, with error handling for missing keys and type validation.""" | |
| # Default values set for optional or potentially missing keys. | |
| self.license = file_dict.get("license", "?") | |
| self.likes = int(file_dict.get("likes", 0)) # Ensure likes is treated as an integer | |
| self.still_on_hub = file_dict.get("still_on_hub", False) # Default to False if key is missing | |
| self.tags = file_dict.get("tags", []) | |
| # Calculate `flagged` only if 'tags' is not empty and avoid calculating each time | |
| self.not_flagged = not (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.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.fullname.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.merged.name: not ("merge" in self.tags if self.tags else False), | |
| AutoEvalColumn.moe.name: not ( | |
| ("moe" in self.tags if self.tags else False) or "moe" in self.full_model.lower() | |
| ), | |
| AutoEvalColumn.not_flagged.name: self.not_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, precision): | |
| """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED""" | |
| requests_path = Path(requests_path) | |
| pattern = f"{model_name}_eval_request_*.json" | |
| # Using pathlib to find files matching the pattern | |
| request_files = list(requests_path.glob(pattern)) | |
| # Sort the files by name in descending order to mimic 'reverse=True' | |
| request_files.sort(reverse=True) | |
| # Select the correct request file based on 'status' and 'precision' | |
| request_file = None | |
| for request_file in request_files: | |
| with request_file.open("r") as f: | |
| req_content = json.load(f) | |
| if req_content["status"] == "FINISHED" and req_content["precision"] == precision.split(".")[-1]: | |
| request_file = str(request_file) | |
| # Return empty string if no file found that matches criteria | |
| 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""" | |
| with open(dynamic_path) as f: | |
| dynamic_data = json.load(f) | |
| results_path = Path(results_path) | |
| model_files = list(results_path.rglob("results_*.json")) | |
| model_files.sort(key=lambda file: parse_datetime(file.stem.removeprefix("results_"))) | |
| eval_results = {} | |
| # Wrap model_files iteration with tqdm for progress display | |
| for model_result_filepath in tqdm(model_files, desc="Processing model files"): | |
| # Creation of result | |
| eval_result = EvalResult.init_from_json_file(model_result_filepath) | |
| with logging_redirect_tqdm(): | |
| 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 any([org in eval_result.full_model for org in ["meta-llama/", "google/", "tiiuae/"]]): | |
| 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 k, v in eval_results.items(): | |
| try: | |
| if v.status == "FINISHED": | |
| v.to_dict() # we test if the dict version is complete | |
| results.append(v) | |
| except KeyError as e: | |
| logging.error(f"Error while checking model {k} {v.date} json, no key: {e}") # not all eval values present | |
| continue | |
| return results | |