src/leaderboard/read_evals.py

import glob
import json
import math
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, Tasks, Precision, WeightType
from src.submission.check_validity import is_model_on_hub


@dataclass
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)
    org: str 
    generative_model: str
    retrieval_model: str
    # revision: str # commit hash, "" if main
    results: dict
    generative_model_link: str = "" # link to the model on the hub
    generative_model_args: dict = None
    retrieval_model_link: str = "" # link to the model on the hub
    retrieval_model_args: dict = None
    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 = "?"
    likes: int = 0
    date: str = "" # submission date of request file
    still_on_hub: bool = False

    @classmethod
    def init_from_json_file(self, json_filepath):
        """Inits the result from the specific model result file"""
        with open(json_filepath) as fp:
            data = json.load(fp)

        config = data.get("config")

        # Precision
        # precision = Precision.from_str(config.get("model_dtype"))

        # Get model and org
        eval_name= config.get("eval_name", "")
        generative_model = config.get("generative_model", "")
        retrieval_model = config.get("retrieval_model", "")
        org= config.get("org", "")
        # 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, _, model_config = is_model_on_hub(
        #     full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
        # )
        # 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 and task.col_name != "hallucination"])
            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
            results[task.metric] = data["results"][task.benchmark].get(task.metric, None)
            if results[task.metric] is not None:
                results[task.metric] = results[task.metric] * 100.0


        generative_model_args = config.get("generative_model_args", None)
        retrieval_model_args = config.get("retrieval_model_args", None)
        open_source= True
        if not generative_model_args or not generative_model_args.get("open_source", False):
            open_source = False
        if not retrieval_model_args or not retrieval_model_args.get("open_source", False):
            open_source = False

        return self(
            eval_name=eval_name,
            # full_model=full_model,
            org=org,
            generative_model=generative_model,
            retrieval_model=retrieval_model,
            results=results,
            generative_model_args=generative_model_args,
            retrieval_model_args=retrieval_model_args,
            model_type=ModelType.OpenSource if open_source else ModelType.ClosedSource,
            # 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.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"""
        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.generative_model.name: self.generative_model,
            AutoEvalColumn.retrieval_model.name: self.retrieval_model,
            AutoEvalColumn.generative_model_link.name: make_clickable_model(self.generative_model, self.generative_model_link),
            AutoEvalColumn.retrieval_model_link.name: make_clickable_model(self.retrieval_model, self.retrieval_model_link),
            # AutoEvalColumn.revision.name: self.revision,
            AutoEvalColumn.ret_average.name: self.results["retrieval"],
            AutoEvalColumn.gen_average.name: self.results["generation"],
            # AutoEvalColumn.license.name: self.license,
            # AutoEvalColumn.likes.name: self.likes,
            # AutoEvalColumn.generative_model_params.name: self.num_params,
            # AutoEvalColumn.still_on_hub.name: self.still_on_hub,
            "Gen#Params (B)": self.generative_model_args.get("num_params", "Unknown"),
            "Ret#Params (B)": self.retrieval_model_args.get("num_params", "Unknown"),
        }

        for task in Tasks:
            data_dict[task.value.col_name] = self.results[task.value.metric]

        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) -> list[EvalResult]:
    """From the path of the results folder root, extract all needed info for results"""
    model_result_filepaths = []
    print(f"Reading results from {results_path}")
    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(f"Adding {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
            import traceback
            traceback.print_exc()
            continue

    return results