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, DisclosedType
from src.submission.check_validity import is_model_on_hub

import pdb


@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)
    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 = "?"
    likes: int = 0
    num_params: int = 0
    date: str = ""  # submission date of request file
    still_on_hub: bool = False
    base_model: str = None
    training_codebase: str = None
    training_data: str = None

    @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")

        additional_info = {
            "license": config.get("license", None),
            "num_params": config.get("params", None),
            "base_model": config.get("base_model", None),
            "model_type": ModelType.from_str(config.get("model_type", "")),
            "weight_type": WeightType.from_str(config.get("weight_type", "")),
            "training_codebase": DisclosedType.from_str(config.get("training_codebase", "")),
            "training_data": DisclosedType.from_str(config.get("training_data", "")),
        }

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

        # Get model and org
        org_and_model = config.get("model_name", data.get("model_name", config.get("model_args", None)))
        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
        )
        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)

        # pdb.set_trace()
        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) if task.higher_is_better else 1 - 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)
            if task.scale_by_100:
                mean_acc *= 100.0

            results[task.benchmark] = {"value": mean_acc, "category": task.category}

        # pdb.set_trace()

        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,
            **additional_info,
        )

    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"""

        # compute one average score per category
        def _get_score_category(category):
            filtered_scores = [v["value"] for _, v in self.results.items() if v["category"] == category]
            return sum(filtered_scores) / len(filtered_scores)

        average_NLU = _get_score_category("NLU")
        average_CFK = _get_score_category("CFK")
        average_BFS = _get_score_category("BFS")
        average = (average_NLU + average_CFK + average_BFS) / 3

        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.average_NLU.name: average_NLU,
            AutoEvalColumn.average_CFK.name: average_CFK,
            AutoEvalColumn.average_BFS.name: average_BFS,
            AutoEvalColumn.average.name: average,
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.params.name: self.num_params,
            AutoEvalColumn.revision.name: self.revision,
            AutoEvalColumn.likes.name: self.likes,
            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
            AutoEvalColumn.training_codebase.name: self.training_codebase.value.symbol,
            AutoEvalColumn.training_data.name: self.training_data.value.symbol,
        }

        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"""
    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 = []

    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))

    # Exclude any "samples_* file"
    model_result_filepaths = [m for m in model_result_filepaths if not os.path.basename(m).startswith("samples_")]

    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_table = list()
    for k, v in eval_results.items():
        try:
            v.to_dict()  # we test if the dict version is complete
            results_for_table.append(v)
        except RuntimeError as e:  # not all eval values present
            print(f"Issue with results of: ", k)
            raise e
            # continue

    return results_for_table