src/leaderboard/read_evals.py

import glob
import json
import os
from dataclasses import dataclass
from decimal import Decimal

import dateutil

from src.display.formatting import make_clickable_model
from src.display.utils import AutoEvalColumn, Backend, ModelType, Tasks, Version, WeightType


@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, ...
    precision: str = "Unknown"
    # model_type: str = "Unknown"
    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
    num_few_shots: str = "0"
    add_special_tokens: str = ""
    llm_jp_eval_version: str = ""
    backend: str = ""

    @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")
        metainfo = config.get("metainfo", {})
        model_config = config.get("model", {})

        # Get model type from metainfo
        # model_type_str = metainfo.get("model_type", "")
        # model_type = ModelType.from_str(model_type_str)
        # model_type = metainfo.get("model_type", "Unknown")

        # Get num_few_shots from metainfo
        num_few_shots = str(metainfo.get("num_few_shots", 0))

        # Precision
        # precision = Precision.from_str(config.get("dtype"))
        precision = model_config.get("dtype", "Unknown")

        # Add Special Tokens
        add_special_tokens = str(
            config.get("pipeline_kwargs", {"add_special_tokens": "Unknown"}).get("add_special_tokens")
        )

        version = Version.from_str(metainfo.get("version", "?")).value.name
        backend = Backend.from_str(model_config.get("_target_", "?").split(".")[0]).value.name
        revision = model_config.get("revision", "")

        # Get model and org
        # org_and_model = config.get("model_name", config.get("offline_inference").get("model_name", None))
        org_and_model = config.get("model_name", config.get("offline_inference", {}).get("model_name", "Unknown"))
        org_and_model = org_and_model.split("/", 1)

        # org_and_modelがリストの場合、"/"で結合
        if isinstance(org_and_model, list):
            full_model = "/".join(org_and_model)
        else:
            full_model = org_and_model

        if len(org_and_model) == 1:
            org = None
            model = org_and_model[0]
            # result_key = f"{model}_{precision.value.name}"
            result_key = f"{model}_{precision}_({num_few_shots}shots)_{add_special_tokens}"
        else:
            org = org_and_model[0]
            model = org_and_model[1]
            # result_key = f"{org}_{model}_{precision.value.name}"
            result_key = f"{model}_{precision}_({num_few_shots}shots)_{add_special_tokens}"
        full_model = "/".join(org_and_model)

        if "scores" not in data:
            raise KeyError(f"'scores' key not found in JSON file: {json_filepath}")

        scores = data["scores"]
        results = {}
        for task in Tasks:
            task_value = task.value
            score = scores.get(task_value.metric)
            results[task_value.metric] = score

        return self(
            eval_name=result_key,
            full_model=full_model,
            org=org,
            model=model,
            results=results,
            precision=precision,
            revision=revision,
            num_few_shots=num_few_shots,
            add_special_tokens=add_special_tokens,
            llm_jp_eval_version=version,
            backend=backend,
        )

    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)
        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", "")
            self.architecture = request.get("architecture", "?")
        except Exception:
            print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision}")

    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,
            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: None,
            AutoEvalColumn.license.name: self.license,
            AutoEvalColumn.likes.name: self.likes,
            AutoEvalColumn.params.name: self.num_params,
            AutoEvalColumn.num_few_shots.name: self.num_few_shots,
            AutoEvalColumn.add_special_tokens.name: self.add_special_tokens,
            AutoEvalColumn.llm_jp_eval_version.name: self.llm_jp_eval_version,
            AutoEvalColumn.backend.name: self.backend,
        }

        # for task in Tasks:
        #     task_value = task.value
        #     data_dict[task_value.col_name] = self.results.get(task_value.benchmark, None)
        for task in Tasks:
            task_value = task.value
            value = self.results.get(task_value.metric)
            data_dict[task_value.col_name] = Decimal(value)

        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))

    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
    # print(f"Processing file: {model_result_filepath}")
    # print(f"Eval result: {eval_result.to_dict()}")

    return results