src/backend/run_eval_suite.py

import json
import os
import logging
from datetime import datetime

from lm_eval import tasks, evaluator, utils

from src.envs import RESULTS_REPO, API
from src.backend.manage_requests import EvalRequest

logging.getLogger("openai").setLevel(logging.WARNING)

def run_evaluation(eval_request: EvalRequest, task_names, num_fewshot, batch_size, device, local_dir: str, results_repo: str, no_cache=True, limit=None):
    if limit:
        print(
            "WARNING: --limit SHOULD ONLY BE USED FOR TESTING. REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT."
        )
    task_names = ["medmcqa", "medqa_4options", "mmlu_anatomy", "mmlu_clinical_knowledge", "mmlu_college_biology", "mmlu_college_medicine", "mmlu_medical_genetics", "mmlu_professional_medicine", "pubmedqa"]

    print(f"Selected Tasks: {task_names}")
    results = evaluator.simple_evaluate(
        model="hf-causal-experimental", # "hf-causal"
        model_args=eval_request.get_model_args(),
        tasks=task_names,
        # num_fewshot=num_fewshot,
        batch_size=batch_size,
        device=device,
        no_cache=no_cache,
        limit=limit,
        write_out=True,
        output_base_path="logs"
    )

    results["config"]["model_dtype"] = eval_request.precision
    results["config"]["model_name"] = eval_request.model
    results["config"]["model_sha"] = eval_request.revision

    dumped = json.dumps(results, indent=2)
    print(dumped)

    output_path = os.path.join(local_dir, *eval_request.model.split("/"), f"results_{datetime.now()}.json")
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    with open(output_path, "w") as f:
        f.write(dumped)

    print(evaluator.make_table(results))

    API.upload_file(
        path_or_fileobj=output_path,
        path_in_repo=f"{eval_request.model}/results_{datetime.now()}.json",
        repo_id=results_repo,
        repo_type="dataset",
    )

    return results