main_backend_lighteval.py

import logging
import pprint

from huggingface_hub import snapshot_download


from src.backend.run_eval_suite_lighteval import run_evaluation
from src.backend.manage_requests import (
    check_completed_evals,
    get_eval_requests,
    set_eval_request,
    PENDING_STATUS,
    RUNNING_STATUS,
    FINISHED_STATUS,
    FAILED_STATUS,
)
from src.backend.sort_queue import sort_models_by_priority
from src.envs import (
    QUEUE_REPO,
    EVAL_REQUESTS_PATH_BACKEND,
    RESULTS_REPO,
    EVAL_RESULTS_PATH_BACKEND,
    API,
    LIMIT,
    TOKEN,
    ACCELERATOR,
    VENDOR,
    REGION,
    TASKS_LIGHTEVAL,
)
from src.logging import setup_logger


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

logger = setup_logger(__name__)

# logging.basicConfig(level=logging.ERROR)
pp = pprint.PrettyPrinter(width=80)

snapshot_download(
    repo_id=RESULTS_REPO,
    revision="main",
    local_dir=EVAL_RESULTS_PATH_BACKEND,
    repo_type="dataset",
    max_workers=60,
    token=TOKEN,
)
snapshot_download(
    repo_id=QUEUE_REPO,
    revision="main",
    local_dir=EVAL_REQUESTS_PATH_BACKEND,
    repo_type="dataset",
    max_workers=60,
    token=TOKEN,
)


def run_auto_eval():
    current_pending_status = [PENDING_STATUS]

    # pull the eval dataset from the hub and parse any eval requests
    # check completed evals and set them to finished
    check_completed_evals(
        api=API,
        checked_status=RUNNING_STATUS,
        completed_status=FINISHED_STATUS,
        failed_status=FAILED_STATUS,
        hf_repo=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH_BACKEND,
        hf_repo_results=RESULTS_REPO,
        local_dir_results=EVAL_RESULTS_PATH_BACKEND,
    )

    # Get all eval request that are PENDING, if you want to run other evals, change this parameter
    eval_requests = get_eval_requests(
        job_status=current_pending_status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND
    )
    # Sort the evals by priority (first submitted first run)
    eval_requests = sort_models_by_priority(api=API, models=eval_requests)

    logger.info(f"Found {len(eval_requests)} {','.join(current_pending_status)} eval requests")

    if len(eval_requests) == 0:
        return

    eval_request = eval_requests[0]
    logger.info(pp.pformat(eval_request))

    set_eval_request(
        api=API,
        eval_request=eval_request,
        set_to_status=RUNNING_STATUS,
        hf_repo=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH_BACKEND,
    )

    # This needs to be done
    # instance_size, instance_type = get_instance_for_model(eval_request)
    # For GPU
    # instance_size, instance_type = "small", "g4dn.xlarge"
    # For CPU
    # Updated naming available at https://huggingface.co/docs/inference-endpoints/pricing
    instance_size, instance_type = "x4", "intel-icl"
    logger.info(
        f"Starting Evaluation of {eval_request.json_filepath} on Inference endpoints: {instance_size} {instance_type}"
    )

    run_evaluation(
        eval_request=eval_request,
        task_names=TASKS_LIGHTEVAL,
        local_dir=EVAL_RESULTS_PATH_BACKEND,
        batch_size=1,
        accelerator=ACCELERATOR,
        region=REGION,
        vendor=VENDOR,
        instance_size=instance_size,
        instance_type=instance_type,
        limit=LIMIT,
    )

    logger.info(
        f"Completed Evaluation of {eval_request.json_filepath} on Inference endpoints: {instance_size} {instance_type}"
    )


if __name__ == "__main__":
    run_auto_eval()