backend-cli.py

#!/usr/bin/env python

import os
import json
import argparse

import socket
import random
from datetime import datetime

from src.backend.run_eval_suite import run_evaluation
from src.backend.manage_requests import check_completed_evals, get_eval_requests, set_eval_request
from src.backend.sort_queue import sort_models_by_priority
from src.backend.envs import Tasks, EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PATH_BACKEND, DEVICE, LIMIT, Task
LIMIT=2
from src.backend.manage_requests import EvalRequest
from src.leaderboard.read_evals import EvalResult

from src.envs import QUEUE_REPO, RESULTS_REPO, API
from src.utils import my_snapshot_download

from src.leaderboard.read_evals import get_raw_eval_results

from typing import Optional

import time

import pprint
import logging


# Configure the root logger
logging.basicConfig(
    format="%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d:%H:%M:%S",
    level=logging.WARNING,
)

# Get the 'lm-eval' logger from the third-party library
eval_logger = logging.getLogger("lm-eval")

# Explicitly set the level for 'lm-eval' logger to WARNING
eval_logger.setLevel(logging.WARNING)


def my_set_eval_request(api, eval_request, set_to_status, hf_repo, local_dir):
    for i in range(10):
        try:
            set_eval_request(
                api=api, eval_request=eval_request, set_to_status=set_to_status, hf_repo=hf_repo, local_dir=local_dir
            )
            return
        except Exception as e:
            print(f"Error setting eval request to {set_to_status}: {e}. Retrying in 60 seconds")
            time.sleep(60)
    return


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

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

PENDING_STATUS = "PENDING"
RUNNING_STATUS = "RUNNING"
FINISHED_STATUS = "FINISHED"
FAILED_STATUS = "FAILED"

TASKS_HARNESS = [task.value for task in Tasks]


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


def sanity_checks():
    print(f"Device: {DEVICE}")

    # pull the eval dataset from the hub and parse any eval requests
    # check completed evals and set them to finished
    my_snapshot_download(
        repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60
    )
    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,
    )
    return


def request_to_result_name(request: EvalRequest) -> str:
    # Request: EvalRequest(model='meta-llama/Llama-2-13b-hf', private=False, status='FINISHED',
    # json_filepath='./eval-queue-bk/meta-llama/Llama-2-13b-hf_eval_request_False_False_False.json',
    # weight_type='Original', model_type='pretrained', precision='float32', base_model='', revision='main',
    # submitted_time='2023-09-09T10:52:17Z', likes=389, params=13.016, license='?')
    #
    # EvalResult(eval_name='meta-llama_Llama-2-13b-hf_float32', full_model='meta-llama/Llama-2-13b-hf',
    # org='meta-llama', model='Llama-2-13b-hf', revision='main',
    # results={'nq_open': 33.739612188365655, 'triviaqa': 74.12505572893447},
    # precision=<Precision.float32: ModelDetails(name='float32', symbol='')>,
    # model_type=<ModelType.PT: ModelDetails(name='pretrained', symbol='🟢')>,
    # weight_type=<WeightType.Original: ModelDetails(name='Original', symbol='')>,
    # architecture='LlamaForCausalLM', license='?', likes=389, num_params=13.016, date='2023-09-09T10:52:17Z', still_on_hub=True)
    #
    org_and_model = request.model.split("/", 1)
    if len(org_and_model) == 1:
        model = org_and_model[0]
        res = f"{model}_{request.precision}"
    else:
        org = org_and_model[0]
        model = org_and_model[1]
        res = f"{org}_{model}_{request.precision}"
    return res


def process_evaluation(task: Task, eval_request: EvalRequest) -> dict:
    batch_size = 1
    try:
        results = run_evaluation(
            eval_request=eval_request,
            task_names=[task.benchmark],
            num_fewshot=task.num_fewshot,
            batch_size=batch_size,
            device=DEVICE,
            use_cache=None,
            limit=LIMIT,
        )
    except RuntimeError as e:
        if "No executable batch size found" in str(e):
            batch_size = 1
            results = run_evaluation(
                eval_request=eval_request,
                task_names=[task.benchmark],
                num_fewshot=task.num_fewshot,
                batch_size=batch_size,
                device=DEVICE,
                use_cache=None,
                limit=LIMIT,
            )
        else:
            raise

    print("RESULTS", results)

    dumped = json.dumps(results, indent=2, default=lambda o: "<not serializable>")
    print(dumped)

    output_path = os.path.join(
        EVAL_RESULTS_PATH_BACKEND, *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)

    my_snapshot_download(
        repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60
    )
    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


def process_finished_requests(thr: int, hard_task_lst: Optional[list[str]] = None) -> bool:
    sanity_checks()

    current_finished_status = [FINISHED_STATUS, FAILED_STATUS]

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

    random.shuffle(eval_requests)

    eval_results: list[EvalResult] = get_raw_eval_results(EVAL_RESULTS_PATH_BACKEND, EVAL_REQUESTS_PATH_BACKEND)

    result_name_to_request = {request_to_result_name(r): r for r in eval_requests}
    result_name_to_result = {r.eval_name: r for r in eval_results}

    for eval_request in eval_requests:
        if eval_request.likes >= thr:
            result_name: str = request_to_result_name(eval_request)

            # Check the corresponding result
            eval_result: Optional[EvalResult] = (
                result_name_to_result[result_name] if result_name in result_name_to_result else None
            )

            # breakpoint()

            task_lst = TASKS_HARNESS.copy()
            random.shuffle(task_lst)

            # Iterate over tasks and, if we do not have results for a task, run the relevant evaluations
            for task in task_lst:
                task_name = task.benchmark

                do_run_task = False
                if hard_task_lst is None or any(ss in task_name for ss in hard_task_lst):
                    do_run_task = True

                if (eval_result is None or task_name not in eval_result.results) and do_run_task:
                    eval_request: EvalRequest = result_name_to_request[result_name]

                    my_snapshot_download(
                        repo_id=QUEUE_REPO,
                        revision="main",
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                        repo_type="dataset",
                        max_workers=60,
                    )
                    my_set_eval_request(
                        api=API,
                        eval_request=eval_request,
                        set_to_status=RUNNING_STATUS,
                        hf_repo=QUEUE_REPO,
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                    )

                    results = process_evaluation(task, eval_request)

                    my_snapshot_download(
                        repo_id=QUEUE_REPO,
                        revision="main",
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                        repo_type="dataset",
                        max_workers=60,
                    )
                    my_set_eval_request(
                        api=API,
                        eval_request=eval_request,
                        set_to_status=FINISHED_STATUS,
                        hf_repo=QUEUE_REPO,
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                    )

                    return True

    return False


def maybe_refresh_results(thr: int, hard_task_lst: Optional[list[str]] = None) -> bool:
    sanity_checks()

    current_finished_status = [PENDING_STATUS, FINISHED_STATUS, FAILED_STATUS]

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

    random.shuffle(eval_requests)

    eval_results: list[EvalResult] = get_raw_eval_results(EVAL_RESULTS_PATH_BACKEND, EVAL_REQUESTS_PATH_BACKEND)

    result_name_to_request = {request_to_result_name(r): r for r in eval_requests}
    result_name_to_result = {r.eval_name: r for r in eval_results}

    for eval_request in eval_requests:
        if eval_request.likes >= thr:
            result_name: str = request_to_result_name(eval_request)

            # Check the corresponding result
            eval_result: Optional[EvalResult] = (
                result_name_to_result[result_name] if result_name in result_name_to_result else None
            )

            task_lst = TASKS_HARNESS.copy()
            random.shuffle(task_lst)

            # Iterate over tasks and, if we do not have results for a task, run the relevant evaluations
            for task in task_lst:
                task_name = task.benchmark

                do_run_task = False
                if hard_task_lst is None or any(ss in task_name for ss in hard_task_lst):
                    do_run_task = True

                task_lst = ["nq", "trivia", "tqa", "self"]
                if (
                    eval_result is None
                    or do_run_task
                    or task_name not in eval_result.results
                    or any(ss in task_name for ss in task_lst)
                ):
                    eval_request: EvalRequest = result_name_to_request[result_name]

                    my_snapshot_download(
                        repo_id=QUEUE_REPO,
                        revision="main",
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                        repo_type="dataset",
                        max_workers=60,
                    )
                    my_set_eval_request(
                        api=API,
                        eval_request=eval_request,
                        set_to_status=RUNNING_STATUS,
                        hf_repo=QUEUE_REPO,
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                    )

                    results = process_evaluation(task, eval_request)

                    my_snapshot_download(
                        repo_id=QUEUE_REPO,
                        revision="main",
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                        repo_type="dataset",
                        max_workers=60,
                    )
                    my_set_eval_request(
                        api=API,
                        eval_request=eval_request,
                        set_to_status=FINISHED_STATUS,
                        hf_repo=QUEUE_REPO,
                        local_dir=EVAL_REQUESTS_PATH_BACKEND,
                    )

                    return True

    return False


def process_pending_requests() -> bool:
    sanity_checks()

    current_pending_status = [PENDING_STATUS]

    # 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)

    random.shuffle(eval_requests)

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

    if len(eval_requests) == 0:
        return False

    eval_request = eval_requests[0]
    pp.pprint(eval_request)

    my_snapshot_download(
        repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60
    )
    my_set_eval_request(
        api=API,
        eval_request=eval_request,
        set_to_status=RUNNING_STATUS,
        hf_repo=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH_BACKEND,
    )

    task_lst = TASKS_HARNESS.copy()
    random.shuffle(task_lst)

    for task in task_lst:
        results = process_evaluation(task, eval_request)

    my_snapshot_download(
        repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60
    )
    my_set_eval_request(
        api=API,
        eval_request=eval_request,
        set_to_status=FINISHED_STATUS,
        hf_repo=QUEUE_REPO,
        local_dir=EVAL_REQUESTS_PATH_BACKEND,
    )

    return True


def get_args():
    parser = argparse.ArgumentParser(description="Run the backend")
    parser.add_argument("--debug", action="store_true", help="Run in debug mode")
    return parser.parse_args()


if __name__ == "__main__":
    args = get_args()
    local_debug = args.debug
    # debug specific task by ping
    if local_debug:
        # debug_model_names = ["mistralai/Mixtral-8x7B-Instruct-v0.1"]
        debug_model_names = ["facebook/opt-1.3b"]
        # debug_model_names = ["TheBloke/Mixtral-8x7B-v0.1-GPTQ"]
        debug_task_name = 'selfcheckgpt'
        # debug_task_name = "mmlu"
        task_lst = TASKS_HARNESS.copy()
        for task in task_lst:
            for debug_model_name in debug_model_names:
                task_name = task.benchmark
                if task_name != debug_task_name:
                    continue
                eval_request = EvalRequest(
                    model=debug_model_name, private=False, status="", json_filepath="", precision="float16", inference_framework="hf-chat"
                )
                results = process_evaluation(task, eval_request)
    else:
        while True:
            res = False

            # if random.randint(0, 10) == 0:
            res = process_pending_requests()
            print(f"waiting for 60 seconds")
            time.sleep(60)

            # if res is False:
            #     if random.randint(0, 5) == 0:
            #         res = maybe_refresh_results(100)
            #     else:
            #         res = process_finished_requests(100)

            # time.sleep(60)

            # if res is False:
            #     if random.randint(0, 5) == 0:
            #         res = maybe_refresh_results(0)
            #     else:
            #         res = process_finished_requests(0)