backend-cli.py

#!/usr/bin/env python

import os
import json

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

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

import time

import logging
import pprint


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:
            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 = "auto"

    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) -> 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)

    # XXX
    # eval_requests = [r for r in eval_requests if 'bloom-560m' in r.model]

    random.shuffle(eval_requests)

    from src.leaderboard.read_evals import get_raw_eval_results
    eval_results: list[EvalResult] = get_raw_eval_results(EVAL_RESULTS_PATH_BACKEND, EVAL_REQUESTS_PATH_BACKEND, True)

    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
            from typing import Optional
            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

                if eval_result is None or task_name not in eval_result.results:
                    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


if __name__ == "__main__":
    wait = True

    import socket
    if socket.gethostname() in {'hamburg'} or os.path.isdir("/home/pminervi"):
        wait = False

    if wait:
        time.sleep(60 * random.randint(5, 10))
        pass

    # res = False
    res = process_pending_requests()

    if res is False:
        res = process_finished_requests(100)

    if res is False:
        res = process_finished_requests(0)