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 EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PATH_BACKEND, DEVICE, LIMIT, Tasks, Task, num_fewshots

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
import argparse


# def get_subdirectories(path):
#     subdirectories = []
#     # Get all entries in the directory
#     entries = os.listdir(path)
#     for entry in entries:
#         # Check if the entry is a directory
#         if os.path.isdir(os.path.join(path, entry)):
#             subdirectories.append(entry)
#     return subdirectories

# parser = argparse.ArgumentParser(description="Get subdirectory names")
# parser.add_argument("include_path", help="Path to the directory", nargs='?', default=None)
# args = parser.parse_args()
    
# # = get_subdirectories(args.include_path)




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]

# starts by downloading results and requests. makes sense since we want to be able to use different backend servers!
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:
    
    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

# doesn't make distinctions for tasks since the original code runs eval on ALL tasks. 
def process_evaluation(task_name: str, eval_request: EvalRequest) -> dict:
    # batch_size = 1
    batch_size = "auto"

    # might not have to get the benchmark. 
    print(f"task_name parameter in process_evaluation() = {task_name}") #, task_names=[task.benchmark] = {[task.benchmark]}")

    num_fewshot = num_fewshots[task_name]

    results = run_evaluation(eval_request=eval_request, task_names=task_name, num_fewshot=num_fewshot,
                             batch_size=batch_size, device=DEVICE, use_cache=None, limit=LIMIT)

    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_{task_name}_{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_{task_name}_{datetime.now()}.json",
                    repo_id=RESULTS_REPO, repo_type="dataset")
    return results


# the rendering is done with files in local repo. 
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
    # GETTING REQUESTS FROM THE HUB NOT LOCAL DIR. 
    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)

    # this says zero 
    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()
    task_lst = eval_request.get_user_requested_task_names()
    random.shuffle(task_lst)
    print(f"task_lst in process_pending_requests(): {task_lst}")

    for task_name in task_lst: 
        
        results = process_evaluation(task_name, 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(2, 5))
    #     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)