Delete backend-cli.py

backend-cli.py

@@ -1,288 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import json
-
-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
-
-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 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)
-
-    random.shuffle(eval_requests)
-
-    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
-            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
-
-                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 maybe_refresh_results(thr: int) -> 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, 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
-            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
-
-                if (eval_result is None or
-                        task_name not in eval_result.results or
-                        'nq' in task_name or 'trivia' in task_name or 'tqa' in task_name or 'self' in task_name):
-                    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
-
-    if socket.gethostname() in {'hamburg', 'neuromancer'} or os.path.isdir("/home/pminervi"):
-        wait = False
-
-    if wait:
-        time.sleep(60 * random.randint(5, 10))
-
-    res = False
-
-    if random.randint(0, 1) == 0:
-        res = process_pending_requests()
-        time.sleep(60)
-
-    if res is False:
-        if random.randint(0, 1) == 0:
-            res = maybe_refresh_results(100)
-        else:
-            res = process_finished_requests(100)
-
-    time.sleep(60)
-
-    if res is False:
-        if random.randint(0, 1) == 0:
-            res = maybe_refresh_results(0)
-        else:
-            res = process_finished_requests(0)