#!/usr/bin/env python import os import json import argparse import socket import random import threading 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, Task from src.backend.manage_requests import EvalRequest from src.leaderboard.read_evals import EvalResult from src.envs import QUEUE_REPO, RESULTS_REPO, API, DEBUG_QUEUE_REPO, DEBUG_RESULTS_REPO from src.utils import my_snapshot_download, analyze_gpu_stats, parse_nvidia_smi, monitor_gpus, get_gpu_details from src.leaderboard.read_evals import get_raw_eval_results from typing import Optional import GPUtil import time import pprint import logging from lm_eval.filters.extraction import RegexFilter # 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 tuple_input_decorator(func): def wrapper(self, resps, docs): stripped_resps = [[resp_data[0] for resp_data in group] for group in resps] filtered_resps = func(self, stripped_resps, docs) combined_resps = [] for original_group, new_group in zip(resps, filtered_resps): combined_group = [(new_resp,) + rest_of_data[1:] for new_resp, rest_of_data in zip(new_group, original_group)] combined_resps.append(combined_group) return combined_resps return wrapper 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=, # model_type=, # weight_type=, # 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, limit: Optional[int] = None) -> dict: batch_size = 1 batch_size = eval_request.batch_size init_gpu_info = analyze_gpu_stats(parse_nvidia_smi()) # if init_gpu_info['Mem(M)'] > 500: # assert False, f"This machine is not empty: {init_gpu_info}" gpu_stats_list = [] stop_event = threading.Event() monitor_thread = threading.Thread(target=monitor_gpus, args=(stop_event, 5, gpu_stats_list)) monitor_thread.start() original_apply = RegexFilter.apply if task.benchmark in ["gsm8k", "gsm8k_cot", "gsm8k_cot_self_consistency", "gsm8k_custom"]: RegexFilter.apply = tuple_input_decorator(RegexFilter.apply) else: RegexFilter.apply = original_apply 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) stop_event.set() monitor_thread.join() gpu_info = analyze_gpu_stats(gpu_stats_list) for task_name in results['results'].keys(): for key, value in gpu_info.items(): if "GPU" not in key: results['results'][task_name][f"{key},none"] = int(value) else: results['results'][task_name][f"{key},none"] = value results['results'][task_name]['batch_size,none'] = batch_size results['results'][task_name]['precision,none'] = eval_request.precision print(f"gpu_stats_list: {gpu_stats_list}") print("GPU Usage:", gpu_info) dumped = json.dumps(results, indent=2, default=lambda o: "") # 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", ) RegexFilter.apply = original_apply 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() print("Processing pending requests") 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) gpu_type = eval_request.gpu_type curr_gpu_type = get_gpu_details() if gpu_type != curr_gpu_type: print(f"GPU type mismatch: {gpu_type} vs {curr_gpu_type}") return False 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") # debug parameters parser.add_argument("--task", type=str, default="selfcheckgpt,mmlu, gsm8k", help="Task to debug") parser.add_argument("--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1,mistralai/Mixtral-8x7B-v0.1", help="Model to debug") parser.add_argument("--precision", type=str, default="float32,float16,8bit,4bit", help="Precision to debug") parser.add_argument("--inference-framework", type=str, default="hf-chat", help="Inference framework to debug") parser.add_argument("--limit", type=int, default=None, help="Limit for the number of samples") parser.add_argument("--gpu-type", type=str, default="NVIDIA-A100-PCIe-80GB", help="GPU type. NVIDIA-A100-PCIe-80GB; NVIDIA-RTX-A5000-24GB; NVIDIA-H100-PCIe-80GB") parser.add_argument("--debug_repo", action="store_true", help="Use debug repo") return parser.parse_args() if __name__ == "__main__": args = get_args() local_debug = args.debug # debug specific task by ping if local_debug and not args.debug_repo: # debug_model_names = [args.model] # Use model from arguments # debug_task_name = [args.task] # Use task from arguments debug_model_names = args.model.split(",") debug_task_name = args.task.split(",") precisions = args.precision.split(",") print(f"debug_model_names: {debug_model_names}, debug_task_name: {debug_task_name}, precisions: {precisions}") task_lst = TASKS_HARNESS.copy() RESULTS_REPO = DEBUG_RESULTS_REPO for precision in precisions: for debug_model_name in debug_model_names: for task in task_lst: task_name = task.benchmark if task_name not in debug_task_name: continue # try: eval_request = EvalRequest( model=debug_model_name, private=False, status="", json_filepath="", precision=precision, # Use precision from arguments inference_framework=args.inference_framework, # Use inference framework from arguments gpu_type=args.gpu_type ) curr_gpu_type = get_gpu_details() if eval_request.gpu_type != curr_gpu_type: print(f"GPU type mismatch: {eval_request.gpu_type} vs {curr_gpu_type}") raise Exception("GPU type mismatch") results = process_evaluation(task, eval_request, limit=args.limit) # except Exception as e: # print(f"debug running error: {e}") elif local_debug and args.debug_repo: QUEUE_REPO = DEBUG_QUEUE_REPO RESULTS_REPO = DEBUG_RESULTS_REPO 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) elif not local_debug and not args.debug_repo: 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) else: raise Exception("Cannot use debug_repo without local debug flag")