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open-moe-llm-leaderboard / backend-cli.py

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	#!/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=<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, 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: "<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",
	)

	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")