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")
parser.add_argument("--model_type", type=str, default="chat", help="Model type")
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,
model_type=args.model_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")