local-llm-server/llm_server/workers/mainer.py

58 lines
3.2 KiB
Python

import time
import requests
from llm_server import opts
from llm_server.cluster.cluster_config import cluster_config, get_backends
from llm_server.custom_redis import redis
from llm_server.database.database import weighted_average_column_for_model
from llm_server.llm.info import get_info
from llm_server.routes.queue import RedisPriorityQueue, priority_queue
def main_background_thread():
while True:
online, offline = get_backends()
for backend_url in online:
backend_info = cluster_config.get_backend(backend_url)
backend_mode = backend_info['mode']
backend_info = get_info(backend_url, backend_mode)
running_model = backend_info.get('model')
if not running_model:
continue
average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps = calc_stats_for_backend(backend_url, running_model, backend_mode)
if average_generation_elapsed_sec: # returns None on exception
cluster_config.set_backend_value(backend_url, 'average_generation_elapsed_sec', average_generation_elapsed_sec)
if average_output_tokens:
cluster_config.set_backend_value(backend_url, 'average_output_tokens', average_output_tokens)
if average_generation_elapsed_sec and average_output_tokens:
cluster_config.set_backend_value(backend_url, 'estimated_avg_tps', estimated_avg_tps)
if opts.background_homepage_cacher:
try:
base_client_api = redis.get('base_client_api', dtype=str)
r = requests.get('https://' + base_client_api, timeout=5)
except Exception as e:
print(f'Failed fetch the homepage - {e.__class__.__name__}: {e}')
backends = priority_queue.get_backends()
for backend_url in backends:
queue = RedisPriorityQueue(backend_url)
queue.cleanup()
time.sleep(30)
def calc_stats_for_backend(backend_url, running_model, backend_mode):
# exclude_zeros=True filters out rows where an error message was returned. Previously, if there was an error, 0
# was entered into the column. The new code enters null instead but we need to be backwards compatible for now.
average_generation_elapsed_sec = weighted_average_column_for_model('prompts', 'generation_time',
running_model, backend_mode, backend_url, exclude_zeros=True,
include_system_tokens=opts.include_system_tokens_in_stats) or 0
average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens',
running_model, backend_mode, backend_url, exclude_zeros=True,
include_system_tokens=opts.include_system_tokens_in_stats) or 0
estimated_avg_tps = round(average_output_tokens / average_generation_elapsed_sec, 2) if average_generation_elapsed_sec > 0 else 0 # Avoid division by zero
return average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps