183 lines
6.6 KiB
Python
183 lines
6.6 KiB
Python
from llm_server.config.config import mode_ui_names
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try:
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import gevent.monkey
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gevent.monkey.patch_all()
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except ImportError:
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pass
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from llm_server.pre_fork import server_startup
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from llm_server.config.load import load_config, parse_backends
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import os
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import sys
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from pathlib import Path
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import simplejson as json
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from flask import Flask, jsonify, render_template, request
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import llm_server
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from llm_server.database.conn import database
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from llm_server.database.create import create_db
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from llm_server.llm import get_token_count
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from llm_server.routes.openai import openai_bp
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from llm_server.routes.server_error import handle_server_error
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from llm_server.routes.v1 import bp
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from llm_server.stream import init_socketio
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# TODO: set VLLM to stream ALL data using socket.io. If the socket disconnects, cancel generation.
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# TODO: add backend fallbacks. Backends at the bottom of the list are higher priority and are fallbacks if the upper ones fail
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# TODO: implement background thread to test backends via sending test prompts
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# TODO: if backend fails request, mark it as down
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# TODO: allow setting concurrent gens per-backend
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# TODO: set the max tokens to that of the lowest backend
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# TODO: implement RRD backend loadbalancer option
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# TODO: have VLLM reject a request if it already has n == concurrent_gens running
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# TODO: add a way to cancel VLLM gens. Maybe use websockets?
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# TODO: use coloredlogs
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# TODO: need to update opts. for workers
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# TODO: add a healthcheck to VLLM
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# Lower priority
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# TODO: estiamted wait time needs to account for full concurrent_gens but the queue is less than concurrent_gens
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# TODO: the estiamted wait time lags behind the stats
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# TODO: simulate OpenAI error messages regardless of endpoint
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# TODO: send extra headers when ratelimited?
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# TODO: make sure log_prompt() is used everywhere, including errors and invalid requests
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# TODO: unify logging thread in a function and use async/await instead
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# TODO: move the netdata stats to a seperate part of the stats and have it set to the currently selected backend
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# TODO: have VLLM reply with stats (TPS, generated token count, processing time)
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# TODO: add config reloading via stored redis variables
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# Done, but need to verify
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# TODO: add more excluding to SYSTEM__ tokens
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# TODO: return 200 when returning formatted sillytavern error
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try:
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import vllm
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except ModuleNotFoundError as e:
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print('Could not import vllm-gptq:', e)
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print('Please see README.md for install instructions.')
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sys.exit(1)
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import config
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from llm_server import opts
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from llm_server.helpers import auto_set_base_client_api
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from llm_server.llm.vllm.info import vllm_info
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from llm_server.custom_redis import RedisCustom, flask_cache
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from llm_server.llm import redis
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from llm_server.routes.stats import get_active_gen_workers
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from llm_server.routes.v1.generate_stats import generate_stats
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app = Flask(__name__)
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init_socketio(app)
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app.register_blueprint(bp, url_prefix='/api/v1/')
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app.register_blueprint(openai_bp, url_prefix='/api/openai/v1/')
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flask_cache.init_app(app)
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flask_cache.clear()
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script_path = os.path.dirname(os.path.realpath(__file__))
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config_path_environ = os.getenv("CONFIG_PATH")
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if config_path_environ:
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config_path = config_path_environ
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else:
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config_path = Path(script_path, 'config', 'config.yml')
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success, config, msg = load_config(config_path, script_path)
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if not success:
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print('Failed to load config:', msg)
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sys.exit(1)
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database.init_db(config['mysql']['host'], config['mysql']['username'], config['mysql']['password'], config['mysql']['database'])
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create_db()
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llm_server.llm.redis = RedisCustom('local_llm')
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create_db()
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x = parse_backends(config)
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print(x)
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# print(app.url_map)
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@app.route('/')
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@app.route('/api')
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@app.route('/api/openai')
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@flask_cache.cached(timeout=10)
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def home():
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stats = generate_stats()
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if not stats['online']:
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running_model = estimated_wait_sec = 'offline'
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else:
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running_model = redis.get('running_model', str, 'ERROR')
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active_gen_workers = get_active_gen_workers()
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if stats['queue']['queued'] == 0 and active_gen_workers >= opts.concurrent_gens:
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# There will be a wait if the queue is empty but prompts are processing, but we don't
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# know how long.
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estimated_wait_sec = f"less than {stats['stats']['average_generation_elapsed_sec']} seconds"
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else:
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estimated_wait_sec = f"{stats['queue']['estimated_wait_sec']} seconds"
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if len(config['analytics_tracking_code']):
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analytics_tracking_code = f"<script>\n{config['analytics_tracking_code']}\n</script>"
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else:
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analytics_tracking_code = ''
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if config['info_html']:
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info_html = config['info_html']
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else:
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info_html = ''
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mode_info = ''
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if opts.mode == 'vllm':
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mode_info = vllm_info
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base_client_api = redis.get('base_client_api', str)
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return render_template('home.html',
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llm_middleware_name=opts.llm_middleware_name,
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analytics_tracking_code=analytics_tracking_code,
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info_html=info_html,
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current_model=opts.manual_model_name if opts.manual_model_name else running_model,
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client_api=f'https://{base_client_api}',
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ws_client_api=f'wss://{base_client_api}/v1/stream' if opts.enable_streaming else None,
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estimated_wait=estimated_wait_sec,
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mode_name=mode_ui_names[opts.mode][0],
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api_input_textbox=mode_ui_names[opts.mode][1],
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streaming_input_textbox=mode_ui_names[opts.mode][2],
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context_size=opts.context_size,
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stats_json=json.dumps(stats, indent=4, ensure_ascii=False),
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extra_info=mode_info,
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openai_client_api=f'https://{base_client_api}/openai/v1' if opts.enable_openi_compatible_backend else 'disabled',
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expose_openai_system_prompt=opts.expose_openai_system_prompt,
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enable_streaming=opts.enable_streaming,
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)
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# TODO: add authenticated route to get the current backend URL. Add it to /v1/backend
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@app.route('/<first>')
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@app.route('/<first>/<path:rest>')
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def fallback(first=None, rest=None):
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return jsonify({
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'code': 404,
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'msg': 'not found'
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}), 404
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@app.errorhandler(500)
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def server_error(e):
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return handle_server_error(e)
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@app.before_request
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def before_app_request():
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auto_set_base_client_api(request)
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if __name__ == "__main__":
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server_startup(None)
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print('FLASK MODE - Startup complete!')
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app.run(host='0.0.0.0', threaded=False, processes=15)
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