Merge cluster to master #3
13
daemon.py
13
daemon.py
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@ -3,13 +3,12 @@ import sys
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import time
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import time
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from pathlib import Path
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from pathlib import Path
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from redis import Redis
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from llm_server.cluster.cluster_config import cluster_config
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from llm_server.cluster.cluster_config import cluster_config
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from llm_server.cluster.redis_cycle import redis_cycler_db
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from llm_server.cluster.stores import redis_running_models
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from llm_server.config.load import load_config, parse_backends
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from llm_server.config.load import load_config, parse_backends
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from llm_server.custom_redis import redis
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from llm_server.custom_redis import redis
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from llm_server.database.create import create_db
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from llm_server.database.create import create_db
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from llm_server.routes.queue import priority_queue
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from llm_server.routes.v1.generate_stats import generate_stats
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from llm_server.routes.v1.generate_stats import generate_stats
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from llm_server.workers.threader import start_background
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from llm_server.workers.threader import start_background
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@ -21,11 +20,8 @@ else:
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config_path = Path(script_path, 'config', 'config.yml')
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config_path = Path(script_path, 'config', 'config.yml')
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if __name__ == "__main__":
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if __name__ == "__main__":
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flushed_keys = redis.flush()
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Redis().flushall()
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print('Flushed', len(flushed_keys), 'keys from Redis.')
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print('Flushed Redis.')
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redis_cycler_db.flushall()
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redis_running_models.flush()
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success, config, msg = load_config(config_path)
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success, config, msg = load_config(config_path)
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if not success:
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if not success:
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@ -34,7 +30,6 @@ if __name__ == "__main__":
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create_db()
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create_db()
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priority_queue.flush()
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cluster_config.clear()
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cluster_config.clear()
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cluster_config.load(parse_backends(config))
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cluster_config.load(parse_backends(config))
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@ -3,11 +3,13 @@ import sys
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import openai
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import openai
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import llm_server
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from llm_server import opts
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from llm_server import opts
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from llm_server.config.config import ConfigLoader, config_default_vars, config_required_vars
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from llm_server.config.config import ConfigLoader, config_default_vars, config_required_vars
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from llm_server.custom_redis import redis
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from llm_server.custom_redis import redis
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from llm_server.database.conn import database
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from llm_server.database.conn import database
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from llm_server.database.database import get_number_of_rows
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from llm_server.database.database import get_number_of_rows
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from llm_server.routes.queue import PriorityQueue
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def load_config(config_path):
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def load_config(config_path):
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@ -54,6 +56,8 @@ def load_config(config_path):
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for item in config['cluster']:
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for item in config['cluster']:
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opts.cluster_workers += item['concurrent_gens']
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opts.cluster_workers += item['concurrent_gens']
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llm_server.routes.queue.priority_queue = PriorityQueue([x['backend_url'] for x in config['cluster']])
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if opts.openai_expose_our_model and not opts.openai_api_key:
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if opts.openai_expose_our_model and not opts.openai_api_key:
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print('If you set openai_epose_our_model to false, you must set your OpenAI key in openai_api_key.')
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print('If you set openai_epose_our_model to false, you must set your OpenAI key in openai_api_key.')
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sys.exit(1)
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sys.exit(1)
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@ -74,7 +74,7 @@ def openai_chat_completions():
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event = None
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event = None
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if not handler.is_client_ratelimited():
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if not handler.is_client_ratelimited():
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# Add a dummy event to the queue and wait for it to reach a worker
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# Add a dummy event to the queue and wait for it to reach a worker
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event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
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event = priority_queue.put(handler.backend_url, (None, handler.client_ip, handler.token, None), handler.token_priority, handler.selected_model)
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if not event:
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if not event:
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log_to_db(
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log_to_db(
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handler.client_ip,
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handler.client_ip,
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@ -107,63 +107,64 @@ def openai_chat_completions():
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oai_string = generate_oai_string(30)
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oai_string = generate_oai_string(30)
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def generate():
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def generate():
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response = generator(msg_to_backend, handler.backend_url)
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try:
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generated_text = ''
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response = generator(msg_to_backend, handler.backend_url)
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partial_response = b''
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generated_text = ''
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for chunk in response.iter_content(chunk_size=1):
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partial_response = b''
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partial_response += chunk
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for chunk in response.iter_content(chunk_size=1):
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if partial_response.endswith(b'\x00'):
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partial_response += chunk
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json_strs = partial_response.split(b'\x00')
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if partial_response.endswith(b'\x00'):
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for json_str in json_strs:
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json_strs = partial_response.split(b'\x00')
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if json_str:
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for json_str in json_strs:
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try:
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if json_str:
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json_obj = json.loads(json_str.decode())
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try:
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new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
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json_obj = json.loads(json_str.decode())
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generated_text = generated_text + new
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new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
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except IndexError:
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generated_text = generated_text + new
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# ????
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except IndexError:
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continue
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# ????
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continue
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data = {
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data = {
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"id": f"chatcmpl-{oai_string}",
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"id": f"chatcmpl-{oai_string}",
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"object": "chat.completion.chunk",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"created": int(time.time()),
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"model": model,
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"model": model,
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"choices": [
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"choices": [
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{
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{
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"index": 0,
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"index": 0,
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"delta": {
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"delta": {
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"content": new
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"content": new
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},
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},
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"finish_reason": None
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"finish_reason": None
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}
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}
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]
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]
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}
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}
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yield f'data: {json.dumps(data)}\n\n'
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yield f'data: {json.dumps(data)}\n\n'
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yield 'data: [DONE]\n\n'
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yield 'data: [DONE]\n\n'
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end_time = time.time()
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end_time = time.time()
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elapsed_time = end_time - start_time
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elapsed_time = end_time - start_time
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log_to_db(
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log_to_db(
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handler.client_ip,
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handler.client_ip,
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handler.token,
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handler.token,
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handler.prompt,
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handler.prompt,
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generated_text,
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generated_text,
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elapsed_time,
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elapsed_time,
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handler.parameters,
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handler.parameters,
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r_headers,
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r_headers,
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response_status_code,
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response_status_code,
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r_url,
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r_url,
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handler.backend_url,
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handler.backend_url,
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)
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)
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finally:
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# After completing inference, we need to tell the worker we
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# are finished.
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if event_id: # may be None if ratelimited.
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redis.publish(event_id, 'finished')
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else:
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print('event_id was None!')
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return Response(generate(), mimetype='text/event-stream')
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return Response(generate(), mimetype='text/event-stream')
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except Exception:
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except Exception:
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traceback.print_exc()
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traceback.print_exc()
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return 'INTERNAL SERVER', 500
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return 'INTERNAL SERVER', 500
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finally:
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# After completing inference, we need to tell the worker we
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# are finished.
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if event_id: # may be None if ratelimited.
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redis.publish(event_id, 'finished')
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else:
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print('event_id was None!')
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@ -102,7 +102,7 @@ def openai_completions():
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event = None
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event = None
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if not handler.is_client_ratelimited():
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if not handler.is_client_ratelimited():
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# Add a dummy event to the queue and wait for it to reach a worker
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# Add a dummy event to the queue and wait for it to reach a worker
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event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
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event = priority_queue.put(handler.backend_url, (None, handler.client_ip, handler.token, None), handler.token_priority, handler.selected_model)
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if not event:
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if not event:
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log_to_db(
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log_to_db(
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handler.client_ip,
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handler.client_ip,
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@ -135,61 +135,62 @@ def openai_completions():
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oai_string = generate_oai_string(30)
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oai_string = generate_oai_string(30)
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def generate():
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def generate():
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generated_text = ''
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try:
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partial_response = b''
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generated_text = ''
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for chunk in response.iter_content(chunk_size=1):
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partial_response = b''
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partial_response += chunk
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for chunk in response.iter_content(chunk_size=1):
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if partial_response.endswith(b'\x00'):
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partial_response += chunk
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json_strs = partial_response.split(b'\x00')
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if partial_response.endswith(b'\x00'):
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for json_str in json_strs:
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json_strs = partial_response.split(b'\x00')
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if json_str:
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for json_str in json_strs:
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try:
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if json_str:
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json_obj = json.loads(json_str.decode())
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try:
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new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
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json_obj = json.loads(json_str.decode())
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generated_text = generated_text + new
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new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
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except IndexError:
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generated_text = generated_text + new
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# ????
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except IndexError:
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continue
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# ????
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continue
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|
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data = {
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data = {
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"id": f"cmpl-{oai_string}",
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"id": f"cmpl-{oai_string}",
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"object": "text_completion",
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"object": "text_completion",
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"created": int(time.time()),
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"created": int(time.time()),
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"model": model,
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"model": model,
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"choices": [
|
"choices": [
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{
|
{
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"index": 0,
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"index": 0,
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"delta": {
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"delta": {
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"content": new
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"content": new
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},
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},
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"finish_reason": None
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"finish_reason": None
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}
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}
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]
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]
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}
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}
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yield f'data: {json.dumps(data)}\n\n'
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yield f'data: {json.dumps(data)}\n\n'
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yield 'data: [DONE]\n\n'
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yield 'data: [DONE]\n\n'
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end_time = time.time()
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end_time = time.time()
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elapsed_time = end_time - start_time
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elapsed_time = end_time - start_time
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|
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log_to_db(
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log_to_db(
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handler.client_ip,
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handler.client_ip,
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handler.token,
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handler.token,
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handler.prompt,
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handler.prompt,
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generated_text,
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generated_text,
|
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elapsed_time,
|
elapsed_time,
|
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handler.parameters,
|
handler.parameters,
|
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r_headers,
|
r_headers,
|
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response_status_code,
|
response_status_code,
|
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r_url,
|
r_url,
|
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handler.backend_url,
|
handler.backend_url,
|
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)
|
)
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|
finally:
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|
if event_id:
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|
redis.publish(event_id, 'finished')
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|
else:
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|
print('event_id was None!')
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|
|
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return Response(generate(), mimetype='text/event-stream')
|
return Response(generate(), mimetype='text/event-stream')
|
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except Exception:
|
except Exception:
|
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traceback.print_exc()
|
traceback.print_exc()
|
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return 'INTERNAL SERVER', 500
|
return 'INTERNAL SERVER', 500
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finally:
|
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if event_id:
|
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redis.publish(event_id, 'finished')
|
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else:
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print('event_id was None!')
|
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|
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@ -1,10 +1,12 @@
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import json
|
import json
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import pickle
|
import pickle
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import time
|
import time
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|
from typing import Tuple
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from uuid import uuid4
|
from uuid import uuid4
|
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|
|
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from redis import Redis
|
from redis import Redis
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|
|
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from llm_server.cluster.cluster_config import cluster_config
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from llm_server.custom_redis import RedisCustom, redis
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from llm_server.custom_redis import RedisCustom, redis
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from llm_server.database.database import get_token_ratelimit
|
from llm_server.database.database import get_token_ratelimit
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|
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@ -20,7 +22,7 @@ def decrement_ip_count(client_ip: str, redis_key):
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|
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|
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class RedisPriorityQueue:
|
class RedisPriorityQueue:
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def __init__(self, name: str = 'priority_queue', db: int = 12):
|
def __init__(self, name, db: int = 12):
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self.redis = RedisCustom(name, db=db)
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self.redis = RedisCustom(name, db=db)
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def put(self, item, priority, selected_model):
|
def put(self, item, priority, selected_model):
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|
@ -98,9 +100,6 @@ class DataEvent:
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return pickle.loads(item['data'])
|
return pickle.loads(item['data'])
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|
|
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|
|
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priority_queue = RedisPriorityQueue()
|
|
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|
|
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|
|
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def update_active_workers(key: str, operation: str):
|
def update_active_workers(key: str, operation: str):
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if operation == 'incr':
|
if operation == 'incr':
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redis.incr(f'active_gen_workers:{key}')
|
redis.incr(f'active_gen_workers:{key}')
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|
@ -118,3 +117,60 @@ def incr_active_workers(selected_model: str, backend_url: str):
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def decr_active_workers(selected_model: str, backend_url: str):
|
def decr_active_workers(selected_model: str, backend_url: str):
|
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update_active_workers(selected_model, 'decr')
|
update_active_workers(selected_model, 'decr')
|
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update_active_workers(backend_url, 'decr')
|
update_active_workers(backend_url, 'decr')
|
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|
|
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|
|
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|
class PriorityQueue:
|
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|
def __init__(self, backends: list = None):
|
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|
"""
|
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|
Only have to load the backends once.
|
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|
:param backends:
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|
"""
|
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|
self.redis = Redis(host='localhost', port=6379, db=9)
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|
if backends:
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|
for item in backends:
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|
self.redis.lpush('backends', item)
|
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|
|
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|
def get_backends(self):
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|
return [x.decode('utf-8') for x in self.redis.lrange('backends', 0, -1)]
|
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|
|
||||||
|
def get_queued_ip_count(self, client_ip: str):
|
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|
count = 0
|
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|
for backend_url in self.get_backends():
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|
queue = RedisPriorityQueue(backend_url)
|
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|
count += queue.get_queued_ip_count(client_ip)
|
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|
return count
|
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|
|
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|
def put(self, backend_url, item: Tuple[dict, str, str, dict], priority: int, selected_model: str):
|
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|
queue = RedisPriorityQueue(backend_url)
|
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|
return queue.put(item, priority, selected_model)
|
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|
|
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|
def len(self, model_name):
|
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|
count = 0
|
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|
backends_with_models = []
|
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|
for k in self.get_backends():
|
||||||
|
info = cluster_config.get_backend(k)
|
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|
if info.get('model') == model_name:
|
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|
backends_with_models.append(k)
|
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|
for backend_url in backends_with_models:
|
||||||
|
queue = RedisPriorityQueue(backend_url)
|
||||||
|
count += queue.len(model_name)
|
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|
return count
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
count = 0
|
||||||
|
for backend_url in self.get_backends():
|
||||||
|
queue = RedisPriorityQueue(backend_url)
|
||||||
|
count += len(queue)
|
||||||
|
return count
|
||||||
|
|
||||||
|
def flush(self):
|
||||||
|
for k in self.redis.keys():
|
||||||
|
q = json.loads(self.redis.get(k))
|
||||||
|
q.flush()
|
||||||
|
self.redis.set(k, json.dumps(q))
|
||||||
|
|
||||||
|
def flush_db(self):
|
||||||
|
self.redis.flushdb()
|
||||||
|
|
||||||
|
|
||||||
|
priority_queue = PriorityQueue()
|
||||||
|
|
|
@ -7,7 +7,7 @@ from flask import Response, request
|
||||||
from llm_server import opts
|
from llm_server import opts
|
||||||
from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
|
from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
|
||||||
from llm_server.custom_redis import redis
|
from llm_server.custom_redis import redis
|
||||||
from llm_server.database.database import get_token_ratelimit, do_db_log
|
from llm_server.database.database import get_token_ratelimit
|
||||||
from llm_server.database.log_to_db import log_to_db
|
from llm_server.database.log_to_db import log_to_db
|
||||||
from llm_server.helpers import auto_set_base_client_api
|
from llm_server.helpers import auto_set_base_client_api
|
||||||
from llm_server.llm.oobabooga.ooba_backend import OobaboogaBackend
|
from llm_server.llm.oobabooga.ooba_backend import OobaboogaBackend
|
||||||
|
@ -131,7 +131,7 @@ class RequestHandler:
|
||||||
request_valid, invalid_response = self.validate_request(prompt, do_log=True)
|
request_valid, invalid_response = self.validate_request(prompt, do_log=True)
|
||||||
if not request_valid:
|
if not request_valid:
|
||||||
return (False, None, None, 0), invalid_response
|
return (False, None, None, 0), invalid_response
|
||||||
event = priority_queue.put((llm_request, self.client_ip, self.token, self.parameters, self.backend_url), self.token_priority, self.selected_model)
|
event = priority_queue.put(self.backend_url, (llm_request, self.client_ip, self.token, self.parameters), self.token_priority, self.selected_model)
|
||||||
else:
|
else:
|
||||||
event = None
|
event = None
|
||||||
|
|
||||||
|
|
|
@ -122,7 +122,7 @@ def do_stream(ws, model_name):
|
||||||
event = None
|
event = None
|
||||||
if not handler.is_client_ratelimited():
|
if not handler.is_client_ratelimited():
|
||||||
# Add a dummy event to the queue and wait for it to reach a worker
|
# Add a dummy event to the queue and wait for it to reach a worker
|
||||||
event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
|
event = priority_queue.put(handler.backend_url, (None, handler.client_ip, handler.token, None), handler.token_priority, handler.selected_model)
|
||||||
if not event:
|
if not event:
|
||||||
log_to_db(
|
log_to_db(
|
||||||
handler.client_ip,
|
handler.client_ip,
|
||||||
|
|
|
@ -1,29 +1,24 @@
|
||||||
import threading
|
import threading
|
||||||
import time
|
import time
|
||||||
|
from uuid import uuid4
|
||||||
|
|
||||||
from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
|
from llm_server.cluster.cluster_config import cluster_config
|
||||||
from llm_server.custom_redis import redis
|
from llm_server.custom_redis import redis, RedisCustom
|
||||||
from llm_server.llm.generator import generator
|
from llm_server.llm.generator import generator
|
||||||
from llm_server.routes.queue import DataEvent, decr_active_workers, decrement_ip_count, incr_active_workers, increment_ip_count, priority_queue
|
from llm_server.routes.queue import DataEvent, decr_active_workers, decrement_ip_count, incr_active_workers, increment_ip_count, RedisPriorityQueue, PriorityQueue, priority_queue
|
||||||
|
|
||||||
|
|
||||||
def worker():
|
def worker(backend_url):
|
||||||
|
queue = RedisPriorityQueue(backend_url)
|
||||||
while True:
|
while True:
|
||||||
(request_json_body, client_ip, token, parameters, backend_url), event_id, selected_model = priority_queue.get()
|
(request_json_body, client_ip, token, parameters), event_id, selected_model = queue.get()
|
||||||
if not backend_url:
|
|
||||||
backend_url = get_a_cluster_backend(selected_model)
|
|
||||||
else:
|
|
||||||
backend_url = cluster_config.validate_backend(backend_url)
|
|
||||||
backend_info = cluster_config.get_backend(backend_url)
|
backend_info = cluster_config.get_backend(backend_url)
|
||||||
|
|
||||||
if not selected_model:
|
if not selected_model:
|
||||||
selected_model = backend_info['model']
|
selected_model = backend_info['model']
|
||||||
|
|
||||||
increment_ip_count(client_ip, 'processing_ips')
|
increment_ip_count(client_ip, 'processing_ips')
|
||||||
incr_active_workers(selected_model, backend_url)
|
incr_active_workers(selected_model, backend_url)
|
||||||
|
|
||||||
need_to_wait(backend_url)
|
|
||||||
|
|
||||||
try:
|
try:
|
||||||
if not request_json_body:
|
if not request_json_body:
|
||||||
# This was a dummy request from the streaming handlers.
|
# This was a dummy request from the streaming handlers.
|
||||||
|
@ -37,7 +32,6 @@ def worker():
|
||||||
redis.publish(event_id, 'begin')
|
redis.publish(event_id, 'begin')
|
||||||
for item in pubsub.listen():
|
for item in pubsub.listen():
|
||||||
if item['type'] == 'message' and item['data'].decode('utf-8') == 'finished':
|
if item['type'] == 'message' and item['data'].decode('utf-8') == 'finished':
|
||||||
# Once the handler is complete, move on.
|
|
||||||
break
|
break
|
||||||
time.sleep(0.1)
|
time.sleep(0.1)
|
||||||
else:
|
else:
|
||||||
|
@ -50,23 +44,12 @@ def worker():
|
||||||
decr_active_workers(selected_model, backend_url)
|
decr_active_workers(selected_model, backend_url)
|
||||||
|
|
||||||
|
|
||||||
def start_workers(num_workers: int):
|
def start_workers(cluster: dict):
|
||||||
i = 0
|
i = 0
|
||||||
for _ in range(num_workers):
|
for item in cluster:
|
||||||
t = threading.Thread(target=worker)
|
for _ in range(item['concurrent_gens']):
|
||||||
t.daemon = True
|
t = threading.Thread(target=worker, args=(item['backend_url'],))
|
||||||
t.start()
|
t.daemon = True
|
||||||
i += 1
|
t.start()
|
||||||
|
i += 1
|
||||||
print(f'Started {i} inference workers.')
|
print(f'Started {i} inference workers.')
|
||||||
|
|
||||||
|
|
||||||
def need_to_wait(backend_url: str):
|
|
||||||
# We need to check the number of active workers since the streaming endpoint may be doing something.
|
|
||||||
active_workers = redis.get(f'active_gen_workers:{backend_url}', 0, dtype=int)
|
|
||||||
concurrent_gens = cluster_config.get_backend(backend_url).get('concurrent_gens', 1)
|
|
||||||
s = time.time()
|
|
||||||
while active_workers >= concurrent_gens:
|
|
||||||
time.sleep(0.01)
|
|
||||||
e = time.time()
|
|
||||||
if e - s > 0.1:
|
|
||||||
print(f'Worker was delayed {e - s} seconds.')
|
|
||||||
|
|
|
@ -25,6 +25,4 @@ def console_printer():
|
||||||
processing_count += redis.get(k, default=0, dtype=int)
|
processing_count += redis.get(k, default=0, dtype=int)
|
||||||
backends = [k for k, v in cluster_config.all().items() if v['online']]
|
backends = [k for k, v in cluster_config.all().items() if v['online']]
|
||||||
logger.info(f'REQUEST QUEUE -> Processing: {processing_count} | Queued: {len(priority_queue)} | Backends Online: {len(backends)}')
|
logger.info(f'REQUEST QUEUE -> Processing: {processing_count} | Queued: {len(priority_queue)} | Backends Online: {len(backends)}')
|
||||||
priority_queue.print_all_items()
|
|
||||||
print('============================')
|
|
||||||
time.sleep(1)
|
time.sleep(1)
|
||||||
|
|
|
@ -20,7 +20,7 @@ def cache_stats():
|
||||||
|
|
||||||
|
|
||||||
def start_background():
|
def start_background():
|
||||||
start_workers(opts.cluster_workers)
|
start_workers(opts.cluster)
|
||||||
|
|
||||||
t = Thread(target=main_background_thread)
|
t = Thread(target=main_background_thread)
|
||||||
t.daemon = True
|
t.daemon = True
|
||||||
|
|
Reference in New Issue