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local-llm-server
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Merge cluster to master #3

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cyberes merged 163 commits from cluster into master 2023-10-27 19:19:22 -06:00
Conversation 0 Commits 163 Files Changed 91 +193 -168
8 changed files with 193 additions and 168 deletions
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1
llm_server/config/config.py
View File

@ -33,7 +33,6 @@ config_default_vars = {
'openai_moderation_enabled': True,
'netdata_root': None,
'show_backends': True,
'cluster_workers': 30,
'background_homepage_cacher': True,
'openai_moderation_timeout': 5,
'prioritize_by_size': False

5
llm_server/config/load.py
View File

@ -45,12 +45,15 @@ def load_config(config_path):
opts.openai_silent_trim = config['openai_silent_trim']
opts.openai_moderation_enabled = config['openai_moderation_enabled']
opts.show_backends = config['show_backends']
opts.cluster_workers = config['cluster_workers']
opts.background_homepage_cacher = config['background_homepage_cacher']
opts.openai_moderation_timeout = config['openai_moderation_timeout']
opts.frontend_api_mode = config['frontend_api_mode']
opts.prioritize_by_size = config['prioritize_by_size']
# Scale the number of workers.
for item in config['cluster']:
opts.cluster_workers += item['concurrent_gens']
if opts.openai_expose_our_model and not opts.openai_api_key:
print('If you set openai_epose_our_model to false, you must set your OpenAI key in openai_api_key.')
sys.exit(1)

4
llm_server/opts.py
View File

@ -34,7 +34,7 @@ openai_silent_trim = False
openai_moderation_enabled = True
cluster = {}
show_backends = True
cluster_workers = 30
background_homepage_cacher = True
openai_moderation_timeout = 5
prioritize_by_size = False
prioritize_by_size = False
cluster_workers = 0

126
llm_server/routes/openai/chat_completions.py
View File

@ -8,7 +8,7 @@ from llm_server.custom_redis import redis
from . import openai_bp
from ..helpers.http import validate_json
from ..openai_request_handler import OpenAIRequestHandler
from ..queue import decr_active_workers, decrement_ip_count, priority_queue
from ..queue import priority_queue
from ... import opts
from ...database.log_to_db import log_to_db
from ...llm.generator import generator
@ -57,6 +57,7 @@ def openai_chat_completions():
else:
handler.prompt = transform_messages_to_prompt(handler.request.json['messages'])
event_id = None
response_status_code = 0
start_time = time.time()
@ -70,8 +71,10 @@ def openai_chat_completions():
'stream': True,
}
# 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 = None
if not handler.is_client_ratelimited():
# 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)
if not event:
log_to_db(
handler.client_ip,
@ -87,8 +90,15 @@ def openai_chat_completions():
)
return handler.handle_ratelimited()
# Wait for a worker to get our request and discard it.
_, _, _ = event.wait()
# Once the worker receives our streaming request, it will tell us we are ready
# to begin inference.
event_id = event.event_id
pubsub = redis.pubsub()
pubsub.subscribe(event_id)
for item in pubsub.listen():
if item['type'] == 'message' and item['data'].decode('utf-8') == 'begin':
break
time.sleep(0.1)
try:
r_headers = dict(request.headers)
@ -97,61 +107,63 @@ def openai_chat_completions():
oai_string = generate_oai_string(30)
def generate():
try:
response = generator(msg_to_backend, handler.backend_url)
generated_text = ''
partial_response = b''
for chunk in response.iter_content(chunk_size=1):
partial_response += chunk
if partial_response.endswith(b'\x00'):
json_strs = partial_response.split(b'\x00')
for json_str in json_strs:
if json_str:
try:
json_obj = json.loads(json_str.decode())
new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
generated_text = generated_text + new
except IndexError:
# ????
continue
response = generator(msg_to_backend, handler.backend_url)
generated_text = ''
partial_response = b''
for chunk in response.iter_content(chunk_size=1):
partial_response += chunk
if partial_response.endswith(b'\x00'):
json_strs = partial_response.split(b'\x00')
for json_str in json_strs:
if json_str:
try:
json_obj = json.loads(json_str.decode())
new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
generated_text = generated_text + new
except IndexError:
# ????
continue
data = {
"id": f"chatcmpl-{oai_string}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {
"content": new
},
"finish_reason": None
}
]
}
yield f'data: {json.dumps(data)}\n\n'
yield 'data: [DONE]\n\n'
end_time = time.time()
elapsed_time = end_time - start_time
log_to_db(
handler.client_ip,
handler.token,
handler.prompt,
generated_text,
elapsed_time,
handler.parameters,
r_headers,
response_status_code,
r_url,
handler.backend_url,
)
finally:
# The worker incremented it, we'll decrement it.
decrement_ip_count(handler.client_ip, 'processing_ips')
decr_active_workers(handler.selected_model, handler.backend_url)
data = {
"id": f"chatcmpl-{oai_string}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {
"content": new
},
"finish_reason": None
}
]
}
yield f'data: {json.dumps(data)}\n\n'
yield 'data: [DONE]\n\n'
end_time = time.time()
elapsed_time = end_time - start_time
log_to_db(
handler.client_ip,
handler.token,
handler.prompt,
generated_text,
elapsed_time,
handler.parameters,
r_headers,
response_status_code,
r_url,
handler.backend_url,
)
return Response(generate(), mimetype='text/event-stream')
except Exception:
traceback.print_exc()
return 'INTERNAL SERVER', 500
finally:
# After completing inference, we need to tell the worker we
# are finished.
if event_id: # may be None if ratelimited.
redis.publish(event_id, 'finished')
else:
print('event_id was None!')

123
llm_server/routes/openai/completions.py
View File

@ -8,9 +8,8 @@ from llm_server.custom_redis import redis
from . import openai_bp
from ..helpers.http import validate_json
from ..ooba_request_handler import OobaRequestHandler
from ..queue import decr_active_workers, decrement_ip_count, priority_queue
from ..queue import priority_queue
from ... import opts
from ...database.database import do_db_log
from ...database.log_to_db import log_to_db
from ...llm import get_token_count
from ...llm.generator import generator
@ -53,7 +52,6 @@ def openai_completions():
return handler.handle_ratelimited()
output = response.json['results'][0]['text']
# TODO: async/await
prompt_tokens = get_token_count(request_json_body['prompt'], handler.backend_url)
response_tokens = get_token_count(output, handler.backend_url)
running_model = redis.get('running_model', 'ERROR', dtype=str)
@ -86,6 +84,7 @@ def openai_completions():
if not opts.enable_streaming:
return 'DISABLED', 401
event_id = None
response_status_code = 0
start_time = time.time()
@ -100,8 +99,10 @@ def openai_completions():
'stream': True,
}
# 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 = None
if not handler.is_client_ratelimited():
# 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)
if not event:
log_to_db(
handler.client_ip,
@ -117,8 +118,14 @@ def openai_completions():
)
return handler.handle_ratelimited()
# Wait for a worker to get our request and discard it.
_, _, _ = event.wait()
# Wait for permission to begin.
event_id = event.event_id
pubsub = redis.pubsub()
pubsub.subscribe(event_id)
for item in pubsub.listen():
if item['type'] == 'message' and item['data'].decode('utf-8') == 'begin':
break
time.sleep(0.1)
try:
response = generator(msg_to_backend, handler.backend_url)
@ -128,61 +135,61 @@ def openai_completions():
oai_string = generate_oai_string(30)
def generate():
try:
generated_text = ''
partial_response = b''
for chunk in response.iter_content(chunk_size=1):
partial_response += chunk
if partial_response.endswith(b'\x00'):
json_strs = partial_response.split(b'\x00')
for json_str in json_strs:
if json_str:
try:
json_obj = json.loads(json_str.decode())
new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
generated_text = generated_text + new
except IndexError:
# ????
continue
generated_text = ''
partial_response = b''
for chunk in response.iter_content(chunk_size=1):
partial_response += chunk
if partial_response.endswith(b'\x00'):
json_strs = partial_response.split(b'\x00')
for json_str in json_strs:
if json_str:
try:
json_obj = json.loads(json_str.decode())
new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
generated_text = generated_text + new
except IndexError:
# ????
continue
data = {
"id": f"cmpl-{oai_string}",
"object": "text_completion",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {
"content": new
},
"finish_reason": None
}
]
}
yield f'data: {json.dumps(data)}\n\n'
yield 'data: [DONE]\n\n'
end_time = time.time()
elapsed_time = end_time - start_time
data = {
"id": f"cmpl-{oai_string}",
"object": "text_completion",
"created": int(time.time()),
"model": model,
"choices": [
{
"index": 0,
"delta": {
"content": new
},
"finish_reason": None
}
]
}
yield f'data: {json.dumps(data)}\n\n'
yield 'data: [DONE]\n\n'
end_time = time.time()
elapsed_time = end_time - start_time
log_to_db(
handler.client_ip,
handler.token,
handler.prompt,
generated_text,
elapsed_time,
handler.parameters,
r_headers,
response_status_code,
r_url,
handler.backend_url,
)
finally:
# The worker incremented it, we'll decrement it.
decrement_ip_count(handler.client_ip, 'processing_ips')
decr_active_workers(handler.selected_model, handler.backend_url)
log_to_db(
handler.client_ip,
handler.token,
handler.prompt,
generated_text,
elapsed_time,
handler.parameters,
r_headers,
response_status_code,
r_url,
handler.backend_url,
)
return Response(generate(), mimetype='text/event-stream')
except Exception:
traceback.print_exc()
return 'INTERNAL SERVER', 500
finally:
if event_id:
redis.publish(event_id, 'finished')
else:
print('event_id was None!')

7
llm_server/routes/queue.py
View File

@ -22,8 +22,6 @@ def decrement_ip_count(client_ip: str, redis_key):
class RedisPriorityQueue:
def __init__(self, name: str = 'priority_queue', db: int = 12):
self.redis = RedisCustom(name, db=db)
self.pubsub = self.redis.pubsub()
self.pubsub.subscribe('events')
def put(self, item, priority, selected_model):
event = DataEvent()
@ -36,8 +34,6 @@ class RedisPriorityQueue:
print(f'Rejecting request from {item[1]} - {ip_count} requests in progress.')
return None # reject the request
print('--->', event.event_id)
self.redis.zadd('queue', {json.dumps((item, event.event_id, selected_model)): -priority})
self.increment_ip_count(item[1], 'queued_ip_count')
return event
@ -54,17 +50,14 @@ class RedisPriorityQueue:
def print_all_items(self):
items = self.redis.zrange('queue', 0, -1)
print(items)
for item in items:
print(item.decode('utf-8'))
def increment_ip_count(self, client_ip: str, redis_key):
new_count = self.redis.hincrby(redis_key, client_ip, 1)
print(client_ip, new_count)
def decrement_ip_count(self, client_ip: str, redis_key):
new_count = self.redis.hincrby(redis_key, client_ip, -1)
print(client_ip, new_count)
if new_count <= 0:
self.redis.hdel(redis_key, client_ip)

45
llm_server/routes/v1/generate_stream.py
View File

@ -7,8 +7,9 @@ from flask import request
from . import bp
from ..helpers.http import require_api_key, validate_json
from ..ooba_request_handler import OobaRequestHandler
from ..queue import decr_active_workers, decrement_ip_count, priority_queue
from ..queue import priority_queue
from ... import opts
from ...custom_redis import redis
from ...database.log_to_db import log_to_db
from ...llm.generator import generator
from ...sock import sock
@ -94,6 +95,7 @@ def do_stream(ws, model_name):
# TODO: implement other backends
raise NotImplementedError
event_id = None
generated_text = ''
input_prompt = request_json_body['prompt']
response_status_code = 0
@ -117,16 +119,33 @@ def do_stream(ws, model_name):
'stream': True,
}
# 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 = None
if not handler.is_client_ratelimited():
# 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)
if not event:
r, _ = handler.handle_ratelimited()
err_msg = r.json['results'][0]['text']
send_err_and_quit(err_msg)
return
log_to_db(
handler.client_ip,
handler.token,
handler.request_json_body.get('prompt'),
None,
None,
handler.parameters,
request.headers,
response_status_code,
request.url,
handler.backend_url,
)
return handler.handle_ratelimited()
# Wait for a worker to get our request and discard it.
_, _, _ = event.wait()
# Wait for permission to begin.
event_id = event.event_id
pubsub = redis.pubsub()
pubsub.subscribe(event_id)
for item in pubsub.listen():
if item['type'] == 'message' and item['data'].decode('utf-8') == 'begin':
break
time.sleep(0.1)
try:
response = generator(llm_request, handler.backend_url)
@ -195,9 +214,11 @@ def do_stream(ws, model_name):
}))
# used to log here
finally:
# The worker incremented it, we'll decrement it.
decrement_ip_count(handler.client_ip, 'processing_ips')
decr_active_workers(handler.selected_model, handler.backend_url)
if event_id:
redis.publish(event_id, 'finished')
else:
print('event_id was None!')
try:
ws.send(json.dumps({
'event': 'stream_end',

50
llm_server/workers/inferencer.py
View File

@ -19,27 +19,30 @@ def worker():
if not selected_model:
selected_model = backend_info['model']
# This wait time will be "invisible", meaning the worker may as
# well be still waiting to get an item from the queue.
need_to_wait(backend_url)
increment_ip_count(client_ip, 'processing_ips')
incr_active_workers(selected_model, backend_url)
print('<---', event_id)
if not request_json_body:
# This was a dummy request from the websocket handlers.
# We're going to let the websocket handler decrement
# processing_ips and active_gen_workers.
event = DataEvent(event_id)
event.set((True, None, None))
continue
try:
success, response, error_msg = generator(request_json_body, backend_url)
event = DataEvent(event_id)
event.set((success, response, error_msg))
if not request_json_body:
# This was a dummy request from the streaming handlers.
# The worker will let the handler do the streaming instead
# of the worker. The worker will block until the handler
# is finished. Since a lot of ratelimiting and stats are
# based off the number of active workers, we must keep
# the generation based off the workers.
pubsub = redis.pubsub()
pubsub.subscribe(event_id)
redis.publish(event_id, 'begin')
for item in pubsub.listen():
if item['type'] == 'message' and item['data'].decode('utf-8') == 'finished':
# Once the handler is complete, move on.
break
time.sleep(0.1)
else:
# Normal inference (not streaming).
success, response, error_msg = generator(request_json_body, backend_url)
event = DataEvent(event_id)
event.set((success, response, error_msg))
finally:
decrement_ip_count(client_ip, 'processing_ips')
decr_active_workers(selected_model, backend_url)
@ -53,16 +56,3 @@ def start_workers(num_workers: int):
t.start()
i += 1
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()
print(active_workers)
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.')