mvp

daemon.py

@@ -1,22 +1,12 @@
-import time
-
-from llm_server.custom_redis import redis
-
-try:
-    import gevent.monkey
-
-    gevent.monkey.patch_all()
-except ImportError:
-    pass
-
 import os
 import sys
+import time
 from pathlib import Path
 
 from llm_server.config.load import load_config
+from llm_server.custom_redis import redis
 from llm_server.database.create import create_db
-
+from llm_server.workers.threader import start_background
-from llm_server.workers.app import start_background
 
 script_path = os.path.dirname(os.path.realpath(__file__))
 config_path_environ = os.getenv("CONFIG_PATH")
@@ -29,7 +19,7 @@ if __name__ == "__main__":
     flushed_keys = redis.flush()
     print('Flushed', len(flushed_keys), 'keys from Redis.')
 
-    success, config, msg = load_config(config_path, script_path)
+    success, config, msg = load_config(config_path)
     if not success:
         print('Failed to load config:', msg)
         sys.exit(1)

llm_server/cluster/backend.py

@@ -0,0 +1,71 @@
+from llm_server.cluster.redis_config_cache import RedisClusterStore
+from llm_server.cluster.redis_cycle import redis_cycle
+from llm_server.cluster.stores import redis_running_models
+from llm_server.llm.info import get_running_model
+
+
+def test_backend(backend_url: str, mode: str):
+    running_model, err = get_running_model(backend_url, mode)
+    if not running_model:
+        return False
+    return True
+
+
+def get_backends():
+    cluster_config = RedisClusterStore('cluster_config')
+    backends = cluster_config.all()
+    result = {}
+    for k, v in backends.items():
+        b = cluster_config.get_backend(k)
+        status = b['online']
+        priority = b['priority']
+        result[k] = {'status': status, 'priority': priority}
+    online_backends = sorted(
+        ((url, info) for url, info in backends.items() if info['online']),
+        key=lambda kv: -kv[1]['priority'],
+        reverse=True
+    )
+    offline_backends = sorted(
+        ((url, info) for url, info in backends.items() if not info['online']),
+        key=lambda kv: -kv[1]['priority'],
+        reverse=True
+    )
+    return [url for url, info in online_backends], [url for url, info in offline_backends]
+
+
+def get_a_cluster_backend():
+    """
+    Get a backend from Redis. If there are no online backends, return None.
+    """
+    online, offline = get_backends()
+    cycled = redis_cycle('backend_cycler')
+    c = cycled.copy()
+    for i in range(len(cycled)):
+        if cycled[i] in offline:
+            del c[c.index(cycled[i])]
+    if len(c):
+        return c[0]
+    else:
+        return None
+
+
+def get_backends_from_model(model_name: str):
+    cluster_config = RedisClusterStore('cluster_config')
+    a = cluster_config.all()
+    matches = []
+    for k, v in a.items():
+        if v['online'] and v['running_model'] == model_name:
+            matches.append(k)
+    return matches
+
+
+def purge_backend_from_running_models(backend_url: str):
+    keys = redis_running_models.keys()
+    pipeline = redis_running_models.pipeline()
+    for model in keys:
+        pipeline.srem(model, backend_url)
+    pipeline.execute()
+
+
+def is_valid_model(model_name: str):
+    return redis_running_models.exists(model_name)

llm_server/cluster/cluster_config.py

@@ -0,0 +1,3 @@
+from llm_server.cluster.redis_config_cache import RedisClusterStore
+
+cluster_config = RedisClusterStore('cluster_config')

llm_server/cluster/funcs/backend.py

@@ -1,26 +0,0 @@
-from llm_server.cluster.redis_config_cache import RedisClusterStore
-from llm_server.llm.info import get_running_model
-
-
-def test_backend(backend_url: str):
-    running_model, err = get_running_model(backend_url)
-    if not running_model:
-        return False
-    return True
-
-
-def get_best_backends():
-    cluster_config = RedisClusterStore('cluster_config')
-    backends = cluster_config.all()
-    result = {}
-    for k, v in backends.items():
-        b = cluster_config.get_backend(k)
-        status = b['online']
-        priority = b['priority']
-        result[k] = {'status': status, 'priority': priority}
-    online_backends = sorted(
-        ((url, info) for url, info in backends.items() if info['online']),
-        key=lambda kv: kv[1]['priority'],
-        reverse=True
-    )
-    return [url for url, info in online_backends]

llm_server/cluster/redis_config_cache.py

@@ -1,3 +1,4 @@
+import hashlib
 import pickle
 
 from llm_server.custom_redis import RedisCustom
@@ -13,14 +14,17 @@ class RedisClusterStore:
 
     def load(self, config: dict):
         for k, v in config.items():
-            self.set_backend(k, v)
+            self.add_backend(k, v)
 
-    def set_backend(self, name: str, values: dict):
+    def add_backend(self, name: str, values: dict):
         self.config_redis.hset(name, mapping={k: pickle.dumps(v) for k, v in values.items()})
         self.set_backend_value(name, 'online', False)
+        h = hashlib.sha256(name.encode('utf-8')).hexdigest()
+        self.set_backend_value(name, 'hash', f'{h[:8]}-{h[-8:]}')
 
-    def set_backend_value(self, key: str, name: str, value):
+    def set_backend_value(self, backend: str, key: str, value):
-        self.config_redis.hset(key, name, pickle.dumps(value))
+        # By storing the value as a pickle we don't have to cast anything when getting the value from Redis.
+        self.config_redis.hset(backend, key, pickle.dumps(value))
 
     def get_backend(self, name: str):
         r = self.config_redis.hgetall(name)

llm_server/cluster/redis_cycle.py

@@ -0,0 +1,21 @@
+import redis
+
+r = redis.Redis(host='localhost', port=6379, db=9)
+
+
+def redis_cycle(list_name):
+    while True:
+        pipe = r.pipeline()
+        pipe.lpop(list_name)
+        popped_element = pipe.execute()[0]
+        if popped_element is None:
+            return None
+        r.rpush(list_name, popped_element)
+        new_list = r.lrange(list_name, 0, -1)
+        return [x.decode('utf-8') for x in new_list]
+
+
+def load_backend_cycle(list_name: str, elements: list):
+    r.delete(list_name)
+    for element in elements:
+        r.rpush(list_name, element)

llm_server/cluster/stores.py

@@ -0,0 +1,3 @@
+from llm_server.custom_redis import RedisCustom
+
+redis_running_models = RedisCustom('running_models')

llm_server/cluster/worker.py

@@ -1,10 +1,10 @@
-import time
+from datetime import datetime
 from threading import Thread
 
-from llm_server.cluster.funcs.backend import test_backend
+from llm_server.cluster.backend import purge_backend_from_running_models, test_backend
-from llm_server.cluster.redis_config_cache import RedisClusterStore
+from llm_server.cluster.cluster_config import cluster_config
-
+from llm_server.cluster.stores import redis_running_models
-cluster_config = RedisClusterStore('cluster_config')
+from llm_server.llm.info import get_running_model
 
 
 def cluster_worker():
@@ -16,10 +16,16 @@ def cluster_worker():
             threads.append(thread)
         for thread in threads:
             thread.join()
-        time.sleep(10)
 
 
 def check_backend(n, v):
     # Check if backends are online
-    online = test_backend(v['backend_url'])
+    # TODO: also have test_backend() get the uptime
+    online = test_backend(v['backend_url'], v['mode'])
+    if online:
+        running_model, err = get_running_model(v['backend_url'], v['mode'])
+        if not err:
+            cluster_config.set_backend_value(n, 'running_model', running_model)
+            purge_backend_from_running_models(n)
+            redis_running_models.sadd(running_model, n)
     cluster_config.set_backend_value(n, 'online', online)

llm_server/config/config.py

@@ -32,7 +32,8 @@ config_default_vars = {
     'openai_org_name': 'OpenAI',
     'openai_silent_trim': False,
     'openai_moderation_enabled': True,
-    'netdata_root': None
+    'netdata_root': None,
+    'show_backends': True,
 }
 config_required_vars = ['token_limit', 'concurrent_gens', 'mode', 'llm_middleware_name']
 

llm_server/config/load.py

@@ -52,6 +52,7 @@ def load_config(config_path):
     opts.openai_org_name = config['openai_org_name']
     opts.openai_silent_trim = config['openai_silent_trim']
     opts.openai_moderation_enabled = config['openai_moderation_enabled']
+    opts.show_backends = config['show_backends']
 
     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.')

llm_server/custom_redis.py

@@ -1,13 +1,13 @@
 import pickle
 import sys
 import traceback
-from typing import Callable, List, Mapping, Union, Optional
+from typing import Callable, List, Mapping, Optional, Union
 
 import redis as redis_pkg
 import simplejson as json
 from flask_caching import Cache
 from redis import Redis
-from redis.typing import AnyKeyT, EncodableT, ExpiryT, FieldT, KeyT, ZScoreBoundT, PatternT
+from redis.typing import AnyKeyT, EncodableT, ExpiryT, FieldT, KeyT, PatternT, ZScoreBoundT
 
 flask_cache = Cache(config={'CACHE_TYPE': 'RedisCache', 'CACHE_REDIS_URL': 'redis://localhost:6379/0', 'CACHE_KEY_PREFIX': 'local_llm_flask'})
 
@@ -35,12 +35,12 @@ class RedisCustom:
     def set(self, key, value, ex: Union[ExpiryT, None] = None):
         return self.redis.set(self._key(key), value, ex=ex)
 
-    def get(self, key, dtype=None, default=None):
+    def get(self, key, default=None, dtype=None):
-        """
+        # TODO: use pickle
-        :param key:
+        import inspect
-        :param dtype: convert to this type
+        if inspect.isclass(default):
-        :return:
+            raise Exception
-        """
+
         d = self.redis.get(self._key(key))
         if dtype and d:
             try:
@@ -153,11 +153,23 @@ class RedisCustom:
         keys = []
         for key in raw_keys:
             p = key.decode('utf-8').split(':')
-            if len(p) > 2:
+            if len(p) >= 2:
+                # Delete prefix
                 del p[0]
-            keys.append(':'.join(p))
+            k = ':'.join(p)
+            if k != '____':
+                keys.append(k)
         return keys
 
+    def pipeline(self, transaction=True, shard_hint=None):
+        return self.redis.pipeline(transaction, shard_hint)
+
+    def exists(self, *names: KeyT):
+        n = []
+        for name in names:
+            n.append(self._key(name))
+        return self.redis.exists(*n)
+
     def set_dict(self, key: Union[list, dict], dict_value, ex: Union[ExpiryT, None] = None):
         return self.set(key, json.dumps(dict_value), ex=ex)
 
@@ -174,7 +186,7 @@ class RedisCustom:
     def getp(self, name: str):
         r = self.redis.get(name)
         if r:
-            return pickle.load(r)
+            return pickle.loads(r)
         return r
 
     def flush(self):

llm_server/database/database.py

@@ -1,15 +1,19 @@
 import json
 import time
 import traceback
+from threading import Thread
 
 import llm_server
 from llm_server import opts
+from llm_server.custom_redis import redis
 from llm_server.database.conn import database
 from llm_server.llm.vllm import tokenize
-from llm_server.custom_redis import redis
 
 
-def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backend_response_code, request_url, response_tokens: int = None, is_error: bool = False):
+def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backend_response_code, request_url, cluster_backend, response_tokens: int = None, is_error: bool = False):
+    def background_task():
+        nonlocal ip, token, prompt, response, gen_time, parameters, headers, backend_response_code, request_url, cluster_backend, response_tokens, is_error
+        # Try not to shove JSON into the database.
         if isinstance(response, dict) and response.get('results'):
             response = response['results'][0]['text']
         try:
@@ -52,10 +56,15 @@ def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backe
             (ip, token, model, backend_mode, backend_url, request_url, generation_time, prompt, prompt_tokens, response, response_tokens, response_status, parameters, headers, timestamp)
             VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
             """,
-                       (ip, token, running_model, opts.mode, opts.backend_url, request_url, gen_time, prompt, prompt_tokens, response, response_tokens, backend_response_code, json.dumps(parameters), json.dumps(headers), timestamp))
+                           (ip, token, running_model, opts.mode, cluster_backend, request_url, gen_time, prompt, prompt_tokens, response, response_tokens, backend_response_code, json.dumps(parameters), json.dumps(headers), timestamp))
         finally:
             cursor.close()
 
+    # TODO: use async/await instead of threads
+    thread = Thread(target=background_task)
+    thread.start()
+    thread.join()
+
 
 def is_valid_api_key(api_key):
     cursor = database.cursor()

llm_server/helpers.py

@@ -60,7 +60,7 @@ def round_up_base(n, base):
 
 
 def auto_set_base_client_api(request):
-    http_host = redis.get('http_host', str)
+    http_host = redis.get('http_host', dtype=str)
     host = request.headers.get("Host")
     if http_host and not re.match(r'((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.?\b){4}', http_host):
         # If the current http_host is not an IP, don't do anything.

llm_server/integer.py

@@ -1,12 +0,0 @@
-import threading
-
-
-class ThreadSafeInteger:
-    def __init__(self, value=0):
-        self.value = value
-        self._value_lock = threading.Lock()
-
-    def increment(self):
-        with self._value_lock:
-            self.value += 1
-            return self.value

llm_server/llm/__init__.py

@@ -3,7 +3,7 @@ from llm_server.custom_redis import redis
 
 
 def get_token_count(prompt: str):
-    backend_mode = redis.get('backend_mode', str)
+    backend_mode = redis.get('backend_mode', dtype=str)
     if backend_mode == 'vllm':
         return vllm.tokenize(prompt)
     elif backend_mode == 'ooba':

llm_server/llm/generator.py

@@ -1,14 +1,14 @@
 from llm_server import opts
 
 
-def generator(request_json_body):
+def generator(request_json_body, cluster_backend):
     if opts.mode == 'oobabooga':
         # from .oobabooga.generate import generate
         # return generate(request_json_body)
         raise NotImplementedError
     elif opts.mode == 'vllm':
         from .vllm.generate import generate
-        r = generate(request_json_body)
+        r = generate(request_json_body, cluster_backend)
         return r
     else:
         raise Exception

llm_server/llm/info.py

@@ -3,19 +3,15 @@ import requests
 from llm_server import opts
 
 
-def get_running_model(backend_url: str):
+def get_running_model(backend_url: str, mode: str):
-    # TODO: remove this once we go to Redis
+    if mode == 'ooba':
-    if not backend_url:
-        backend_url = opts.backend_url
-
-    if opts.mode == 'oobabooga':
         try:
             backend_response = requests.get(f'{backend_url}/api/v1/model', timeout=opts.backend_request_timeout, verify=opts.verify_ssl)
             r_json = backend_response.json()
             return r_json['result'], None
         except Exception as e:
             return False, e
-    elif opts.mode == 'vllm':
+    elif mode == 'vllm':
         try:
             backend_response = requests.get(f'{backend_url}/model', timeout=opts.backend_request_timeout, verify=opts.verify_ssl)
             r_json = backend_response.json()

llm_server/llm/llm_backend.py

@@ -40,6 +40,6 @@ class LLMBackend:
     def validate_prompt(self, prompt: str) -> Tuple[bool, Union[str, None]]:
         prompt_len = get_token_count(prompt)
         if prompt_len > opts.context_size - 10:
-            model_name = redis.get('running_model', str, 'NO MODEL ERROR')
+            model_name = redis.get('running_model', 'NO MODEL ERROR', dtype=str)
             return False, f'Token indices sequence length is longer than the specified maximum sequence length for this model ({prompt_len} > {opts.context_size}, model: {model_name}). Please lower your context size'
         return True, None

llm_server/llm/openai/transform.py

@@ -34,7 +34,7 @@ def build_openai_response(prompt, response, model=None):
     # TODO: async/await
     prompt_tokens = llm_server.llm.get_token_count(prompt)
     response_tokens = llm_server.llm.get_token_count(response)
-    running_model = redis.get('running_model', str, 'ERROR')
+    running_model = redis.get('running_model', 'ERROR', dtype=str)
 
     response = make_response(jsonify({
         "id": f"chatcmpl-{generate_oai_string(30)}",
@@ -57,7 +57,7 @@ def build_openai_response(prompt, response, model=None):
         }
     }), 200)
 
-    stats = redis.get('proxy_stats', dict)
+    stats = redis.get('proxy_stats', dtype=dict)
     if stats:
         response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
     return response

llm_server/llm/vllm/generate.py

@@ -49,7 +49,7 @@ def transform_to_text(json_request, api_response):
 
     prompt_tokens = len(llm_server.llm.get_token_count(prompt))
     completion_tokens = len(llm_server.llm.get_token_count(text))
-    running_model = redis.get('running_model', str, 'ERROR')
+    running_model = redis.get('running_model', 'ERROR', dtype=str)
 
     # https://platform.openai.com/docs/api-reference/making-requests?lang=python
     return {
@@ -82,9 +82,9 @@ def transform_prompt_to_text(prompt: list):
     return text.strip('\n')
 
 
-def handle_blocking_request(json_data: dict):
+def handle_blocking_request(json_data: dict, cluster_backend):
     try:
-        r = requests.post(f'{opts.backend_url}/generate', json=prepare_json(json_data), verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
+        r = requests.post(f'{cluster_backend}/generate', json=prepare_json(json_data), verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
     except requests.exceptions.ReadTimeout:
         print(f'Failed to reach VLLM inference endpoint - request to backend timed out')
         return False, None, 'Request to backend timed out'
@@ -97,11 +97,11 @@ def handle_blocking_request(json_data: dict):
     return True, r, None
 
 
-def generate(json_data: dict):
+def generate(json_data: dict, cluster_backend):
     if json_data.get('stream'):
         try:
-            return requests.post(f'{opts.backend_url}/generate', json=prepare_json(json_data), stream=True, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
+            return requests.post(f'{cluster_backend}/generate', json=prepare_json(json_data), stream=True, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
         except Exception as e:
             print(f'Failed to reach VLLM inference endpoint -', f'{e.__class__.__name__}: {e}')
     else:
-        return handle_blocking_request(json_data)
+        return handle_blocking_request(json_data, cluster_backend)

llm_server/llm/vllm/vllm_backend.py

@@ -19,17 +19,9 @@ class VLLMBackend(LLMBackend):
             # Failsafe
             backend_response = ''
 
-        r_url = request.url
+        log_prompt(ip=client_ip, token=token, prompt=prompt, response=backend_response, gen_time=elapsed_time, parameters=parameters, headers=headers, backend_response_code=response_status_code, request_url=request.url,
-
-        def background_task():
-            log_prompt(ip=client_ip, token=token, prompt=prompt, response=backend_response, gen_time=elapsed_time, parameters=parameters, headers=headers, backend_response_code=response_status_code, request_url=r_url,
                    response_tokens=response_json_body.get('details', {}).get('generated_tokens'))
 
-        # TODO: use async/await instead of threads
-        thread = threading.Thread(target=background_task)
-        thread.start()
-        thread.join()
-
         return jsonify({'results': [{'text': backend_response}]}), 200
 
     def get_parameters(self, parameters) -> Tuple[dict | None, str | None]:

llm_server/opts.py

@@ -2,7 +2,6 @@
 
 # TODO: rewrite the config system so I don't have to add every single config default here
 
-running_model = 'ERROR'
 concurrent_gens = 3
 mode = 'oobabooga'
 backend_url = None
@@ -38,3 +37,4 @@ openai_org_name = 'OpenAI'
 openai_silent_trim = False
 openai_moderation_enabled = True
 cluster = {}
+show_backends = True

llm_server/pre_fork.py

@@ -7,7 +7,7 @@ from llm_server.routes.v1.generate_stats import generate_stats
 
 
 def server_startup(s):
-    if not redis.get('daemon_started', bool):
+    if not redis.get('daemon_started', dtype=bool):
         print('Could not find the key daemon_started in Redis. Did you forget to start the daemon process?')
         sys.exit(1)
 

llm_server/routes/helpers/client.py

@@ -1,10 +1,14 @@
+from llm_server.cluster.cluster_config import cluster_config
 from llm_server.custom_redis import redis
 
 
-def format_sillytavern_err(msg: str, level: str = 'info'):
+def format_sillytavern_err(msg: str, backend_url: str, level: str = 'info'):
-    http_host = redis.get('http_host', str)
+    cluster_backend_hash = cluster_config.get_backend_handler(backend_url)['hash']
+    http_host = redis.get('http_host', dtype=str)
     return f"""```
 === MESSAGE FROM LLM MIDDLEWARE AT {http_host} ===
 -> {level.upper()} <-
 {msg}
+
+BACKEND HASH: {cluster_backend_hash}
 ```"""

llm_server/routes/ooba_request_handler.py

@@ -31,7 +31,7 @@ class OobaRequestHandler(RequestHandler):
         msg = f'Ratelimited: you are only allowed to have {opts.simultaneous_requests_per_ip} simultaneous requests at a time. Please complete your other requests before sending another.'
         backend_response = self.handle_error(msg)
         if do_log:
-            log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, is_error=True)
+            log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.cluster_backend, is_error=True)
         return backend_response[0], 200  # We only return the response from handle_error(), not the error code
 
     def handle_error(self, error_msg: str, error_type: str = 'error') -> Tuple[flask.Response, int]:
@@ -40,7 +40,7 @@ class OobaRequestHandler(RequestHandler):
             # TODO: how to format this
             response_msg = error_msg
         else:
-            response_msg = format_sillytavern_err(error_msg, error_type)
+            response_msg = format_sillytavern_err(error_msg, error_type, self.cluster_backend)
 
         return jsonify({
             'results': [{'text': response_msg}]

llm_server/routes/openai/chat_completions.py

@@ -5,11 +5,12 @@ import traceback
 
 from flask import Response, jsonify, request
 
-from . import openai_bp
 from llm_server.custom_redis import redis
+from . import openai_bp
 from ..helpers.http import validate_json
 from ..openai_request_handler import OpenAIRequestHandler
 from ... import opts
+from ...cluster.backend import get_a_cluster_backend
 from ...database.database import log_prompt
 from ...llm.generator import generator
 from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt
@@ -48,10 +49,11 @@ def openai_chat_completions():
                     'stream': True,
                 }
                 try:
-                    response = generator(msg_to_backend)
+                    cluster_backend = get_a_cluster_backend()
+                    response = generator(msg_to_backend, cluster_backend)
                     r_headers = dict(request.headers)
                     r_url = request.url
-                    model = redis.get('running_model', str, 'ERROR') if opts.openai_expose_our_model else request_json_body.get('model')
+                    model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
                     oai_string = generate_oai_string(30)
 
                     def generate():
@@ -94,7 +96,7 @@ def openai_chat_completions():
 
                         def background_task():
                             generated_tokens = tokenize(generated_text)
-                            log_prompt(handler.client_ip, handler.token, handler.prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, response_tokens=generated_tokens)
+                            log_prompt(handler.client_ip, handler.token, handler.prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, cluster_backend, response_tokens=generated_tokens)
 
                         # TODO: use async/await instead of threads
                         thread = threading.Thread(target=background_task)

llm_server/routes/openai/completions.py

@@ -29,7 +29,7 @@ def openai_completions():
             # TODO: async/await
             prompt_tokens = get_token_count(request_json_body['prompt'])
             response_tokens = get_token_count(output)
-            running_model = redis.get('running_model', str, 'ERROR')
+            running_model = redis.get('running_model', 'ERROR', dtype=str)
 
             response = make_response(jsonify({
                 "id": f"cmpl-{generate_oai_string(30)}",
@@ -51,7 +51,7 @@ def openai_completions():
                 }
             }), 200)
 
-            stats = redis.get('proxy_stats', dict)
+            stats = redis.get('proxy_stats', dtype=dict)
             if stats:
                 response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
             return response

llm_server/routes/openai/models.py

@@ -7,6 +7,7 @@ from . import openai_bp
 from llm_server.custom_redis import ONE_MONTH_SECONDS, flask_cache, redis
 from ..stats import server_start_time
 from ... import opts
+from ...cluster.backend import get_a_cluster_backend
 from ...helpers import jsonify_pretty
 from ...llm.info import get_running_model
 
@@ -22,7 +23,7 @@ def openai_list_models():
             'type': error.__class__.__name__
         }), 500  # return 500 so Cloudflare doesn't intercept us
     else:
-        running_model = redis.get('running_model', str, 'ERROR')
+        running_model = redis.get('running_model', 'ERROR', dtype=str)
         oai = fetch_openai_models()
         r = []
         if opts.openai_expose_our_model:

llm_server/routes/queue.py

@@ -93,6 +93,6 @@ def incr_active_workers():
 
 def decr_active_workers():
     redis.decr('active_gen_workers')
-    new_count = redis.get('active_gen_workers', int, 0)
+    new_count = redis.get('active_gen_workers', 0, dtype=int)
     if new_count < 0:
         redis.set('active_gen_workers', 0)

llm_server/routes/request_handler.py

@@ -5,13 +5,15 @@ import flask
 from flask import Response, request
 
 from llm_server import opts
+from llm_server.cluster.backend import get_a_cluster_backend
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.custom_redis import redis
 from llm_server.database.conn import database
 from llm_server.database.database import log_prompt
 from llm_server.helpers import auto_set_base_client_api
 from llm_server.llm.oobabooga.ooba_backend import OobaboogaBackend
 from llm_server.llm.vllm.vllm_backend import VLLMBackend
 from llm_server.routes.auth import parse_token
-from llm_server.custom_redis import redis
 from llm_server.routes.helpers.http import require_api_key, validate_json
 from llm_server.routes.queue import priority_queue
 
@@ -35,7 +37,9 @@ class RequestHandler:
         self.client_ip = self.get_client_ip()
         self.token = self.get_auth_token()
         self.token_priority, self.token_simultaneous_ip = self.get_token_ratelimit()
-        self.backend = get_backend()
+        self.cluster_backend = get_a_cluster_backend()
+        self.cluster_backend_info = cluster_config.get_backend(self.cluster_backend)
+        self.backend = get_backend_handler(self.cluster_backend)
         self.parameters = None
         self.used = False
         redis.zadd('recent_prompters', {self.client_ip: time.time()})
@@ -119,7 +123,7 @@ class RequestHandler:
             backend_response = self.handle_error(combined_error_message, 'Validation Error')
 
             if do_log:
-                log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), 0, self.parameters, dict(self.request.headers), 0, self.request.url, is_error=True)
+                log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), 0, self.parameters, dict(self.request.headers), 0, self.request.url, self.cluster_backend, is_error=True)
             return False, backend_response
         return True, (None, 0)
 
@@ -131,7 +135,7 @@ class RequestHandler:
             request_valid, invalid_response = self.validate_request(prompt, do_log=True)
             if not request_valid:
                 return (False, None, None, 0), invalid_response
-            event = priority_queue.put((llm_request, self.client_ip, self.token, self.parameters), self.token_priority)
+            event = priority_queue.put((llm_request, self.client_ip, self.token, self.parameters, self.cluster_backend), self.token_priority)
         else:
             event = None
 
@@ -160,7 +164,7 @@ class RequestHandler:
             else:
                 error_msg = error_msg.strip('.') + '.'
             backend_response = self.handle_error(error_msg)
-            log_prompt(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), response_status_code, self.request.url, is_error=True)
+            log_prompt(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), response_status_code, self.request.url, self.cluster_backend, is_error=True)
             return (False, None, None, 0), backend_response
 
         # ===============================================
@@ -180,7 +184,7 @@ class RequestHandler:
         if return_json_err:
             error_msg = 'The backend did not return valid JSON.'
             backend_response = self.handle_error(error_msg)
-            log_prompt(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), elapsed_time, self.parameters, dict(self.request.headers), response_status_code, self.request.url, is_error=True)
+            log_prompt(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), elapsed_time, self.parameters, dict(self.request.headers), response_status_code, self.request.url, self.cluster_backend, is_error=True)
             return (False, None, None, 0), backend_response
 
         # ===============================================
@@ -214,10 +218,10 @@ class RequestHandler:
         raise NotImplementedError
 
 
-def get_backend():
+def get_backend_handler(mode):
-    if opts.mode == 'oobabooga':
+    if mode == 'oobabooga':
         return OobaboogaBackend()
-    elif opts.mode == 'vllm':
+    elif mode == 'vllm':
         return VLLMBackend()
     else:
         raise Exception

llm_server/routes/stats.py

@@ -2,32 +2,9 @@ from datetime import datetime
 
 from llm_server.custom_redis import redis
 
-# proompters_5_min = 0
-# concurrent_semaphore = Semaphore(concurrent_gens)
-
 server_start_time = datetime.now()
 
 
-# TODO: do I need this?
-# def elapsed_times_cleanup():
-#     global wait_in_queue_elapsed
-#     while True:
-#         current_time = time.time()
-#         with wait_in_queue_elapsed_lock:
-#             global wait_in_queue_elapsed
-#             wait_in_queue_elapsed = [(end_time, elapsed_time) for end_time, elapsed_time in wait_in_queue_elapsed if current_time - end_time <= 60]
-#         time.sleep(1)
-
-
-def calculate_avg_gen_time():
-    # Get the average generation time from Redis
-    average_generation_time = redis.get('average_generation_time')
-    if average_generation_time is None:
-        return 0
-    else:
-        return float(average_generation_time)
-
-
 def get_total_proompts():
     count = redis.get('proompts')
     if count is None:

llm_server/routes/v1/generate_stats.py

@@ -2,11 +2,12 @@ import time
 from datetime import datetime
 
 from llm_server import opts
+from llm_server.cluster.backend import get_a_cluster_backend, test_backend
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.custom_redis import redis
 from llm_server.database.database import get_distinct_ips_24h, sum_column
 from llm_server.helpers import deep_sort, round_up_base
 from llm_server.llm.info import get_running_model
-from llm_server.netdata import get_power_states
-from llm_server.custom_redis import redis
 from llm_server.routes.queue import priority_queue
 from llm_server.routes.stats import get_active_gen_workers, get_total_proompts, server_start_time
 
@@ -33,52 +34,43 @@ def calculate_wait_time(gen_time_calc, proompters_in_queue, concurrent_gens, act
         return gen_time_calc
 
 
-# TODO: have routes/__init__.py point to the latest API version generate_stats()
-
 def generate_stats(regen: bool = False):
     if not regen:
-        c = redis.get('proxy_stats', dict)
+        c = redis.get('proxy_stats', dtype=dict)
         if c:
             return c
 
-    model_name, error = get_running_model()  # will return False when the fetch fails
+    default_backend_url = get_a_cluster_backend()
-    if isinstance(model_name, bool):
+    default_backend_info = cluster_config.get_backend(default_backend_url)
-        online = False
+    if not default_backend_info.get('mode'):
-    else:
+        # TODO: remove
-        online = True
+        print('DAEMON NOT FINISHED STARTING')
-        redis.set('running_model', model_name)
+        return
+    base_client_api = redis.get('base_client_api', dtype=str)
+    proompters_5_min = len(redis.zrangebyscore('recent_prompters', time.time() - 5 * 60, '+inf'))
+    average_generation_elapsed_sec = redis.get('average_generation_elapsed_sec', 0)
 
-    # t = elapsed_times.copy()  # copy since we do multiple operations and don't want it to change
+    online = test_backend(default_backend_url, default_backend_info['mode'])
-    # if len(t) == 0:
+    if online:
-    #     estimated_wait = 0
+        running_model, err = get_running_model(default_backend_url, default_backend_info['mode'])
-    # else:
+        cluster_config.set_backend_value(default_backend_url, 'running_model', running_model)
-    #     waits = [elapsed for end, elapsed in t]
+    else:
-    #     estimated_wait = int(sum(waits) / len(waits))
+        running_model = None
 
     active_gen_workers = get_active_gen_workers()
     proompters_in_queue = len(priority_queue)
 
-    # This is so wildly inaccurate it's disabled until I implement stats reporting into VLLM.
+    # This is so wildly inaccurate it's disabled.
     # estimated_avg_tps = redis.get('estimated_avg_tps', float, default=0)
 
-    average_generation_time = redis.get('average_generation_elapsed_sec', float, default=0)
+    # TODO: make this for the currently selected backend
-    estimated_wait_sec = calculate_wait_time(average_generation_time, proompters_in_queue, opts.concurrent_gens, active_gen_workers)
+    estimated_wait_sec = calculate_wait_time(average_generation_elapsed_sec, proompters_in_queue, opts.concurrent_gens, active_gen_workers)
-
-    if opts.netdata_root:
-        netdata_stats = {}
-        power_states = get_power_states()
-        for gpu, power_state in power_states.items():
-            netdata_stats[gpu] = {
-                'power_state': power_state,
-                # 'wh_wasted_1_hr': get_gpu_wh(int(gpu.strip('gpu')))
-            }
-    else:
-        netdata_stats = {}
-
-    base_client_api = redis.get('base_client_api', str)
-    proompters_5_min = len(redis.zrangebyscore('recent_prompters', time.time() - 5 * 60, '+inf'))
 
     output = {
+        'default': {
+            'model': running_model,
+            'backend': default_backend_info['hash'],
+        },
         'stats': {
             'proompters': {
                 '5_min': proompters_5_min,
@@ -86,9 +78,10 @@ def generate_stats(regen: bool = False):
             },
             'proompts_total': get_total_proompts() if opts.show_num_prompts else None,
             'uptime': int((datetime.now() - server_start_time).total_seconds()) if opts.show_uptime else None,
-            'average_generation_elapsed_sec': int(average_generation_time),
+            'average_generation_elapsed_sec': int(average_generation_elapsed_sec),
             # 'estimated_avg_tps': estimated_avg_tps,
             'tokens_generated': sum_column('prompts', 'response_tokens') if opts.show_total_output_tokens else None,
+            'num_backends': len(cluster_config.all()) if opts.show_backends else None,
         },
         'online': online,
         'endpoints': {
@@ -103,10 +96,7 @@ def generate_stats(regen: bool = False):
         'timestamp': int(time.time()),
         'config': {
             'gatekeeper': 'none' if opts.auth_required is False else 'token',
-            'context_size': opts.context_size,
             'concurrent': opts.concurrent_gens,
-            'model': opts.manual_model_name if opts.manual_model_name else model_name,
-            'mode': opts.mode,
             'simultaneous_requests_per_ip': opts.simultaneous_requests_per_ip,
         },
         'keys': {
@@ -114,8 +104,41 @@ def generate_stats(regen: bool = False):
             'anthropicKeys': '∞',
         },
         'backend_info': redis.get_dict('backend_info') if opts.show_backend_info else None,
+    }
+
+    if opts.show_backends:
+        for backend_url, v in cluster_config.all().items():
+            backend_info = cluster_config.get_backend(backend_url)
+            if not backend_info['online']:
+                continue
+
+            # TODO: have this fetch the data from VLLM which will display GPU utalization
+            # if opts.netdata_root:
+            #     netdata_stats = {}
+            #     power_states = get_power_states()
+            #     for gpu, power_state in power_states.items():
+            #         netdata_stats[gpu] = {
+            #             'power_state': power_state,
+            #             # 'wh_wasted_1_hr': get_gpu_wh(int(gpu.strip('gpu')))
+            #         }
+            # else:
+            #     netdata_stats = {}
+            netdata_stats = {}
+
+            # TODO: use value returned by VLLM backend here
+            # backend_uptime = int((datetime.now() - backend_info['start_time']).total_seconds()) if opts.show_uptime else None
+            backend_uptime = -1
+
+            output['backend_info'][backend_info['hash']] = {
+                'uptime': backend_uptime,
+                # 'context_size': opts.context_size,
+                'model': opts.manual_model_name if opts.manual_model_name else backend_info.get('running_model', 'ERROR'),
+                'mode': backend_info['mode'],
                 'nvidia': netdata_stats
             }
+    else:
+        output['backend_info'] = {}
+
     result = deep_sort(output)
 
     # It may take a bit to get the base client API, so don't cache until then.

llm_server/routes/v1/generate_stream.py

@@ -1,5 +1,4 @@
 import json
-import threading
 import time
 import traceback
 from typing import Union
@@ -10,10 +9,11 @@ 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 ... import opts
+from ...cluster.backend import get_a_cluster_backend
 from ...database.database import log_prompt
 from ...llm.generator import generator
 from ...llm.vllm import tokenize
-from ...stream import sock
+from ...sock import sock
 
 
 # TODO: have workers process streaming requests
@@ -35,19 +35,13 @@ def stream(ws):
         log_in_bg(quitting_err_msg, is_error=True)
 
     def log_in_bg(generated_text_bg, elapsed_time_bg: Union[int, float] = None, is_error: bool = False, status_code: int = None):
-
-        def background_task_exception():
         generated_tokens = tokenize(generated_text_bg)
-            log_prompt(handler.client_ip, handler.token, input_prompt, generated_text_bg, elapsed_time_bg, handler.parameters, r_headers, status_code, r_url, response_tokens=generated_tokens, is_error=is_error)
+        log_prompt(handler.client_ip, handler.token, input_prompt, generated_text_bg, elapsed_time_bg, handler.parameters, r_headers, status_code, r_url, cluster_backend, response_tokens=generated_tokens, is_error=is_error)
-
-        # TODO: use async/await instead of threads
-        thread = threading.Thread(target=background_task_exception)
-        thread.start()
-        thread.join()
 
     if not opts.enable_streaming:
         return 'Streaming is disabled', 401
 
+    cluster_backend = None
     r_headers = dict(request.headers)
     r_url = request.url
     message_num = 0
@@ -90,14 +84,15 @@ def stream(ws):
             }
 
             # 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.token_priority)
+            event = priority_queue.put((None, handler.client_ip, handler.token, None, None), handler.token_priority)
             if not event:
                 r, _ = handler.handle_ratelimited()
                 err_msg = r.json['results'][0]['text']
                 send_err_and_quit(err_msg)
                 return
             try:
-                response = generator(llm_request)
+                cluster_backend = get_a_cluster_backend()
+                response = generator(llm_request, cluster_backend)
                 if not response:
                     error_msg = 'Failed to reach backend while streaming.'
                     print('Streaming failed:', error_msg)
@@ -142,7 +137,7 @@ def stream(ws):
                                         ws.close()
                                         end_time = time.time()
                                         elapsed_time = end_time - start_time
-                                        log_prompt(handler.client_ip, handler.token, input_prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, response_tokens=tokenize(generated_text))
+                                        log_prompt(handler.client_ip, handler.token, input_prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, cluster_backend, response_tokens=tokenize(generated_text))
                                         return
 
                                     message_num += 1
@@ -181,5 +176,5 @@ def stream(ws):
                 # The client closed the stream.
                 end_time = time.time()
                 elapsed_time = end_time - start_time
-                log_prompt(handler.client_ip, handler.token, input_prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, response_tokens=tokenize(generated_text))
+                log_prompt(handler.client_ip, handler.token, input_prompt, generated_text, elapsed_time, handler.parameters, r_headers, response_status_code, r_url, cluster_backend, response_tokens=tokenize(generated_text))
     ws.close()  # this is important if we encountered and error and exited early.

llm_server/routes/v1/info.py

@@ -2,22 +2,21 @@ import time
 
 from flask import jsonify, request
 
+from llm_server.custom_redis import flask_cache
 from . import bp
 from ..auth import requires_auth
-from llm_server.custom_redis import flask_cache
 from ... import opts
-from ...llm.info import get_running_model
+from ...cluster.backend import get_a_cluster_backend, get_backends, get_backends_from_model, is_valid_model
-
+from ...cluster.cluster_config import cluster_config
-
-# @bp.route('/info', methods=['GET'])
-# # @cache.cached(timeout=3600, query_string=True)
-# def get_info():
-#     # requests.get()
-#     return 'yes'
 
 
 @bp.route('/model', methods=['GET'])
-def get_model():
+@bp.route('/<model_name>/model', methods=['GET'])
+def get_model(model_name=None):
+    if not model_name:
+        b = get_a_cluster_backend()
+        model_name = cluster_config.get_backend(b)['running_model']
+
     # We will manage caching ourself since we don't want to cache
     # when the backend is down. Also, Cloudflare won't cache 500 errors.
     cache_key = 'model_cache::' + request.url
@@ -26,16 +25,17 @@ def get_model():
     if cached_response:
         return cached_response
 
-    model_name, error = get_running_model()
+    if not is_valid_model(model_name):
-    if not model_name:
         response = jsonify({
-            'code': 502,
+            'code': 400,
-            'msg': 'failed to reach backend',
+            'msg': 'Model does not exist.',
-            'type': error.__class__.__name__
+        }), 400
-        }), 500  # return 500 so Cloudflare doesn't intercept us
     else:
+        num_backends = len(get_backends_from_model(model_name))
+
         response = jsonify({
             'result': opts.manual_model_name if opts.manual_model_name else model_name,
+            'model_backend_count': num_backends,
             'timestamp': int(time.time())
         }), 200
         flask_cache.set(cache_key, response, timeout=60)
@@ -43,7 +43,11 @@ def get_model():
     return response
 
 
-@bp.route('/backend', methods=['GET'])
+@bp.route('/backends', methods=['GET'])
 @requires_auth
 def get_backend():
-    return jsonify({'backend': opts.backend_url, 'mode': opts.mode}), 200
+    online, offline = get_backends()
+    result = []
+    for i in online + offline:
+        result.append(cluster_config.get_backend(i))
+    return jsonify(result), 200

llm_server/workers/app.py

@@ -1,35 +0,0 @@
-from threading import Thread
-
-from .blocking import start_workers
-from .main import main_background_thread
-from .moderator import start_moderation_workers
-from .printer import console_printer
-from .recent import recent_prompters_thread
-from .threads import cache_stats
-from .. import opts
-
-
-def start_background():
-    start_workers(opts.concurrent_gens)
-
-    t = Thread(target=main_background_thread)
-    t.daemon = True
-    t.start()
-    print('Started the main background thread.')
-
-    start_moderation_workers(opts.openai_moderation_workers)
-
-    t = Thread(target=cache_stats)
-    t.daemon = True
-    t.start()
-    print('Started the stats cacher.')
-
-    t = Thread(target=recent_prompters_thread)
-    t.daemon = True
-    t.start()
-    print('Started the recent proompters thread.')
-
-    t = Thread(target=console_printer)
-    t.daemon = True
-    t.start()
-    print('Started the console printer.')

llm_server/workers/inferencer.py

@@ -2,15 +2,15 @@ import threading
 import time
 
 from llm_server import opts
-from llm_server.llm.generator import generator
 from llm_server.custom_redis import redis
+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
 
 
 def worker():
     while True:
         need_to_wait()
-        (request_json_body, client_ip, token, parameters), event_id = priority_queue.get()
+        (request_json_body, client_ip, token, parameters, cluster_backend), event_id = priority_queue.get()
         need_to_wait()
 
         increment_ip_count(client_ip, 'processing_ips')
@@ -22,7 +22,7 @@ def worker():
             continue
 
         try:
-            success, response, error_msg = generator(request_json_body)
+            success, response, error_msg = generator(request_json_body, cluster_backend)
             event = DataEvent(event_id)
             event.set((success, response, error_msg))
         finally:
@@ -42,7 +42,7 @@ def start_workers(num_workers: int):
 
 def need_to_wait():
     # We need to check the number of active workers since the streaming endpoint may be doing something.
-    active_workers = redis.get('active_gen_workers', int, 0)
+    active_workers = redis.get('active_gen_workers', 0, dtype=int)
     s = time.time()
     while active_workers >= opts.concurrent_gens:
         time.sleep(0.01)

llm_server/workers/main.py

@@ -1,55 +0,0 @@
-import time
-
-from llm_server import opts
-from llm_server.database.database import weighted_average_column_for_model
-from llm_server.llm.info import get_running_model
-from llm_server.custom_redis import redis
-
-
-def main_background_thread():
-    redis.set('average_generation_elapsed_sec', 0)
-    redis.set('estimated_avg_tps', 0)
-    redis.set('average_output_tokens', 0)
-    redis.set('backend_online', 0)
-    redis.set_dict('backend_info', {})
-
-    while True:
-        # TODO: unify this
-        if opts.mode == 'oobabooga':
-            running_model, err = get_running_model()
-            if err:
-                print(err)
-                redis.set('backend_online', 0)
-            else:
-                redis.set('running_model', running_model)
-                redis.set('backend_online', 1)
-        elif opts.mode == 'vllm':
-            running_model, err = get_running_model()
-            if err:
-                print(err)
-                redis.set('backend_online', 0)
-            else:
-                redis.set('running_model', running_model)
-                redis.set('backend_online', 1)
-        else:
-            raise Exception
-
-        # 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, opts.mode, opts.backend_url, exclude_zeros=True, include_system_tokens=opts.include_system_tokens_in_stats) or 0
-        if average_generation_elapsed_sec:  # returns None on exception
-            redis.set('average_generation_elapsed_sec', average_generation_elapsed_sec)
-
-        # overall = average_column_for_model('prompts', 'generation_time', opts.running_model)
-        # print(f'Weighted: {average_generation_elapsed_sec}, overall: {overall}')
-
-        average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens', running_model, opts.mode, opts.backend_url, exclude_zeros=True, include_system_tokens=opts.include_system_tokens_in_stats) or 0
-        if average_generation_elapsed_sec:
-            redis.set('average_output_tokens', average_output_tokens)
-
-        # overall = average_column_for_model('prompts', 'response_tokens', opts.running_model)
-        # print(f'Weighted: {average_output_tokens}, overall: {overall}')
-
-        estimated_avg_tps = round(average_output_tokens / average_generation_elapsed_sec, 2) if average_generation_elapsed_sec > 0 else 0  # Avoid division by zero
-        redis.set('estimated_avg_tps', estimated_avg_tps)
-        time.sleep(60)

llm_server/workers/mainer.py

@@ -0,0 +1,56 @@
+import time
+
+from llm_server import opts
+from llm_server.cluster.backend import get_a_cluster_backend, get_backends
+from llm_server.cluster.cluster_config import cluster_config
+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_running_model
+
+
+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']
+            running_model, err = get_running_model(backend_url, backend_mode)
+            if err:
+                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)
+
+            default_backend_url = get_a_cluster_backend()
+            default_backend_info = cluster_config.get_backend(default_backend_url)
+            default_backend_mode = default_backend_info['mode']
+            default_running_model, err = get_running_model(default_backend_url, default_backend_mode)
+            if err:
+                continue
+
+            default_average_generation_elapsed_sec, default_average_output_tokens, default_estimated_avg_tps = calc_stats_for_backend(default_running_model, default_running_model, default_backend_mode)
+            if default_average_generation_elapsed_sec:
+                redis.set('average_generation_elapsed_sec', default_average_generation_elapsed_sec)
+            if default_average_output_tokens:
+                redis.set('average_output_tokens', default_average_output_tokens)
+            if default_average_generation_elapsed_sec and default_average_output_tokens:
+                redis.set('estimated_avg_tps', default_estimated_avg_tps)
+        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

llm_server/workers/threader.py

@@ -0,0 +1,50 @@
+import time
+from threading import Thread
+
+from llm_server import opts
+from llm_server.cluster.stores import redis_running_models
+from llm_server.cluster.worker import cluster_worker
+from llm_server.routes.v1.generate_stats import generate_stats
+from llm_server.workers.inferencer import start_workers
+from llm_server.workers.mainer import main_background_thread
+from llm_server.workers.moderator import start_moderation_workers
+from llm_server.workers.printer import console_printer
+from llm_server.workers.recenter import recent_prompters_thread
+
+
+def cache_stats():
+    while True:
+        generate_stats(regen=True)
+        time.sleep(1)
+
+
+def start_background():
+    start_workers(opts.concurrent_gens)
+
+    t = Thread(target=main_background_thread)
+    t.daemon = True
+    t.start()
+    print('Started the main background thread.')
+
+    start_moderation_workers(opts.openai_moderation_workers)
+
+    t = Thread(target=cache_stats)
+    t.daemon = True
+    t.start()
+    print('Started the stats cacher.')
+
+    t = Thread(target=recent_prompters_thread)
+    t.daemon = True
+    t.start()
+    print('Started the recent proompters thread.')
+
+    t = Thread(target=console_printer)
+    t.daemon = True
+    t.start()
+    print('Started the console printer.')
+
+    redis_running_models.flush()
+    t = Thread(target=cluster_worker)
+    t.daemon = True
+    t.start()
+    print('Started the cluster worker.')

llm_server/workers/threads.py

@@ -1,9 +0,0 @@
-import time
-
-from llm_server.routes.v1.generate_stats import generate_stats
-
-
-def cache_stats():
-    while True:
-        generate_stats(regen=True)
-        time.sleep(5)

other/gunicorn.py

@@ -1,3 +1,8 @@
+"""
+This file is used to run certain tasks when the HTTP server starts.
+It's located here so it doesn't get imported with daemon.py
+"""
+
 try:
     import gevent.monkey
 

server.py

@@ -1,4 +1,4 @@
-from llm_server.config.config import mode_ui_names
+from llm_server.cluster.cluster_config import cluster_config
 
 try:
     import gevent.monkey
@@ -7,8 +7,6 @@ try:
 except ImportError:
     pass
 
-from llm_server.pre_fork import server_startup
-from llm_server.config.load import load_config, parse_backends
 import os
 import sys
 from pathlib import Path
@@ -16,14 +14,17 @@ from pathlib import Path
 import simplejson as json
 from flask import Flask, jsonify, render_template, request
 
-import llm_server
+from llm_server.cluster.backend import get_a_cluster_backend, get_backends
+from llm_server.cluster.redis_cycle import load_backend_cycle
+from llm_server.config.config import mode_ui_names
+from llm_server.config.load import load_config, parse_backends
 from llm_server.database.conn import database
 from llm_server.database.create import create_db
-from llm_server.llm import get_token_count
+from llm_server.pre_fork import server_startup
 from llm_server.routes.openai import openai_bp
 from llm_server.routes.server_error import handle_server_error
 from llm_server.routes.v1 import bp
-from llm_server.stream import init_socketio
+from llm_server.sock import init_socketio
 
 # TODO: set VLLM to stream ALL data using socket.io. If the socket disconnects, cancel generation.
 # TODO: add backend fallbacks. Backends at the bottom of the list are higher priority and are fallbacks if the upper ones fail
@@ -37,6 +38,8 @@ from llm_server.stream import init_socketio
 # TODO: use coloredlogs
 # TODO: need to update opts. for workers
 # TODO: add a healthcheck to VLLM
+# TODO: allow choosing the model by the URL path
+# TODO: have VLLM report context size, uptime
 
 # Lower priority
 # TODO: estiamted wait time needs to account for full concurrent_gens but the queue is less than concurrent_gens
@@ -64,7 +67,7 @@ import config
 from llm_server import opts
 from llm_server.helpers import auto_set_base_client_api
 from llm_server.llm.vllm.info import vllm_info
-from llm_server.custom_redis import RedisCustom, flask_cache
+from llm_server.custom_redis import flask_cache
 from llm_server.llm import redis
 from llm_server.routes.stats import get_active_gen_workers
 from llm_server.routes.v1.generate_stats import generate_stats
@@ -83,20 +86,18 @@ if config_path_environ:
 else:
     config_path = Path(script_path, 'config', 'config.yml')
 
-success, config, msg = load_config(config_path, script_path)
+success, config, msg = load_config(config_path)
 if not success:
     print('Failed to load config:', msg)
     sys.exit(1)
 
 database.init_db(config['mysql']['host'], config['mysql']['username'], config['mysql']['password'], config['mysql']['database'])
 create_db()
-llm_server.llm.redis = RedisCustom('local_llm')
-create_db()
 
-x = parse_backends(config)
+cluster_config.clear()
-print(x)
+cluster_config.load(parse_backends(config))
-
+on, off = get_backends()
-# print(app.url_map)
+load_backend_cycle('backend_cycler', on + off)
 
 
 @app.route('/')
@@ -104,12 +105,18 @@ print(x)
 @app.route('/api/openai')
 @flask_cache.cached(timeout=10)
 def home():
-    stats = generate_stats()
+    # Use the default backend
+    backend_url = get_a_cluster_backend()
+    if backend_url:
+        backend_info = cluster_config.get_backend(backend_url)
+        stats = generate_stats(backend_url)
+    else:
+        backend_info = stats = None
 
     if not stats['online']:
         running_model = estimated_wait_sec = 'offline'
     else:
-        running_model = redis.get('running_model', str, 'ERROR')
+        running_model = backend_info['running_model']
 
         active_gen_workers = get_active_gen_workers()
         if stats['queue']['queued'] == 0 and active_gen_workers >= opts.concurrent_gens:
@@ -130,10 +137,16 @@ def home():
         info_html = ''
 
     mode_info = ''
-    if opts.mode == 'vllm':
+    using_vllm = False
+    for k, v in cluster_config.all().items():
+        if v['mode'] == vllm:
+            using_vllm = True
+            break
+
+    if using_vllm == 'vllm':
         mode_info = vllm_info
 
-    base_client_api = redis.get('base_client_api', str)
+    base_client_api = redis.get('base_client_api', dtype=str)
 
     return render_template('home.html',
                            llm_middleware_name=opts.llm_middleware_name,

test-cluster.py

@@ -7,23 +7,33 @@ except ImportError:
 
 import time
 from threading import Thread
+from llm_server.cluster.redis_cycle import load_backend_cycle
 
-from llm_server.cluster.funcs.backend import get_best_backends
+from llm_server.cluster.backend import get_backends, get_a_cluster_backend
-from llm_server.cluster.redis_config_cache import RedisClusterStore
 from llm_server.cluster.worker import cluster_worker
 from llm_server.config.load import parse_backends, load_config
+from llm_server.cluster.redis_config_cache import RedisClusterStore
 
-success, config, msg = load_config('./config/config.yml').resolve().absolute()
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('config')
+args = parser.parse_args()
+
+success, config, msg = load_config(args.config)
 
 cluster_config = RedisClusterStore('cluster_config')
 cluster_config.clear()
 cluster_config.load(parse_backends(config))
+on, off = get_backends()
+load_backend_cycle('backend_cycler', on + off)
 
 t = Thread(target=cluster_worker)
 t.daemon = True
 t.start()
 
 while True:
-    x = get_best_backends()
+    # online, offline = get_backends()
-    print(x)
+    # print(online, offline)
+    # print(get_a_cluster_backend())
     time.sleep(3)