Merge cluster to master (#3)

Co-authored-by: Cyberes <cyberes@evulid.cc>
Reviewed-on: #3

README.md

@@ -43,7 +43,9 @@ To set up token auth, add rows to the `token_auth` table in the SQLite database.
 
 ### Use
 
+If you see unexpected errors in the console, make sure `daemon.py` is running or else the required data will be missing from Redis. You may need to wait a few minutes for the daemon to populate the database.
 
+Flask may give unusual errors when running `python server.py`. I think this is coming from Flask-Socket. Running with Gunicorn seems to fix the issue: `gunicorn -b :5000 --worker-class gevent server:app`
 
 ### To Do
 

daemon.py

@@ -1,22 +1,19 @@
-import time
-
-from llm_server.routes.cache import redis
-
-try:
-    import gevent.monkey
-
-    gevent.monkey.patch_all()
-except ImportError:
-    pass
-
+import argparse
+import logging
 import os
 import sys
+import time
 from pathlib import Path
 
-from llm_server.config.load import load_config
-from llm_server.database.create import create_db
+from redis import Redis
 
-from llm_server.workers.app import start_background
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.config.load import load_config, parse_backends
+from llm_server.custom_redis import redis
+from llm_server.database.create import create_db
+from llm_server.logging import create_logger, logging_info, init_logging
+from llm_server.routes.v1.generate_stats import generate_stats
+from llm_server.workers.threader import start_background
 
 script_path = os.path.dirname(os.path.realpath(__file__))
 config_path_environ = os.getenv("CONFIG_PATH")
@@ -26,19 +23,46 @@ else:
     config_path = Path(script_path, 'config', 'config.yml')
 
 if __name__ == "__main__":
-    flushed_keys = redis.flush()
-    print('Flushed', len(flushed_keys), 'keys from Redis.')
+    parser = argparse.ArgumentParser(description='Daemon microservice.')
+    parser.add_argument('--no-reset', action='store_true', help="Don't clear the Redis server databases.")
+    parser.add_argument('-d', '--debug', action='store_true', help='Enable debug logging.')
+    args = parser.parse_args()
 
-    success, config, msg = load_config(config_path, script_path)
+    # TODO: have this be set by either the arg or a config value
+    if args.debug:
+        logging_info.level = logging.DEBUG
+
+    init_logging()
+    logger = create_logger('daemon')
+    logger.debug('Debug logging enabled.')
+
+    if not args.no_reset:
+        Redis().flushall()
+        logger.info('Flushed Redis.')
+
+    success, config, msg = load_config(config_path)
     if not success:
-        print('Failed to load config:', msg)
+        logger.info(f'Failed to load config: {msg}')
         sys.exit(1)
 
     create_db()
+
+    cluster_config.clear()
+    cluster_config.load(parse_backends(config))
+
+    logger.info('Loading backend stats...')
+    generate_stats(regen=True)
+
     start_background()
 
-    redis.set('daemon_started', 1)
-    print('== Daemon Setup Complete ==\n')
+    # Give some time for the background threads to get themselves ready to go.
+    time.sleep(2)
 
-    while True:
-        time.sleep(3600)
+    redis.set('daemon_started', 1)
+    logger.info('== Daemon Setup Complete ==')
+
+    try:
+        while True:
+            time.sleep(3600)
+    except KeyboardInterrupt:
+        redis.set('daemon_started', 0)

llm_server/cluster/backend.py

@@ -0,0 +1,117 @@
+import numpy as np
+
+from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
+from llm_server.cluster.stores import redis_running_models
+from llm_server.custom_redis import redis
+from llm_server.llm.generator import generator
+from llm_server.llm.info import get_info
+from llm_server.llm.vllm.vllm_backend import VLLMBackend
+from llm_server.routes.queue import priority_queue
+from llm_server.routes.stats import calculate_wait_time, get_active_gen_workers_model
+
+
+def get_backends_from_model(model_name: str):
+    return [x.decode('utf-8') for x in redis_running_models.smembers(model_name)]
+
+
+def get_running_models():
+    return redis_running_models.keys()
+
+
+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)
+
+
+def test_backend(backend_url: str, test_prompt: bool = False):
+    backend_info = cluster_config.get_backend(backend_url)
+    if test_prompt:
+        handler = VLLMBackend(backend_url)
+        parameters, _ = handler.get_parameters({
+            "stream": False,
+            "temperature": 0,
+            "max_new_tokens": 3,
+        })
+        data = {
+            'prompt': 'test prompt',
+            **parameters
+        }
+        try:
+            success, response, err = generator(data, backend_url, timeout=10)
+            if not success or not response or err:
+                return False, {}
+        except:
+            return False, {}
+    i = get_info(backend_url, backend_info['mode'])
+    if not i.get('model'):
+        return False, {}
+    return True, i
+
+
+def get_model_choices(regen: bool = False):
+    if not regen:
+        c = redis.getp('model_choices')
+        if c:
+            return c
+
+    base_client_api = redis.get('base_client_api', dtype=str)
+    running_models = get_running_models()
+    model_choices = {}
+    for model in running_models:
+        b = get_backends_from_model(model)
+
+        context_size = []
+        avg_gen_per_worker = []
+        concurrent_gens = 0
+        for backend_url in b:
+            backend_info = cluster_config.get_backend(backend_url)
+            if backend_info.get('model_config'):
+                context_size.append(backend_info['model_config']['max_position_embeddings'])
+            if backend_info.get('average_generation_elapsed_sec'):
+                avg_gen_per_worker.append(backend_info['average_generation_elapsed_sec'])
+            concurrent_gens += backend_info['concurrent_gens']
+
+        active_gen_workers = get_active_gen_workers_model(model)
+        proompters_in_queue = priority_queue.len(model)
+
+        if len(avg_gen_per_worker):
+            average_generation_elapsed_sec = np.average(avg_gen_per_worker)
+        else:
+            average_generation_elapsed_sec = 0
+        estimated_wait_sec = calculate_wait_time(average_generation_elapsed_sec, proompters_in_queue, concurrent_gens, active_gen_workers)
+
+        model_choices[model] = {
+            'model': model,
+            'client_api': f'https://{base_client_api}/{model}',
+            'ws_client_api': f'wss://{base_client_api}/{model}/v1/stream' if opts.enable_streaming else None,
+            'openai_client_api': f'https://{base_client_api}/openai/{model}/v1' if opts.enable_openi_compatible_backend else 'disabled',
+            'backend_count': len(b),
+            'estimated_wait': estimated_wait_sec,
+            'queued': proompters_in_queue,
+            'processing': active_gen_workers,
+            'avg_generation_time': average_generation_elapsed_sec,
+            'concurrent_gens': concurrent_gens
+        }
+
+        if len(context_size):
+            model_choices[model]['context_size'] = min(context_size)
+
+    # Python wants to sort lowercase vs. uppercase letters differently.
+    model_choices = dict(sorted(model_choices.items(), key=lambda item: item[0].upper()))
+
+    default_backend_url = get_a_cluster_backend()
+    default_backend_info = cluster_config.get_backend(default_backend_url)
+    if not default_backend_info.get('model'):
+        return {}, None
+    default_model = default_backend_info['model']
+
+    redis.setp('model_choices', (model_choices, default_model))
+    return model_choices, default_model

llm_server/cluster/cluster_config.py

@@ -0,0 +1,124 @@
+import hashlib
+import pickle
+import traceback
+
+from llm_server import opts
+from llm_server.cluster.redis_cycle import add_backend_cycler, redis_cycle
+from llm_server.cluster.stores import redis_running_models
+from llm_server.custom_redis import RedisCustom
+from llm_server.routes.helpers.model import estimate_model_size
+
+
+class RedisClusterStore:
+    def __init__(self, name: str, **kwargs):
+        self.name = name
+        self.config_redis = RedisCustom(name, **kwargs)
+
+    def clear(self):
+        self.config_redis.flush()
+
+    def load(self, config: dict):
+        for k, v in config.items():
+            self.add_backend(k, v)
+
+    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, backend: str, key: str, 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)
+        output = {}
+        for k, v in r.items():
+            output[k.decode('utf8')] = pickle.loads(v)
+        return output
+
+    def all(self):
+        keys = self.config_redis.keys('*')
+        if keys:
+            result = {}
+            for key in keys:
+                if key != f'{self.name}:____':
+                    v = self.get_backend(key)
+                    result[key] = v
+            return result
+        else:
+            return {}
+
+    def validate_backend(self, backend_url: str):
+        """
+        Returns the backend URL that was given, or a new one if that was offline.
+        :param backend_url:
+        :return:
+        """
+        backend_info = self.get_backend(backend_url)
+        if not backend_info['online']:
+            old = backend_url
+            backend_url = get_a_cluster_backend()
+            print(f'Backend {old} offline. Request was redirected to {backend_url}')
+        return backend_url
+
+
+cluster_config = RedisClusterStore('cluster_config')
+
+
+def get_backends():
+    backends = cluster_config.all()
+    result = {}
+    for k, v in backends.items():
+        b = cluster_config.get_backend(k)
+        status = b.get('online', False)
+        priority = b['priority']
+        result[k] = {'status': status, 'priority': priority}
+
+    try:
+        if not opts.prioritize_by_size:
+            online_backends = sorted(
+                ((url, info) for url, info in backends.items() if info['online']),
+                key=lambda kv: -kv[1]['priority'],
+                reverse=True
+            )
+        else:
+            online_backends = sorted(
+                ((url, info) for url, info in backends.items() if info['online']),
+                key=lambda kv: estimate_model_size(kv[1]['model_config']),
+                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]
+    except KeyError:
+        traceback.print_exc()
+        print(backends)
+
+
+def get_a_cluster_backend(model=None):
+    """
+    Get a backend from Redis. If there are no online backends, return None.
+    If `model` is not supplied, we will pick one ourself.
+    """
+    if model:
+        # First, determine if there are multiple backends hosting the same model.
+        backends_hosting_model = [i.decode('utf-8') for i in redis_running_models.smembers(model)]
+
+        # If so, create an iterator for those backends
+        if len(backends_hosting_model):
+            add_backend_cycler(model, backends_hosting_model)
+            cycled = redis_cycle(model)
+            if len(cycled):
+                return cycled[0]
+        else:
+            # No backend hosting that model
+            return None
+    else:
+        online, _ = get_backends()
+        if len(online):
+            return online[0]

llm_server/cluster/redis_cycle.py

@@ -0,0 +1,39 @@
+import redis
+
+redis_cycler_db = redis.Redis(host='localhost', port=6379, db=9)
+
+
+def redis_cycle(list_name):
+    """
+    Emulates itertools.cycle() but returns the complete shuffled list.
+    :param list_name:
+    :return:
+    """
+    pipeline = redis_cycler_db.pipeline()
+    pipeline.lpop(list_name)
+    to_move = pipeline.execute()[0]
+    if not to_move:
+        return []
+    pipeline.rpush(list_name, to_move)
+    pipeline.lrange(list_name, 0, -1)
+    results = pipeline.execute()
+    new_list = results[-1]
+    return [x.decode('utf-8') for x in new_list]
+
+
+def add_backend_cycler(list_name: str, new_elements: list):
+    existing_elements = [i.decode('utf-8') for i in redis_cycler_db.lrange(list_name, 0, -1)]
+    existing_set = set(existing_elements)
+
+    with redis_cycler_db.pipeline() as pipe:
+        # Add elements
+        for element in new_elements:
+            if element not in existing_set:
+                pipe.rpush(list_name, element)
+
+        # Remove elements
+        for element in existing_set:
+            if element not in new_elements:
+                pipe.lrem(list_name, 0, element)
+
+        pipe.execute()

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

@@ -0,0 +1,38 @@
+import time
+from threading import Thread
+
+from llm_server.cluster.backend import test_backend
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.cluster.stores import redis_running_models
+
+
+def cluster_worker():
+    counter = 0
+    while True:
+        test_prompt = False
+        if counter % 4 == 0:
+            # Only send a test prompt every 120 seconds.
+            test_prompt = True
+        threads = []
+        for n, v in cluster_config.all().items():
+            thread = Thread(target=check_backend, args=(n, v, test_prompt))
+            thread.start()
+            threads.append(thread)
+        for thread in threads:
+            thread.join()
+        time.sleep(15)
+        counter += 1
+
+
+def check_backend(n, v, test_prompt):
+    online, backend_info = test_backend(v['backend_url'], test_prompt=test_prompt)
+    if online:
+        running_model = backend_info['model']
+        for k, v in backend_info.items():
+            cluster_config.set_backend_value(n, k, v)
+        redis_running_models.sadd(running_model, n)
+    else:
+        for model in redis_running_models.keys():
+            redis_running_models.srem(model, n)
+
+    cluster_config.set_backend_value(n, 'online', online)

llm_server/config/config.py

@@ -28,16 +28,19 @@ config_default_vars = {
     'openai_force_no_hashes': True,
     'include_system_tokens_in_stats': True,
     'openai_moderation_scan_last_n': 5,
-    'openai_moderation_workers': 10,
     'openai_org_name': 'OpenAI',
     'openai_silent_trim': False,
     'openai_moderation_enabled': True,
-    'netdata_root': None
+    'netdata_root': None,
+    'show_backends': True,
+    'background_homepage_cacher': True,
+    'openai_moderation_timeout': 5,
+    'prioritize_by_size': False
 }
-config_required_vars = ['token_limit', 'concurrent_gens', 'mode', 'llm_middleware_name']
+config_required_vars = ['cluster', 'frontend_api_mode', 'llm_middleware_name']
 
 mode_ui_names = {
-    'oobabooga': ('Text Gen WebUI (ooba)', 'Blocking API url', 'Streaming API url'),
+    'ooba': ('Text Gen WebUI (ooba)', 'Blocking API url', 'Streaming API url'),
     'vllm': ('Text Gen WebUI (ooba)', 'Blocking API url', 'Streaming API url'),
 }
 

llm_server/config/load.py

@@ -3,38 +3,28 @@ import sys
 
 import openai
 
+import llm_server
 from llm_server import opts
 from llm_server.config.config import ConfigLoader, config_default_vars, config_required_vars
+from llm_server.custom_redis import redis
 from llm_server.database.conn import database
 from llm_server.database.database import get_number_of_rows
-from llm_server.helpers import resolve_path
-from llm_server.routes.cache import redis
+from llm_server.routes.queue import PriorityQueue
 
 
-def load_config(config_path, script_path):
+def load_config(config_path):
     config_loader = ConfigLoader(config_path, config_default_vars, config_required_vars)
     success, config, msg = config_loader.load_config()
     if not success:
         return success, config, msg
 
-    # Resolve relative directory to the directory of the script
-    if config['database_path'].startswith('./'):
-        config['database_path'] = resolve_path(script_path, config['database_path'].strip('./'))
-
-    if config['mode'] not in ['oobabooga', 'vllm']:
-        print('Unknown mode:', config['mode'])
-        sys.exit(1)
-
     # TODO: this is atrocious
-    opts.mode = config['mode']
     opts.auth_required = config['auth_required']
     opts.log_prompts = config['log_prompts']
-    opts.concurrent_gens = config['concurrent_gens']
     opts.frontend_api_client = config['frontend_api_client']
-    opts.context_size = config['token_limit']
     opts.show_num_prompts = config['show_num_prompts']
     opts.show_uptime = config['show_uptime']
-    opts.backend_url = config['backend_url'].strip('/')
+    opts.cluster = config['cluster']
     opts.show_total_output_tokens = config['show_total_output_tokens']
     opts.netdata_root = config['netdata_root']
     opts.simultaneous_requests_per_ip = config['simultaneous_requests_per_ip']
@@ -53,10 +43,20 @@ def load_config(config_path, script_path):
     opts.openai_force_no_hashes = config['openai_force_no_hashes']
     opts.include_system_tokens_in_stats = config['include_system_tokens_in_stats']
     opts.openai_moderation_scan_last_n = config['openai_moderation_scan_last_n']
-    opts.openai_moderation_workers = config['openai_moderation_workers']
     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']
+    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']
+
+    llm_server.routes.queue.priority_queue = PriorityQueue([x['backend_url'] for x in config['cluster']])
 
     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.')
@@ -78,6 +78,16 @@ def load_config(config_path, script_path):
     if config['load_num_prompts']:
         redis.set('proompts', get_number_of_rows('prompts'))
 
-    redis.set('backend_mode', opts.mode)
-
     return success, config, msg
+
+
+def parse_backends(config):
+    if not config.get('cluster'):
+        return False
+    cluster = config.get('cluster')
+    config = {}
+    for item in cluster:
+        backend_url = item['backend_url'].strip('/')
+        item['backend_url'] = backend_url
+        config[backend_url] = item
+    return config

llm_server/config/redis_config.py

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

llm_server/custom_redis.py

@@ -1,24 +1,27 @@
+import pickle
 import sys
 import traceback
-from typing import Callable, List, Mapping, Union
+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
+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'})
+flask_cache = Cache(config={'CACHE_TYPE': 'RedisCache', 'CACHE_REDIS_URL': 'redis://localhost:6379/15', 'CACHE_KEY_PREFIX': 'local_llm_flask'})
 
 ONE_MONTH_SECONDS = 2678000
 
 
-class RedisWrapper:
+class RedisCustom(Redis):
     """
-    A wrapper class to set prefixes to keys.
+    A simple wrapper class for Redis to create a "namespace" within a DB,
+    which simplyifies key management.
     """
 
     def __init__(self, prefix, **kwargs):
+        super().__init__()
         self.redis = Redis(**kwargs)
         self.prefix = prefix
         try:
@@ -34,12 +37,11 @@ class RedisWrapper:
     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):
-        """
-        :param key:
-        :param dtype: convert to this type
-        :return:
-        """
+    def get(self, key, default=None, dtype=None):
+        # TODO: use pickle
+        import inspect
+        if inspect.isclass(default):
+            raise Exception
 
         d = self.redis.get(self._key(key))
         if dtype and d:
@@ -108,7 +110,10 @@ class RedisWrapper:
     ):
         return self.redis.hincrby(self._key(name), key, amount)
 
-    def hdel(self, name: str, *keys: List):
+    def zcard(self, name: KeyT):
+        return self.redis.zcard(self._key(name))
+
+    def hdel(self, name: str, *keys: str):
         return self.redis.hdel(self._key(name), *keys)
 
     def hget(
@@ -129,9 +134,62 @@ class RedisWrapper:
     ):
         return self.redis.zadd(self._key(name), mapping, nx, xx, ch, incr, gt, lt)
 
+    def lpush(self, name: str, *values: FieldT):
+        return self.redis.lpush(self._key(name), *values)
+
+    def hset(
+            self,
+            name: str,
+            key: Optional = None,
+            value=None,
+            mapping: Optional[dict] = None,
+            items: Optional[list] = None,
+    ):
+        return self.redis.hset(self._key(name), key, value, mapping, items)
+
     def hkeys(self, name: str):
         return self.redis.hkeys(self._key(name))
 
+    def hmget(self, name: str, keys: List, *args: List):
+        return self.redis.hmget(self._key(name), keys, *args)
+
+    def hgetall(self, name: str):
+        return self.redis.hgetall(self._key(name))
+
+    def keys(self, pattern: PatternT = "*", **kwargs):
+        raw_keys = self.redis.keys(self._key(pattern), **kwargs)
+        keys = []
+        for key in raw_keys:
+            p = key.decode('utf-8').split(':')
+            if len(p) >= 2:
+                # Delete prefix
+                del p[0]
+            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 smembers(self, name: str):
+        return self.redis.smembers(self._key(name))
+
+    def spop(self, name: str, count: Optional[int] = None):
+        return self.redis.spop(self._key(name), count)
+
+    def rpoplpush(self, src, dst):
+        return self.redis.rpoplpush(src, dst)
+
+    def zpopmin(self, name: KeyT, count: Union[int, None] = None):
+        return self.redis.zpopmin(self._key(name), count)
+
+    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)
 
@@ -142,6 +200,15 @@ class RedisWrapper:
         else:
             return json.loads(r.decode("utf-8"))
 
+    def setp(self, name, value):
+        self.redis.set(self._key(name), pickle.dumps(value))
+
+    def getp(self, name: str):
+        r = self.redis.get(self._key(name))
+        if r:
+            return pickle.loads(r)
+        return r
+
     def flush(self):
         flushed = []
         for key in self.redis.scan_iter(f'{self.prefix}:*'):
@@ -149,5 +216,40 @@ class RedisWrapper:
             self.redis.delete(key)
         return flushed
 
+    def flushall(self, asynchronous: bool = ..., **kwargs) -> bool:
+        self.flush()
+        return True
 
-redis = RedisWrapper('local_llm')
+    def flushdb(self, asynchronous: bool = ..., **kwargs) -> bool:
+        self.flush()
+        return True
+
+    def lrange(self, name: str, start: int, end: int):
+        return self.redis.lrange(self._key(name), start, end)
+
+    def delete(self, *names: KeyT):
+        return self.redis.delete(*[self._key(i) for i in names])
+
+    def lpop(self, name: str, count: Optional[int] = None):
+        return self.redis.lpop(self._key(name), count)
+
+    def zrange(
+            self,
+            name: KeyT,
+            start: int,
+            end: int,
+            desc: bool = False,
+            withscores: bool = False,
+            score_cast_func: Union[type, Callable] = float,
+            byscore: bool = False,
+            bylex: bool = False,
+            offset: int = None,
+            num: int = None,
+    ):
+        return self.redis.zrange(self._key(name), start, end, desc, withscores, score_cast_func, byscore, bylex, offset, num)
+
+    def zrem(self, name: KeyT, *values: FieldT):
+        return self.redis.zrem(self._key(name), *values)
+
+
+redis = RedisCustom('local_llm')

llm_server/database/conn.py

@@ -5,20 +5,20 @@ class DatabaseConnection:
     host: str = None
     username: str = None
     password: str = None
-    database: str = None
+    database_name: str = None
 
-    def init_db(self, host, username, password, database):
+    def init_db(self, host, username, password, database_name):
         self.host = host
         self.username = username
         self.password = password
-        self.database = database
+        self.database_name = database_name
 
     def cursor(self):
         db = pymysql.connect(
             host=self.host,
             user=self.username,
             password=self.password,
-            database=self.database,
+            database=self.database_name,
             charset='utf8mb4',
             autocommit=True,
         )

llm_server/database/database.py

@@ -1,15 +1,19 @@
 import json
 import time
 import traceback
+from typing import Union
 
-import llm_server
 from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config
 from llm_server.database.conn import database
-from llm_server.llm.vllm import tokenize
-from llm_server.routes.cache import redis
+from llm_server.llm import get_token_count
 
 
-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 do_db_log(ip: str, token: str, prompt: str, response: Union[str, None], gen_time: Union[int, float, None], parameters: dict, headers: dict, backend_response_code: int, request_url: str, backend_url: str, response_tokens: int = None, is_error: bool = False):
+    assert isinstance(prompt, str)
+    assert isinstance(backend_url, str)
+
+    # Try not to shove JSON into the database.
     if isinstance(response, dict) and response.get('results'):
         response = response['results'][0]['text']
     try:
@@ -19,10 +23,11 @@ def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backe
     except:
         pass
 
-    prompt_tokens = llm_server.llm.get_token_count(prompt)
+    prompt_tokens = get_token_count(prompt, backend_url)
+
     if not is_error:
         if not response_tokens:
-            response_tokens = llm_server.llm.get_token_count(response)
+            response_tokens = get_token_count(response, backend_url)
     else:
         response_tokens = None
 
@@ -43,7 +48,9 @@ def log_prompt(ip, token, prompt, response, gen_time, parameters, headers, backe
     if token:
         increment_token_uses(token)
 
-    running_model = redis.get('running_model', str, 'ERROR')
+    backend_info = cluster_config.get_backend(backend_url)
+    running_model = backend_info.get('model')
+    backend_mode = backend_info['mode']
     timestamp = int(time.time())
     cursor = database.cursor()
     try:
@@ -52,7 +59,7 @@ 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, backend_mode, backend_url, request_url, gen_time, prompt, prompt_tokens, response, response_tokens, backend_response_code, json.dumps(parameters), json.dumps(headers), timestamp))
     finally:
         cursor.close()
 
@@ -179,3 +186,21 @@ def increment_token_uses(token):
         cursor.execute('UPDATE token_auth SET uses = uses + 1 WHERE token = %s', (token,))
     finally:
         cursor.close()
+
+
+def get_token_ratelimit(token):
+    priority = 9990
+    simultaneous_ip = opts.simultaneous_requests_per_ip
+    if token:
+        cursor = database.cursor()
+        try:
+            cursor.execute("SELECT priority, simultaneous_ip FROM token_auth WHERE token = %s", (token,))
+            result = cursor.fetchone()
+            if result:
+                priority, simultaneous_ip = result
+                if simultaneous_ip is None:
+                    # No ratelimit for this token if null
+                    simultaneous_ip = 999999999
+        finally:
+            cursor.close()
+    return priority, simultaneous_ip

llm_server/database/log_to_db.py

@@ -0,0 +1,30 @@
+import pickle
+from typing import Union
+
+from redis import Redis
+
+
+def log_to_db(ip: str, token: str, prompt: str, response: Union[str, None], gen_time: Union[int, float, None], parameters: dict, headers: dict, backend_response_code: int, request_url: str, backend_url: str, response_tokens: int = None, is_error: bool = False):
+    assert isinstance(prompt, str)
+    assert isinstance(backend_url, str)
+
+    r = Redis(host='localhost', port=6379, db=3)
+    data = {
+        'function': 'log_prompt',
+        'args': [],
+        'kwargs': {
+            'ip': ip,
+            'token': token,
+            'prompt': prompt,
+            'response': response,
+            'gen_time': gen_time,
+            'parameters': parameters,
+            'headers': dict(headers) if headers else headers,
+            'backend_response_code': backend_response_code,
+            'request_url': request_url,
+            'backend_url': backend_url,
+            'response_tokens': response_tokens,
+            'is_error': is_error
+        }
+    }
+    r.publish('database-logger', pickle.dumps(data))

llm_server/helpers.py

@@ -8,7 +8,7 @@ import simplejson as json
 from flask import make_response
 
 from llm_server import opts
-from llm_server.routes.cache import redis
+from llm_server.custom_redis import redis
 
 
 def resolve_path(*p: str):
@@ -54,13 +54,14 @@ def jsonify_pretty(json_dict: Union[list, dict], status=200, indent=4, sort_keys
 
 def round_up_base(n, base):
     if base == 0:
-        print('round_up_base DIVIDE BY ZERO ERROR????', n, base)
+        # TODO: I don't think passing (0, 0) to this function is a sign of any underlying issues.
+        # print('round_up_base DIVIDE BY ZERO ERROR????', n, base)
         return 0
     return math.ceil(n / base) * 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

@@ -1,11 +1,30 @@
+import tiktoken
+
+from llm_server.cluster.cluster_config import cluster_config
 from llm_server.llm import oobabooga, vllm
-from llm_server.routes.cache import redis
+from llm_server.logging import create_logger
 
 
-def get_token_count(prompt: str):
-    backend_mode = redis.get('backend_mode', str)
+def fallback_tokenizer(prompt: str):
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+    return len(tokenizer.encode(prompt)) + 10
+
+
+def get_token_count(prompt: str, backend_url: str):
+    backend_url = cluster_config.validate_backend(backend_url)
+    if not backend_url:
+        logger = create_logger('tokenizer')
+        logger.warning('using fallback tokenizer as there is no valid backend')
+        return fallback_tokenizer(prompt)
+
+    backend_mode = cluster_config.get_backend(backend_url).get('mode')
+    if not backend_mode:
+        logger = create_logger('tokenizer')
+        logger.warning("using fallback tokenizer as the backend isn't initalized")
+        return fallback_tokenizer(prompt)
+
     if backend_mode == 'vllm':
-        return vllm.tokenize(prompt)
+        return vllm.tokenize(prompt, backend_url)
     elif backend_mode == 'ooba':
         return oobabooga.tokenize(prompt)
     else:

llm_server/llm/generator.py

@@ -1,14 +1,15 @@
 from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config
 
 
-def generator(request_json_body):
-    if opts.mode == 'oobabooga':
+def generator(request_json_body, cluster_backend, timeout: int = None):
+    mode = cluster_config.get_backend(cluster_backend)['mode']
+    if mode == 'ooba':
         # from .oobabooga.generate import generate
         # return generate(request_json_body)
         raise NotImplementedError
-    elif opts.mode == 'vllm':
+    elif mode == 'vllm':
         from .vllm.generate import generate
-        r = generate(request_json_body)
-        return r
+        return generate(request_json_body, cluster_backend, timeout=timeout)
     else:
         raise Exception

llm_server/llm/info.py

@@ -3,23 +3,35 @@ import requests
 from llm_server import opts
 
 
-def get_running_model():
-    # TODO: cache the results for 1 min so we don't have to keep calling the backend
-    # TODO: only use one try/catch
-
-    if opts.mode == 'oobabooga':
+def get_running_model(backend_url: str, mode: str):
+    if mode == 'ooba':
         try:
-            backend_response = requests.get(f'{opts.backend_url}/api/v1/model', timeout=opts.backend_request_timeout, verify=opts.verify_ssl)
+            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'{opts.backend_url}/model', timeout=opts.backend_request_timeout, verify=opts.verify_ssl)
+            backend_response = requests.get(f'{backend_url}/model', timeout=opts.backend_request_timeout, verify=opts.verify_ssl)
             r_json = backend_response.json()
             return r_json['model'], None
         except Exception as e:
             return False, e
     else:
         raise Exception
+
+
+def get_info(backend_url: str, mode: str):
+    if mode == 'ooba':
+        return {}
+        # raise NotImplementedError
+    elif mode == 'vllm':
+        try:
+            r = requests.get(f'{backend_url}/info', verify=opts.verify_ssl, timeout=opts.backend_request_timeout)
+            j = r.json()
+        except Exception as e:
+            return {}
+        return j
+    else:
+        raise Exception

llm_server/llm/llm_backend.py

@@ -2,14 +2,17 @@ from typing import Tuple, Union
 
 import flask
 
-from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config
 from llm_server.llm import get_token_count
-from llm_server.routes.cache import redis
 
 
 class LLMBackend:
     _default_params: dict
 
+    def __init__(self, backend_url: str):
+        self.backend_url = backend_url
+        self.backend_info = cluster_config.get_backend(self.backend_url)
+
     def handle_response(self, success, request: flask.Request, response_json_body: dict, response_status_code: int, client_ip, token, prompt, elapsed_time, parameters, headers):
         raise NotImplementedError
 
@@ -32,14 +35,16 @@ class LLMBackend:
         """
         If a backend needs to do other checks not related to the prompt or parameters.
         Default is no extra checks preformed.
+        :param request:
+        :param prompt:
         :param parameters:
         :return:
         """
         return True, None
 
     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')
-            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'
+        prompt_len = get_token_count(prompt, self.backend_url)
+        token_limit = self.backend_info['model_config']['max_position_embeddings']
+        if prompt_len > token_limit - 10:
+            return False, f'Token indices sequence length is longer than the specified maximum sequence length for this model ({prompt_len} > {token_limit}, model: {self.backend_info["model"]}). Please lower your context size'
         return True, None

llm_server/llm/oobabooga/ooba_backend.py

@@ -1,78 +1,6 @@
-from flask import jsonify
-
 from ..llm_backend import LLMBackend
-from ...database.database import log_prompt
-from ...helpers import safe_list_get
-from ...routes.cache import redis
-from ...routes.helpers.client import format_sillytavern_err
-from ...routes.helpers.http import validate_json
 
 
 class OobaboogaBackend(LLMBackend):
-    default_params = {}
-
-    def handle_response(self, success, request, response, error_msg, client_ip, token, prompt, elapsed_time, parameters, headers):
-        raise NotImplementedError('need to implement default_params')
-
-        backend_err = False
-        response_valid_json, response_json_body = validate_json(response)
-        if response:
-            try:
-                # Be extra careful when getting attributes from the response object
-                response_status_code = response.status_code
-            except:
-                response_status_code = 0
-        else:
-            response_status_code = None
-
-        # ===============================================
-
-        # We encountered an error
-        if not success or not response or error_msg:
-            if not error_msg or error_msg == '':
-                error_msg = 'Unknown error.'
-            else:
-                error_msg = error_msg.strip('.') + '.'
-            backend_response = format_sillytavern_err(error_msg, 'error')
-            log_prompt(client_ip, token, prompt, backend_response, None, parameters, headers, response_status_code, request.url, is_error=True)
-            return jsonify({
-                'code': 500,
-                'msg': error_msg,
-                'results': [{'text': backend_response}]
-            }), 400
-
-        # ===============================================
-
-        if response_valid_json:
-            backend_response = safe_list_get(response_json_body.get('results', []), 0, {}).get('text')
-            if not backend_response:
-                # Ooba doesn't return any error messages so we will just tell the client an error occurred
-                backend_err = True
-                backend_response = format_sillytavern_err(
-                    f'Backend (oobabooga) returned an empty string. This is usually due to an error on the backend during inference. Please check your parameters and try again.',
-                    'error')
-                response_json_body['results'][0]['text'] = backend_response
-
-            if not backend_err:
-                redis.incr('proompts')
-
-            log_prompt(client_ip, token, prompt, backend_response, elapsed_time if not backend_err else None, parameters, headers, response_status_code, request.url, response_tokens=response_json_body.get('details', {}).get('generated_tokens'), is_error=backend_err)
-            return jsonify({
-                **response_json_body
-            }), 200
-        else:
-            backend_response = format_sillytavern_err(f'The backend did not return valid JSON.', 'error')
-            log_prompt(client_ip, token, prompt, backend_response, elapsed_time, parameters, headers, response.status_code, request.url, is_error=True)
-            return jsonify({
-                'code': 500,
-                'msg': 'the backend did not return valid JSON',
-                'results': [{'text': backend_response}]
-            }), 400
-
-    def validate_params(self, params_dict: dict):
-        # No validation required
-        return True, None
-
-    def get_parameters(self, parameters):
-        del parameters['prompt']
-        return parameters
+    def __int__(self):
+        return

llm_server/llm/openai/moderation.py

@@ -10,7 +10,7 @@ def check_moderation_endpoint(prompt: str):
     }
     response = requests.post('https://api.openai.com/v1/moderations', headers=headers, json={"input": prompt}, timeout=10)
     if response.status_code != 200:
-        print(response.text)
+        print('moderation failed:', response)
         response.raise_for_status()
     response = response.json()
 

llm_server/llm/openai/oai_to_vllm.py

@@ -0,0 +1,97 @@
+from flask import jsonify
+
+from llm_server import opts
+
+
+def oai_to_vllm(request_json_body, stop_hashes: bool, mode):
+    if not request_json_body.get('stop'):
+        request_json_body['stop'] = []
+    if not isinstance(request_json_body['stop'], list):
+        # It is a string, so create a list with the existing element.
+        request_json_body['stop'] = [request_json_body['stop']]
+
+    if stop_hashes:
+        if opts.openai_force_no_hashes:
+            request_json_body['stop'].append('###')
+        else:
+            # TODO: make stopping strings a configurable
+            request_json_body['stop'].extend(['### INSTRUCTION', '### USER', '### ASSISTANT'])
+    else:
+        request_json_body['stop'].extend(['user:', 'assistant:'])
+
+    if request_json_body.get('frequency_penalty', 0) < -2:
+        request_json_body['frequency_penalty'] = -2
+    elif request_json_body.get('frequency_penalty', 0) > 2:
+        request_json_body['frequency_penalty'] = 2
+
+    if mode == 'vllm' and request_json_body.get('top_p') == 0:
+        request_json_body['top_p'] = 0.01
+
+    request_json_body['max_tokens'] = min(max(request_json_body.get('max_new_tokens', 0), request_json_body.get('max_tokens', 0)), opts.max_new_tokens)
+    if request_json_body['max_tokens'] == 0:
+        # We don't want to set any defaults here.
+        del request_json_body['max_tokens']
+
+    return request_json_body
+
+
+def format_oai_err(err_msg):
+    print('OAI ERROR MESSAGE:', err_msg)
+    return jsonify({
+        "error": {
+            "message": err_msg,
+            "type": "invalid_request_error",
+            "param": None,
+            "code": None
+        }
+    }), 400
+
+
+def validate_oai(parameters):
+    if parameters.get('messages'):
+        for m in parameters['messages']:
+            if m['role'].lower() not in ['assistant', 'user', 'system']:
+                return format_oai_err('messages role must be assistant, user, or system')
+
+    if parameters.get('temperature', 0) > 2:
+        return format_oai_err(f"{parameters['temperature']} is greater than the maximum of 2 - 'temperature'")
+    if parameters.get('temperature', 0) < 0:
+        return format_oai_err(f"{parameters['temperature']} less than the minimum of 0 - 'temperature'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")
+
+    if parameters.get('presence_penalty', 1) > 2:
+        return format_oai_err(f"{parameters['presence_penalty']} is greater than the maximum of 2 - 'presence_penalty'")
+    if parameters.get('presence_penalty', 1) < -2:
+        return format_oai_err(f"{parameters['presence_penalty']} less than the minimum of -2 - 'presence_penalty'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")
+
+    if parameters.get('top_p', 1) > 2:
+        return format_oai_err(f"{parameters['top_p']} is greater than the maximum of 1 - 'top_p'")
+    if parameters.get('top_p', 1) < 0:
+        return format_oai_err(f"{parameters['top_p']} less than the minimum of 0 - 'top_p'")
+
+    if parameters.get('max_tokens', 2) < 1:
+        return format_oai_err(f"{parameters['max_tokens']} is less than the minimum of 1 - 'max_tokens'")
+
+
+def return_invalid_model_err(requested_model: str):
+    if requested_model:
+        msg = f"The model `{requested_model}` does not exist"
+    else:
+        msg = "The requested model does not exist"
+    return jsonify({
+        "error": {
+            "message": msg,
+            "type": "invalid_request_error",
+            "param": None,
+            "code": "model_not_found"
+        }
+    }), 404

llm_server/llm/openai/transform.py

@@ -2,86 +2,35 @@ import concurrent.futures
 import re
 import secrets
 import string
-import time
 import traceback
 from typing import Dict, List
 
 import tiktoken
-from flask import jsonify, make_response
 
-import llm_server
 from llm_server import opts
 from llm_server.llm import get_token_count
-from llm_server.routes.cache import redis
 
 ANTI_RESPONSE_RE = re.compile(r'^### (.*?)(?:\:)?\s')  # Match a "### XXX" line.
 ANTI_CONTINUATION_RE = re.compile(r'(.*?### .*?(?:\:)?(.|\n)*)')  # Match everything after a "### XXX" line.
 
 
-def build_openai_response(prompt, response, model=None):
-    # Seperate the user's prompt from the context
-    x = prompt.split('### USER:')
-    if len(x) > 1:
-        prompt = re.sub(r'\n$', '', x[-1].strip(' '))
-
-    # Make sure the bot doesn't put any other instructions in its response
-    # y = response.split('\n### ')
-    # if len(y) > 1:
-    #     response = re.sub(r'\n$', '', y[0].strip(' '))
-    response = re.sub(ANTI_RESPONSE_RE, '', response)
-    response = re.sub(ANTI_CONTINUATION_RE, '', response)
-
-    # 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')
-
-    response = make_response(jsonify({
-        "id": f"chatcmpl-{generate_oai_string(30)}",
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": running_model if opts.openai_expose_our_model else model,
-        "choices": [{
-            "index": 0,
-            "message": {
-                "role": "assistant",
-                "content": response,
-            },
-            "logprobs": None,
-            "finish_reason": "stop"
-        }],
-        "usage": {
-            "prompt_tokens": prompt_tokens,
-            "completion_tokens": response_tokens,
-            "total_tokens": prompt_tokens + response_tokens
-        }
-    }), 200)
-
-    stats = redis.get('proxy_stats', dict)
-    if stats:
-        response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
-    return response
-
-
 def generate_oai_string(length=24):
     alphabet = string.ascii_letters + string.digits
     return ''.join(secrets.choice(alphabet) for i in range(length))
 
 
-def trim_prompt_to_fit(prompt: List[Dict[str, str]], context_token_limit: int) -> List[Dict[str, str]]:
-    tokenizer = tiktoken.get_encoding("cl100k_base")
-
-    def get_token_count_tiktoken_thread(msg):
-        return len(tokenizer.encode(msg["content"]))
+def trim_messages_to_fit(prompt: List[Dict[str, str]], context_token_limit: int, backend_url: str) -> List[Dict[str, str]]:
+    def get_token_count_thread(msg):
+        return get_token_count(msg["content"], backend_url)
 
     with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
-        token_counts = list(executor.map(get_token_count_tiktoken_thread, prompt))
+        token_counts = list(executor.map(get_token_count_thread, prompt))
 
     total_tokens = sum(token_counts)
-    formatting_tokens = len(tokenizer.encode(transform_messages_to_prompt(prompt))) - total_tokens
+    formatting_tokens = get_token_count(transform_messages_to_prompt(prompt), backend_url) - total_tokens
 
     # If total tokens exceed the limit, start trimming
-    if total_tokens > context_token_limit:
+    if total_tokens + formatting_tokens > context_token_limit:
         while True:
             while total_tokens + formatting_tokens > context_token_limit:
                 # Calculate the index to start removing messages from
@@ -94,22 +43,43 @@ def trim_prompt_to_fit(prompt: List[Dict[str, str]], context_token_limit: int) -
                     if total_tokens + formatting_tokens <= context_token_limit or remove_index == len(prompt):
                         break
 
-            def get_token_count_thread(msg):
-                return get_token_count(msg["content"])
-
             with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
                 token_counts = list(executor.map(get_token_count_thread, prompt))
 
             total_tokens = sum(token_counts)
-            formatting_tokens = get_token_count(transform_messages_to_prompt(prompt)) - total_tokens
-
+            formatting_tokens = get_token_count(transform_messages_to_prompt(prompt), backend_url) - total_tokens
             if total_tokens + formatting_tokens > context_token_limit:
                 # Start over, but this time calculate the token count using the backend
                 with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
                     token_counts = list(executor.map(get_token_count_thread, prompt))
             else:
                 break
+    return prompt
 
+
+def trim_string_to_fit(prompt: str, context_token_limit: int, backend_url: str) -> str:
+    tokenizer = tiktoken.get_encoding("cl100k_base")
+    token_count = get_token_count(prompt, backend_url)
+
+    # If total tokens exceed the limit, start trimming
+    if token_count > context_token_limit:
+        while True:
+            while token_count > context_token_limit:
+                # Calculate the index to start removing characters from
+                remove_index = len(prompt) // 3
+
+                while remove_index < len(prompt):
+                    prompt = prompt[:remove_index] + prompt[remove_index + 100:]
+                    token_count = len(tokenizer.encode(prompt))
+                    if token_count <= context_token_limit or remove_index == len(prompt):
+                        break
+
+            token_count = get_token_count(prompt, backend_url)
+            if token_count > context_token_limit:
+                # Start over, but this time calculate the token count using the backend
+                token_count = get_token_count(prompt, backend_url)
+            else:
+                break
     return prompt
 
 
@@ -117,8 +87,9 @@ def transform_messages_to_prompt(oai_messages):
     try:
         prompt = f'### INSTRUCTION: {opts.openai_system_prompt}'
         for msg in oai_messages:
-            if not msg.get('content') or not msg.get('role'):
+            if 'content' not in msg.keys() or 'role' not in msg.keys():
                 return False
+            msg['content'] = str(msg['content'])  # Prevent any weird issues.
             if msg['role'] == 'system':
                 prompt += f'### INSTRUCTION: {msg["content"]}\n\n'
             elif msg['role'] == 'user':
@@ -126,7 +97,7 @@ def transform_messages_to_prompt(oai_messages):
             elif msg['role'] == 'assistant':
                 prompt += f'### ASSISTANT: {msg["content"]}\n\n'
             else:
-                return False
+                raise Exception(f'Unknown role: {msg["role"]}')
     except Exception as e:
         # TODO: use logging
         traceback.print_exc()

llm_server/llm/vllm/generate.py

@@ -1,80 +1,21 @@
 """
 This file is used by the worker that processes requests.
 """
-import json
-import time
-from uuid import uuid4
 
 import requests
 
-import llm_server
 from llm_server import opts
-from llm_server.routes.cache import redis
 
 
 # TODO: make the VLMM backend return TPS and time elapsed
 # https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py
 
 def prepare_json(json_data: dict):
-    # logit_bias is not currently supported
-    # del json_data['logit_bias']
-
     # Convert back to VLLM.
     json_data['max_tokens'] = json_data.pop('max_new_tokens')
     return json_data
 
 
-def transform_to_text(json_request, api_response):
-    """
-    This is to convert a streaming request to a non-streamed request. Don't think this is nessesary.
-    :param json_request:
-    :param api_response:
-    :return:
-    """
-    prompt = transform_prompt_to_text(json_request['messages'])
-    text = ''
-    finish_reason = None
-    for line in api_response.split('\n'):
-        if line.startswith('data:'):
-            try:
-                data = json.loads(line[5:].strip())
-            except json.decoder.JSONDecodeError:
-                break
-            if 'choices' in data:
-                for choice in data['choices']:
-                    if 'delta' in choice and 'content' in choice['delta']:
-                        text += choice['delta']['content']
-                    if data['choices'][0]['finish_reason']:
-                        finish_reason = data['choices'][0]['finish_reason']
-
-    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')
-
-    # https://platform.openai.com/docs/api-reference/making-requests?lang=python
-    return {
-        "id": str(uuid4()),
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": running_model,
-        "usage": {
-            "prompt_tokens": prompt_tokens,
-            "completion_tokens": completion_tokens,
-            "total_tokens": prompt_tokens + completion_tokens
-        },
-        "choices": [
-            {
-                "message": {
-                    "role": "assistant",
-                    "content": text
-                },
-                "finish_reason": finish_reason,
-                "index": 0
-            }
-        ]
-    }
-
-
 def transform_prompt_to_text(prompt: list):
     text = ''
     for item in prompt:
@@ -82,26 +23,26 @@ 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, timeout: int = 10):
     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 if not timeout else timeout)
     except requests.exceptions.ReadTimeout:
-        print(f'Failed to reach VLLM inference endpoint - request to backend timed out')
+        # print(f'Failed to reach VLLM inference endpoint - request to backend timed out')
         return False, None, 'Request to backend timed out'
     except Exception as e:
-        print(f'Failed to reach VLLM inference endpoint -', f'{e.__class__.__name__}: {e}')
+        # print(f'Failed to reach VLLM inference endpoint -', f'{e.__class__.__name__}: {e}')
         return False, None, 'Request to backend encountered error'
     if r.status_code != 200:
-        print(f'Failed to reach VLLM inference endpoint - got code {r.status_code}')
+        # print(f'Failed to reach VLLM inference endpoint - got code {r.status_code}')
         return False, r, f'Backend returned {r.status_code}'
     return True, r, None
 
 
-def generate(json_data: dict):
+def generate(json_data: dict, cluster_backend, timeout: int = None):
     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 if not timeout else timeout)
         except Exception as e:
-            print(f'Failed to reach VLLM inference endpoint -', f'{e.__class__.__name__}: {e}')
+            return False
     else:
-        return handle_blocking_request(json_data)
+        return handle_blocking_request(json_data, cluster_backend, timeout=timeout)

llm_server/llm/vllm/info.py

@@ -1,3 +1,7 @@
+import requests
+
+from llm_server import opts
+
 vllm_info = """<p><strong>Important:</strong> This endpoint is running <a href="https://github.com/vllm-project/vllm" target="_blank">vllm</a> and not all Oobabooga parameters are supported.</p>
 <strong>Supported Parameters:</strong>
 <ul>
@@ -7,4 +11,4 @@ vllm_info = """<p><strong>Important:</strong> This endpoint is running <a href="
 <li><kbd>max_new_tokens</kbd></li>
 <li><kbd>num_beams</kbd> <span style="font-size:9pt">(setting to greater than 1 enables beam search)</span></li>
 <li><kbd>ban_eos_token</kbd></li>
-</ul>"""
+</ul>"""

llm_server/llm/vllm/tokenize.py

@@ -1,26 +1,51 @@
+import concurrent.futures
+
 import requests
 import tiktoken
 
 from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.logging import create_logger
 
 
-def tokenize(prompt: str) -> int:
+def tokenize(prompt: str, backend_url: str) -> int:
+    assert backend_url
+    assert isinstance(backend_url, str)
+
     if not prompt:
         # The tokenizers have issues when the prompt is None.
         return 0
+    assert isinstance(prompt, str)
+
+    logger = create_logger('tokenizer')
+
+    # The backend could have died between when the request was
+    # submitted and now, so let's double check it's still online.
+    backend_url = cluster_config.validate_backend(backend_url)
+
     tokenizer = tiktoken.get_encoding("cl100k_base")
 
-    # First we tokenize it locally to determine if it's worth sending it to the backend.
-    initial_estimate = len(tokenizer.encode(prompt))
-    if initial_estimate <= opts.context_size + 200:
+    # Split the prompt into 2000 character chunks
+    chunk_size = 2000
+    chunks = [prompt[i:i + chunk_size] for i in range(0, len(prompt), chunk_size)]
+
+    # Define a function to send a chunk to the server
+    def send_chunk(chunk):
         try:
-            r = requests.post(f'{opts.backend_url}/tokenize', json={'input': prompt}, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
+            r = requests.post(f'{backend_url}/tokenize', json={'input': chunk}, verify=opts.verify_ssl, timeout=opts.backend_generate_request_timeout)
             j = r.json()
             return j['length']
         except Exception as e:
-            print(f'Failed to tokenize using VLLM -', f'{e.__class__.__name__}: {e}')
-            return len(tokenizer.encode(prompt)) + 10
-    else:
-        # If the result was greater than our context size, return the estimate.
-        # We won't be sending it through the backend so it does't need to be accurage.
-        return initial_estimate
+            logger.debug(f'Failed to tokenize using VLLM - {e.__class__.__name__}')
+            return len(tokenizer.encode(chunk)) + 10
+
+    # Use a ThreadPoolExecutor to send all chunks to the server at once
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        future_to_chunk = {executor.submit(send_chunk, chunk): chunk for chunk in chunks}
+        for future in concurrent.futures.as_completed(future_to_chunk):
+            chunk = future_to_chunk[future]
+            try:
+                data = future.result()
+            except Exception as exc:
+                logger.warning('%r generated an exception: %s' % (chunk, exc))
+    return sum(future.result() for future in future_to_chunk)

llm_server/llm/vllm/vllm_backend.py

@@ -1,10 +1,9 @@
-import threading
 from typing import Tuple
 
 from flask import jsonify
 from vllm import SamplingParams
 
-from llm_server.database.database import log_prompt
+from llm_server.database.log_to_db import log_to_db
 from llm_server.llm.llm_backend import LLMBackend
 
 
@@ -19,16 +18,8 @@ class VLLMBackend(LLMBackend):
             # Failsafe
             backend_response = ''
 
-        r_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()
+        log_to_db(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,
+                  response_tokens=response_json_body.get('details', {}).get('generated_tokens'), backend_url=self.backend_url)
 
         return jsonify({'results': [{'text': backend_response}]}), 200
 
@@ -38,14 +29,20 @@ class VLLMBackend(LLMBackend):
             top_k = parameters.get('top_k', self._default_params['top_k'])
             if top_k <= 0:
                 top_k = -1
+
+            # TODO: support more params
             sampling_params = SamplingParams(
                 temperature=parameters.get('temperature', self._default_params['temperature']),
                 top_p=parameters.get('top_p', self._default_params['top_p']),
                 top_k=top_k,
                 use_beam_search=True if parameters.get('num_beams', 0) > 1 else False,
-                stop=parameters.get('stopping_strings', self._default_params['stop']),
+                stop=list(set(parameters.get('stopping_strings') or parameters.get('stop', self._default_params['stop']))),
                 ignore_eos=parameters.get('ban_eos_token', False),
-                max_tokens=parameters.get('max_new_tokens', self._default_params['max_tokens'])
+                max_tokens=parameters.get('max_new_tokens') or parameters.get('max_tokens', self._default_params['max_tokens']),
+                presence_penalty=parameters.get('presence_penalty', self._default_params['presence_penalty']),
+                frequency_penalty=parameters.get('frequency_penalty', self._default_params['frequency_penalty']),
+                length_penalty=parameters.get('length_penalty', self._default_params['length_penalty']),
+                early_stopping=parameters.get('early_stopping', self._default_params['early_stopping'])
             )
         except ValueError as e:
             return None, str(e).strip('.')

llm_server/logging.py

@@ -0,0 +1,52 @@
+import logging
+
+import coloredlogs
+
+from llm_server import opts
+
+
+class LoggingInfo:
+    def __init__(self):
+        self._level = logging.INFO
+        self._format = opts.LOGGING_FORMAT
+
+    @property
+    def level(self):
+        return self._level
+
+    @level.setter
+    def level(self, value):
+        self._level = value
+
+    @property
+    def format(self):
+        return self._format
+
+    @format.setter
+    def format(self, value):
+        self._format = value
+
+
+logging_info = LoggingInfo()
+
+
+def init_logging():
+    """
+    Set up the parent logger.
+    :return:
+    """
+    logger = logging.getLogger('llm_server')
+    logger.setLevel(logging_info.level)
+
+
+def create_logger(name):
+    logger = logging.getLogger('llm_server').getChild(name)
+    logger.setLevel(logging_info.level)
+    if not logger.handlers:
+        handler = logging.StreamHandler()
+        handler.setLevel(logging_info.level)
+        formatter = logging.Formatter(logging_info.format)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+        coloredlogs.install(logger=logger, level=logging_info.level)
+    return logger

llm_server/messages.py

@@ -0,0 +1 @@
+BACKEND_OFFLINE = 'The model you requested is not a valid choice. Please retry your query.'

llm_server/netdata.py

@@ -1,52 +0,0 @@
-import json
-from datetime import datetime, timedelta
-
-import requests
-
-from llm_server import opts
-
-
-def get_power_states():
-    gpu_num = 0
-    output = {}
-    while True:
-        url = f"{opts.netdata_root}/api/v1/data?chart=nvidia_smi.gpu{gpu_num}_power_state"
-        try:
-            response = requests.get(url, timeout=10)
-            if response.status_code != 200:
-                break
-            data = json.loads(response.text)
-            power_state_data = data['data'][0]
-            power_state = None
-            for i in range(1, len(power_state_data)):
-                if power_state_data[i] == 1:
-                    power_state = data['labels'][i]
-                    break
-            output[f'gpu{gpu_num}'] = int(power_state.lower().strip('p'))
-        except Exception as e:
-            print('Failed to fetch Netdata metrics:', e)
-            return output
-        gpu_num += 1
-    return output
-
-
-def get_gpu_wh(gpu_id: int):
-    chart_name = f"nvidia_smi.gpu{gpu_id}_power"
-    now = datetime.now()
-    one_hour_ago = now - timedelta(hours=1)
-    num_seconds = int((now - one_hour_ago).total_seconds())
-    params = {
-        "chart": chart_name,
-        "after": int(one_hour_ago.timestamp()),
-        "before": int(now.timestamp()),
-        "points": num_seconds,
-        "group": "second",
-        "format": "json",
-        "options": "absolute|jsonwrap"
-    }
-    response = requests.get(f'{opts.netdata_root}/api/v1/data', params=params, timeout=10)
-    data = json.loads(response.text)
-    total_power_usage_watts = sum(point[1] for point in data['result']['data'])
-    # total_power_usage_watt_hours = round(total_power_usage_watts / 3600, 1)
-    total_power_usage_kwh = round(total_power_usage_watts / 1000 / 3600, 3)
-    return total_power_usage_kwh

llm_server/opts.py

@@ -1,12 +1,11 @@
 # Read-only global variables
 
+# Uppercase variables are read-only globals.
+# Lowercase variables are ones that are set on startup and are never changed.
+
 # 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
-context_size = 5555
+frontend_api_mode = 'ooba'
 max_new_tokens = 500
 auth_required = False
 log_prompts = False
@@ -33,7 +32,15 @@ openai_expose_our_model = False
 openai_force_no_hashes = True
 include_system_tokens_in_stats = True
 openai_moderation_scan_last_n = 5
-openai_moderation_workers = 10
 openai_org_name = 'OpenAI'
 openai_silent_trim = False
 openai_moderation_enabled = True
+cluster = {}
+show_backends = True
+background_homepage_cacher = True
+openai_moderation_timeout = 5
+prioritize_by_size = False
+cluster_workers = 0
+redis_stream_timeout = 25000
+
+LOGGING_FORMAT = "%(asctime)s: %(levelname)s:%(name)s - %(message)s"

llm_server/pre_fork.py

@@ -1,21 +1,9 @@
 import sys
 
-from redis import Redis
-
-from llm_server.routes.cache import redis
-from llm_server.routes.v1.generate_stats import generate_stats
+from llm_server.custom_redis import redis
 
 
 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)
-
-    # Flush the RedisPriorityQueue database.
-    queue_redis = Redis(host='localhost', port=6379, db=15)
-    for key in queue_redis.scan_iter('*'):
-        queue_redis.delete(key)
-
-    # Cache the initial stats
-    print('Loading backend stats...')
-    generate_stats()

llm_server/routes/helpers/client.py

@@ -1,11 +1,18 @@
-from llm_server import opts
-from llm_server.routes.cache import redis
+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'):
-    http_host = redis.get('http_host', str)
+def format_sillytavern_err(msg: str, backend_url: str = None, error_type: str = 'info'):
+    if backend_url:
+        cluster_backend_hash = cluster_config.get_backend(backend_url)['hash']
+    else:
+        cluster_backend_hash = 'none'
+    http_host = redis.get('http_host', dtype=str)
     return f"""```
 === MESSAGE FROM LLM MIDDLEWARE AT {http_host} ===
--> {level.upper()} <-
+-> {error_type.upper()} <-
 {msg}
+```
+```
+BACKEND: {cluster_backend_hash}
 ```"""

llm_server/routes/helpers/http.py

@@ -100,4 +100,4 @@ def validate_json(data: Union[str, flask.Request, requests.models.Response, flas
         j = json.loads(str(data))
         return True, j
     except Exception as e:
-        return False, e
+        return False, e

llm_server/routes/helpers/model.py

@@ -0,0 +1,15 @@
+def estimate_model_size(config: dict):
+    """
+    Estimate the size of a model from its config. No idea if this is correct,
+    but it allows us to compare models.
+    :param config:
+    :return:
+    """
+    vocab_size = config.get('vocab_size')
+    hidden_size = config.get('hidden_size')
+    num_hidden_layers = config.get('num_hidden_layers')
+    intermediate_size = config.get('intermediate_size')
+    if vocab_size and hidden_size and num_hidden_layers and intermediate_size:
+        total_params = (vocab_size * hidden_size) + (num_hidden_layers * ((hidden_size * intermediate_size * 4) + (hidden_size * hidden_size * 3)))
+        return int(total_params / 1e9)
+    return 0

llm_server/routes/ooba_request_handler.py

@@ -3,8 +3,8 @@ from typing import Tuple
 import flask
 from flask import jsonify, request
 
-from llm_server import opts
-from llm_server.database.database import log_prompt
+from llm_server import messages, opts
+from llm_server.database.log_to_db import log_to_db
 from llm_server.routes.helpers.client import format_sillytavern_err
 from llm_server.routes.request_handler import RequestHandler
 
@@ -13,8 +13,11 @@ class OobaRequestHandler(RequestHandler):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
-    def handle_request(self):
+    def handle_request(self, return_ok: bool = True):
         assert not self.used
+        if self.offline:
+            print('This backend is offline:', messages.BACKEND_OFFLINE)
+            return self.handle_error(messages.BACKEND_OFFLINE)
 
         request_valid, invalid_response = self.validate_request()
         if not request_valid:
@@ -25,14 +28,19 @@ class OobaRequestHandler(RequestHandler):
         llm_request = {**self.parameters, 'prompt': prompt}
 
         _, backend_response = self.generate_response(llm_request)
-        return backend_response
+        if return_ok:
+            # Always return 200 so ST displays our error messages
+            return backend_response[0], 200
+        else:
+            # The OpenAI route needs to detect 429 errors.
+            return backend_response
 
     def handle_ratelimited(self, do_log: bool = True):
         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)
-        return backend_response[0], 200  # We only return the response from handle_error(), not the error code
+            log_to_db(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.backend_url, is_error=True)
+        return backend_response[0], 429
 
     def handle_error(self, error_msg: str, error_type: str = 'error') -> Tuple[flask.Response, int]:
         disable_st_error_formatting = request.headers.get('LLM-ST-Errors', False) == 'true'
@@ -40,7 +48,7 @@ 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=error_type, backend_url=self.backend_url)
 
         return jsonify({
             'results': [{'text': response_msg}]

llm_server/routes/openai/__init__.py

@@ -5,9 +5,11 @@ from ..server_error import handle_server_error
 from ... import opts
 
 openai_bp = Blueprint('openai/v1/', __name__)
+openai_model_bp = Blueprint('openai/', __name__)
 
 
 @openai_bp.before_request
+@openai_model_bp.before_request
 def before_oai_request():
     if not opts.enable_openi_compatible_backend:
         return 'The OpenAI-compatible backend is disabled.', 401
@@ -15,8 +17,22 @@ def before_oai_request():
 
 
 @openai_bp.errorhandler(500)
+@openai_model_bp.errorhandler(500)
 def handle_error(e):
-    return handle_server_error(e)
+    """
+    Found Codes:
+    "auth_subrequest_error"
+    """
+
+    print('OAI returning error:', e)
+    return jsonify({
+        "error": {
+            "message": "Internal server error",
+            "type": "auth_subrequest_error",
+            "param": None,
+            "code": "internal_error"
+        }
+    }), 500
 
 
 from .models import openai_list_models

llm_server/routes/openai/chat_completions.py

@@ -1,113 +1,175 @@
 import json
-import threading
 import time
 import traceback
 
+import ujson
 from flask import Response, jsonify, request
+from redis import Redis
 
-from . import openai_bp
-from ..cache import redis
+from llm_server.custom_redis import redis
+from . import openai_bp, openai_model_bp
 from ..helpers.http import validate_json
 from ..openai_request_handler import OpenAIRequestHandler
+from ..queue import priority_queue
 from ... import opts
-from ...database.database import log_prompt
-from ...llm.generator import generator
-from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt
-from ...llm.vllm import tokenize
+from ...database.log_to_db import log_to_db
+from ...llm.openai.oai_to_vllm import oai_to_vllm, return_invalid_model_err, validate_oai
+from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
 
 
 # TODO: add rate-limit headers?
 
+
 @openai_bp.route('/chat/completions', methods=['POST'])
-def openai_chat_completions():
+@openai_model_bp.route('/<model_name>/v1/chat/completions', methods=['POST'])
+def openai_chat_completions(model_name=None):
     request_valid_json, request_json_body = validate_json(request)
     if not request_valid_json or not request_json_body.get('messages') or not request_json_body.get('model'):
         return jsonify({'code': 400, 'msg': 'invalid JSON'}), 400
     else:
-        handler = OpenAIRequestHandler(request, request_json_body)
-        if request_json_body.get('stream'):
-            if not opts.enable_streaming:
-                # TODO: return a proper OAI error message
-                return 'disabled', 401
+        handler = OpenAIRequestHandler(incoming_request=request, incoming_json=request_json_body, selected_model=model_name)
+        if handler.offline:
+            return return_invalid_model_err(model_name)
 
-            if opts.mode != 'vllm':
-                # TODO: implement other backends
-                raise NotImplementedError
-
-            response_status_code = 0
-            start_time = time.time()
-            request_valid, invalid_response = handler.validate_request()
-            if not request_valid:
-                # TODO: simulate OAI here
-                raise Exception('TODO: simulate OAI here')
-            else:
-                handler.prompt = transform_messages_to_prompt(request_json_body['messages'])
-                msg_to_backend = {
-                    **handler.parameters,
-                    'prompt': handler.prompt,
-                    'stream': True,
-                }
-                try:
-                    response = generator(msg_to_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')
-                    oai_string = generate_oai_string(30)
-
-                    def generate():
-                        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
-
-                        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)
-
-                        # TODO: use async/await instead of threads
-                        thread = threading.Thread(target=background_task)
-                        thread.start()
-                        thread.join()
-
-                    return Response(generate(), mimetype='text/event-stream')
-                except:
-                    # TODO: simulate OAI here
-                    raise Exception
-        else:
+        if not request_json_body.get('stream'):
             try:
                 return handler.handle_request()
             except Exception:
                 traceback.print_exc()
                 return 'Internal server error', 500
+        else:
+            if not opts.enable_streaming:
+                return 'Streaming disabled', 403
+
+            invalid_oai_err_msg = validate_oai(handler.request_json_body)
+            if invalid_oai_err_msg:
+                return invalid_oai_err_msg
+
+            handler.request_json_body = oai_to_vllm(handler.request_json_body, stop_hashes=True, mode=handler.cluster_backend_info['mode'])
+
+            handler.parameters, e = handler.get_parameters()
+            handler.request_json_body = {
+                'messages': handler.request_json_body['messages'],
+                'model': handler.request_json_body['model'],
+                **handler.parameters
+            }
+
+            if opts.openai_silent_trim:
+                handler.prompt = transform_messages_to_prompt(trim_messages_to_fit(handler.request.json['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url))
+            else:
+                handler.prompt = transform_messages_to_prompt(handler.request.json['messages'])
+            if not handler.prompt:
+                # Prevent issues on the backend.
+                return 'Invalid prompt', 400
+
+            # Need to set the prompt in the JSON body since that's what the inference worker expects.
+            handler.request_json_body['prompt'] = handler.prompt
+
+            start_time = time.time()
+
+            request_valid, invalid_response = handler.validate_request()
+            if not request_valid:
+                return invalid_response
+
+            event = None
+            if not handler.is_client_ratelimited():
+                event = priority_queue.put(handler.backend_url, (handler.request_json_body, handler.client_ip, handler.token, handler.parameters), handler.token_priority, handler.selected_model, do_stream=True)
+            if not event:
+                log_to_db(
+                    handler.client_ip,
+                    handler.token,
+                    handler.prompt,
+                    None,
+                    None,
+                    handler.parameters,
+                    request.headers,
+                    429,
+                    request.url,
+                    handler.backend_url,
+                )
+                return handler.handle_ratelimited()
+
+            try:
+                r_headers = dict(request.headers)
+                r_url = request.url
+                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)
+
+                # Need to do this before we enter generate() since we want to be able to
+                # return a 408 if necessary.
+                _, stream_name, error_msg = event.wait()
+                if error_msg:
+                    print('OAI failed to start streaming:', error_msg)
+                    stream_name = None  # set to null so that the Finally ignores it.
+                    return 'Request Timeout', 408
+
+                def generate():
+                    stream_redis = Redis(db=8)
+                    generated_text = ''
+                    try:
+                        last_id = '0-0'
+                        while True:
+                            stream_data = stream_redis.xread({stream_name: last_id}, block=opts.redis_stream_timeout)
+                            if not stream_data:
+                                print(f"No message received in {opts.redis_stream_timeout / 1000} seconds, closing stream.")
+                                yield 'data: [DONE]\n\n'
+                            else:
+                                for stream_index, item in stream_data[0][1]:
+                                    last_id = stream_index
+                                    timestamp = int(stream_index.decode('utf-8').split('-')[0])
+                                    data = ujson.loads(item[b'data'])
+                                    if data['error']:
+                                        # Not printing error since we can just check the daemon log.
+                                        print('OAI streaming encountered error')
+                                        yield 'data: [DONE]\n\n'
+                                        return
+                                    elif data['new']:
+                                        response = {
+                                            "id": f"chatcmpl-{oai_string}",
+                                            "object": "chat.completion.chunk",
+                                            "created": timestamp,
+                                            "model": model,
+                                            "choices": [
+                                                {
+                                                    "index": 0,
+                                                    "delta": {
+                                                        "content": data['new']
+                                                    },
+                                                    "finish_reason": None
+                                                }
+                                            ]
+                                        }
+                                        generated_text = generated_text + data['new']
+                                        yield f'data: {json.dumps(response)}\n\n'
+                                    elif data['completed']:
+                                        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,
+                                            200,
+                                            r_url,
+                                            handler.backend_url,
+                                        )
+                                        return
+                    except GeneratorExit:
+                        return
+                    except Exception:
+                        traceback.print_exc()
+                        yield 'data: [DONE]\n\n'
+                    finally:
+                        if event:
+                            redis.publish(f'notifications:{event.event_id}', 'canceled')
+                        if stream_name:
+                            stream_redis.delete(stream_name)
+
+                return Response(generate(), mimetype='text/event-stream')
+            except Exception:
+                traceback.print_exc()
+                return 'INTERNAL SERVER', 500

llm_server/routes/openai/completions.py

@@ -1,38 +1,68 @@
 import time
 import traceback
 
-from flask import jsonify, make_response, request
+import simplejson as json
+import ujson
+from flask import Response, jsonify, request
+from redis import Redis
 
-from . import openai_bp
-from ..cache import redis
-from ..helpers.client import format_sillytavern_err
+from llm_server.custom_redis import redis
+from . import openai_bp, openai_model_bp
 from ..helpers.http import validate_json
 from ..ooba_request_handler import OobaRequestHandler
+from ..queue import priority_queue
 from ... import opts
+from ...database.log_to_db import log_to_db
 from ...llm import get_token_count
-from ...llm.openai.transform import build_openai_response, generate_oai_string
+from ...llm.openai.oai_to_vllm import oai_to_vllm, return_invalid_model_err, validate_oai
+from ...llm.openai.transform import generate_oai_string, trim_string_to_fit
 
 
 # TODO: add rate-limit headers?
 
 @openai_bp.route('/completions', methods=['POST'])
-def openai_completions():
+@openai_model_bp.route('/<model_name>/v1/completions', methods=['POST'])
+def openai_completions(model_name=None):
     request_valid_json, request_json_body = validate_json(request)
     if not request_valid_json or not request_json_body.get('prompt'):
         return jsonify({'code': 400, 'msg': 'Invalid JSON'}), 400
     else:
-        try:
-            response, status_code = OobaRequestHandler(request).handle_request()
-            if status_code != 200:
-                return status_code
+        handler = OobaRequestHandler(incoming_request=request, incoming_json=request_json_body, selected_model=model_name)
+        if handler.offline:
+            return return_invalid_model_err(model_name)
+
+        if handler.cluster_backend_info['mode'] != 'vllm':
+            # TODO: implement other backends
+            raise NotImplementedError
+
+        invalid_oai_err_msg = validate_oai(handler.request_json_body)
+        if invalid_oai_err_msg:
+            return invalid_oai_err_msg
+        handler.request_json_body = oai_to_vllm(handler.request_json_body, stop_hashes=False, mode=handler.cluster_backend_info['mode'])
+
+        if opts.openai_silent_trim:
+            handler.prompt = trim_string_to_fit(request_json_body['prompt'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
+        else:
+            # The handle_request() call below will load the prompt so we don't have
+            # to do anything else here.
+            pass
+
+        handler.request_json_body['prompt'] = handler.prompt
+
+        if not request_json_body.get('stream'):
+            invalid_oai_err_msg = validate_oai(request_json_body)
+            if invalid_oai_err_msg:
+                return invalid_oai_err_msg
+            response, status_code = handler.handle_request(return_ok=False)
+            if status_code == 429:
+                return handler.handle_ratelimited()
             output = response.json['results'][0]['text']
 
-            # 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')
+            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)
 
-            response = make_response(jsonify({
+            response = jsonify({
                 "id": f"cmpl-{generate_oai_string(30)}",
                 "object": "text_completion",
                 "created": int(time.time()),
@@ -42,7 +72,7 @@ def openai_completions():
                         "text": output,
                         "index": 0,
                         "logprobs": None,
-                        "finish_reason": None
+                        "finish_reason": "stop"
                     }
                 ],
                 "usage": {
@@ -50,12 +80,141 @@ def openai_completions():
                     "completion_tokens": response_tokens,
                     "total_tokens": prompt_tokens + response_tokens
                 }
-            }), 200)
+            })
 
-            stats = redis.get('proxy_stats', dict)
-            if stats:
-                response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
-            return response
-        except Exception:
-            traceback.print_exc()
-            return 'Internal Server Error', 500
+            # TODO:
+            # stats = redis.get('proxy_stats', dtype=dict)
+            # if stats:
+            #     response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
+            return response, 200
+        else:
+            if not opts.enable_streaming:
+                return 'Streaming disabled', 403
+
+            request_valid, invalid_response = handler.validate_request()
+            if not request_valid:
+                return invalid_response
+
+            handler.parameters, _ = handler.get_parameters()
+            handler.request_json_body = {
+                'prompt': handler.request_json_body['prompt'],
+                'model': handler.request_json_body['model'],
+                **handler.parameters
+            }
+
+            invalid_oai_err_msg = validate_oai(handler.request_json_body)
+            if invalid_oai_err_msg:
+                return invalid_oai_err_msg
+
+            if opts.openai_silent_trim:
+                handler.request_json_body['prompt'] = handler.request_json_body['prompt'][:handler.cluster_backend_info['model_config']['max_position_embeddings']]
+            if not handler.prompt:
+                # Prevent issues on the backend.
+                return 'Invalid prompt', 400
+
+            start_time = time.time()
+
+            request_valid, invalid_response = handler.validate_request()
+            if not request_valid:
+                return invalid_response
+
+            event = None
+            if not handler.is_client_ratelimited():
+                event = priority_queue.put(handler.backend_url, (handler.request_json_body, handler.client_ip, handler.token, handler.parameters), handler.token_priority, handler.selected_model, do_stream=True)
+            if not event:
+                log_to_db(
+                    handler.client_ip,
+                    handler.token,
+                    handler.prompt,
+                    None,
+                    None,
+                    handler.parameters,
+                    request.headers,
+                    429,
+                    request.url,
+                    handler.backend_url,
+                )
+                return handler.handle_ratelimited()
+
+            try:
+                r_headers = dict(request.headers)
+                r_url = request.url
+                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)
+
+                _, stream_name, error_msg = event.wait()
+                if error_msg:
+                    print('OAI failed to start streaming:', error_msg)
+                    stream_name = None
+                    return 'Request Timeout', 408
+
+                def generate():
+                    stream_redis = Redis(db=8)
+                    generated_text = ''
+                    try:
+                        last_id = '0-0'
+                        while True:
+                            stream_data = stream_redis.xread({stream_name: last_id}, block=opts.redis_stream_timeout)
+                            if not stream_data:
+                                print(f"No message received in {opts.redis_stream_timeout / 1000} seconds, closing stream.")
+                                yield 'data: [DONE]\n\n'
+                            else:
+                                for stream_index, item in stream_data[0][1]:
+                                    last_id = stream_index
+                                    timestamp = int(stream_index.decode('utf-8').split('-')[0])
+                                    data = ujson.loads(item[b'data'])
+                                    if data['error']:
+                                        print('OAI streaming encountered error')
+                                        yield 'data: [DONE]\n\n'
+                                        return
+                                    elif data['new']:
+                                        response = {
+                                            "id": f"cmpl-{oai_string}",
+                                            "object": "text_completion",
+                                            "created": timestamp,
+                                            "model": model,
+                                            "choices": [
+                                                {
+                                                    "index": 0,
+                                                    "delta": {
+                                                        "content": data['new']
+                                                    },
+                                                    "finish_reason": None
+                                                }
+                                            ]
+                                        }
+                                        generated_text = generated_text + data['new']
+                                        yield f'data: {json.dumps(response)}\n\n'
+                                    elif data['completed']:
+                                        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,
+                                            200,
+                                            r_url,
+                                            handler.backend_url,
+                                        )
+                                        return
+                    except GeneratorExit:
+                        # This should be triggered if a client disconnects early.
+                        return
+                    except Exception:
+                        traceback.print_exc()
+                        yield 'data: [DONE]\n\n'
+                    finally:
+                        if event:
+                            redis.publish(f'notifications:{event.event_id}', 'canceled')
+                        if stream_name:
+                            stream_redis.delete(stream_name)
+
+                return Response(generate(), mimetype='text/event-stream')
+            except Exception:
+                traceback.print_exc()
+                return 'INTERNAL SERVER', 500

llm_server/routes/openai/info.py

@@ -1,7 +1,7 @@
 from flask import Response
 
 from . import openai_bp
-from ..cache import flask_cache
+from llm_server.custom_redis import flask_cache
 from ... import opts
 
 

llm_server/routes/openai/models.py

@@ -3,59 +3,58 @@ import traceback
 import requests
 from flask import jsonify
 
+from llm_server.custom_redis import ONE_MONTH_SECONDS, flask_cache, redis
 from . import openai_bp
-from ..cache import ONE_MONTH_SECONDS, flask_cache, redis
 from ..stats import server_start_time
 from ... import opts
+from ...cluster.cluster_config import cluster_config, get_a_cluster_backend
 from ...helpers import jsonify_pretty
-from ...llm.info import get_running_model
+from ...llm.openai.transform import generate_oai_string
 
 
 @openai_bp.route('/models', methods=['GET'])
 @flask_cache.cached(timeout=60, query_string=True)
 def openai_list_models():
-    model, error = get_running_model()
-    if not model:
+    model_name = cluster_config.get_backend(get_a_cluster_backend()).get('model')
+    if not model_name:
         response = jsonify({
             'code': 502,
             'msg': 'failed to reach backend',
-            '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 = []
+        r = {
+            "object": "list",
+            "data": oai
+        }
+        # TODO: verify this works
         if opts.openai_expose_our_model:
-            r = [{
-                "object": "list",
-                "data": [
+            r["data"].insert(0, {
+                "id": running_model,
+                "object": "model",
+                "created": int(server_start_time.timestamp()),
+                "owned_by": opts.llm_middleware_name,
+                "permission": [
                     {
                         "id": running_model,
-                        "object": "model",
+                        "object": "model_permission",
                         "created": int(server_start_time.timestamp()),
-                        "owned_by": opts.llm_middleware_name,
-                        "permission": [
-                            {
-                                "id": running_model,
-                                "object": "model_permission",
-                                "created": int(server_start_time.timestamp()),
-                                "allow_create_engine": False,
-                                "allow_sampling": False,
-                                "allow_logprobs": False,
-                                "allow_search_indices": False,
-                                "allow_view": True,
-                                "allow_fine_tuning": False,
-                                "organization": "*",
-                                "group": None,
-                                "is_blocking": False
-                            }
-                        ],
-                        "root": None,
-                        "parent": None
+                        "allow_create_engine": False,
+                        "allow_sampling": False,
+                        "allow_logprobs": False,
+                        "allow_search_indices": False,
+                        "allow_view": True,
+                        "allow_fine_tuning": False,
+                        "organization": "*",
+                        "group": None,
+                        "is_blocking": False
                     }
-                ]
-            }]
-        response = jsonify_pretty(r + oai), 200
+                ],
+                "root": None,
+                "parent": None
+            })
+        response = jsonify_pretty(r), 200
     return response
 
 
@@ -64,7 +63,14 @@ def fetch_openai_models():
     if opts.openai_api_key:
         try:
             response = requests.get('https://api.openai.com/v1/models', headers={'Authorization': f"Bearer {opts.openai_api_key}"}, timeout=10)
-            return response.json()['data']
+            j = response.json()['data']
+
+            # The "modelperm" string appears to be user-specific, so we'll
+            # randomize it just to be safe.
+            for model in range(len(j)):
+                for p in range(len(j[model]['permission'])):
+                    j[model]['permission'][p]['id'] = f'modelperm-{generate_oai_string(24)}'
+            return j
         except:
             traceback.print_exc()
             return []

llm_server/routes/openai/simulated.py

@@ -1,7 +1,7 @@
 from flask import jsonify
 
 from . import openai_bp
-from ..cache import ONE_MONTH_SECONDS, flask_cache
+from llm_server.custom_redis import ONE_MONTH_SECONDS, flask_cache
 from ...llm.openai.transform import generate_oai_string
 from ..stats import server_start_time
 
@@ -17,7 +17,7 @@ def openai_organizations():
                 "id": f"org-{generate_oai_string(24)}",
                 "created": int(server_start_time.timestamp()),
                 "title": "Personal",
-                "name": "user-abcdefghijklmnopqrstuvwx",
+                "name": f"user-{generate_oai_string(24)}",
                 "description": "Personal org for bobjoe@0.0.0.0",
                 "personal": True,
                 "is_default": True,

llm_server/routes/openai_request_handler.py

@@ -1,14 +1,21 @@
 import json
+import re
+import time
 import traceback
 from typing import Tuple
 from uuid import uuid4
 
 import flask
-from flask import jsonify
+from flask import Response, jsonify, make_response
 
 from llm_server import opts
+from llm_server.cluster.backend import get_model_choices
+from llm_server.custom_redis import redis
 from llm_server.database.database import is_api_key_moderated
-from llm_server.llm.openai.transform import build_openai_response, transform_messages_to_prompt, trim_prompt_to_fit
+from llm_server.database.log_to_db import log_to_db
+from llm_server.llm import get_token_count
+from llm_server.llm.openai.oai_to_vllm import oai_to_vllm, validate_oai, return_invalid_model_err
+from llm_server.llm.openai.transform import ANTI_CONTINUATION_RE, ANTI_RESPONSE_RE, generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
 from llm_server.routes.request_handler import RequestHandler
 from llm_server.workers.moderator import add_moderation_task, get_results
 
@@ -20,20 +27,37 @@ class OpenAIRequestHandler(RequestHandler):
 
     def handle_request(self) -> Tuple[flask.Response, int]:
         assert not self.used
+        if self.offline:
+            msg = return_invalid_model_err(self.selected_model)
+            print('OAI Offline:', msg)
+            return self.handle_error(msg)
 
         if opts.openai_silent_trim:
-            oai_messages = trim_prompt_to_fit(self.request.json['messages'], opts.context_size)
+            oai_messages = trim_messages_to_fit(self.request.json['messages'], self.cluster_backend_info['model_config']['max_position_embeddings'], self.backend_url)
         else:
             oai_messages = self.request.json['messages']
 
         self.prompt = transform_messages_to_prompt(oai_messages)
+        self.request_json_body = oai_to_vllm(self.request_json_body, stop_hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
+
         request_valid, invalid_response = self.validate_request()
         if not request_valid:
             return invalid_response
 
-        if opts.openai_api_key and is_api_key_moderated(self.token):
+        if not self.prompt:
+            # TODO: format this as an openai error message
+            return Response('Invalid prompt'), 400
+
+        # TODO: support Ooba backend
+        self.parameters = oai_to_vllm(self.parameters, stop_hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
+
+        invalid_oai_err_msg = validate_oai(self.request_json_body)
+        if invalid_oai_err_msg:
+            return invalid_oai_err_msg
+
+        if opts.openai_moderation_enabled and opts.openai_api_key and is_api_key_moderated(self.token):
             try:
-                # Gather the last message from the user and all preceeding system messages
+                # Gather the last message from the user and all preceding system messages
                 msg_l = self.request.json['messages'].copy()
                 msg_l.reverse()
                 tag = uuid4()
@@ -49,33 +73,40 @@ class OpenAIRequestHandler(RequestHandler):
                     self.prompt = transform_messages_to_prompt(self.request.json['messages'])
             except Exception as e:
                 print(f'OpenAI moderation endpoint failed:', f'{e.__class__.__name__}: {e}')
-                print(traceback.format_exc())
-
-        # Reconstruct the request JSON with the validated parameters and prompt.
-        self.parameters['stop'].extend(['\n### INSTRUCTION', '\n### USER', '\n### ASSISTANT', '\n### RESPONSE'])
-        if opts.openai_force_no_hashes:
-            self.parameters['stop'].append('### ')
-
-        if opts.mode == 'vllm' and self.request_json_body.get('top_p') == 0:
-            self.request_json_body['top_p'] = 0.01
+                traceback.print_exc()
 
         llm_request = {**self.parameters, 'prompt': self.prompt}
         (success, _, _, _), (backend_response, backend_response_status_code) = self.generate_response(llm_request)
-
         model = self.request_json_body.get('model')
 
         if success:
-            return build_openai_response(self.prompt, backend_response.json['results'][0]['text'], model=model), backend_response_status_code
+            return self.build_openai_response(self.prompt, backend_response.json['results'][0]['text'], model=model), backend_response_status_code
         else:
             return backend_response, backend_response_status_code
 
     def handle_ratelimited(self, do_log: bool = True):
-        # TODO: return a simulated OpenAI error message
-        # 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.
-        return 'Ratelimited', 429
+        model_choices, default_model = get_model_choices()
+        default_model_info = model_choices[default_model]
+        w = int(default_model_info['estimated_wait']) if default_model_info['estimated_wait'] > 0 else 2
+        response = jsonify({
+            "error": {
+                "message": "Rate limit reached on tokens per min. Limit: 10000 / min. Please try again in 6s. Contact us through our help center at help.openai.com if you continue to have issues.",
+                "type": "rate_limit_exceeded",
+                "param": None,
+                "code": None
+            }
+        })
+        response.headers['x-ratelimit-limit-requests'] = '2'
+        response.headers['x-ratelimit-remaining-requests'] = '0'
+        response.headers['x-ratelimit-reset-requests'] = f"{w}s"
+
+        if do_log:
+            log_to_db(self.client_ip, self.token, self.request_json_body.get('prompt', ''), response.data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.backend_url, is_error=True)
+
+        return response, 429
 
     def handle_error(self, error_msg: str, error_type: str = 'error') -> Tuple[flask.Response, int]:
-        # TODO: return a simulated OpenAI error message
+        print('OAI Error:', error_msg)
         return jsonify({
             "error": {
                 "message": "Invalid request, check your parameters and try again.",
@@ -84,3 +115,51 @@ class OpenAIRequestHandler(RequestHandler):
                 "code": None
             }
         }), 400
+
+    def build_openai_response(self, prompt, response, model=None):
+        # Seperate the user's prompt from the context
+        x = prompt.split('### USER:')
+        if len(x) > 1:
+            prompt = re.sub(r'\n$', '', x[-1].strip(' '))
+
+        # Make sure the bot doesn't put any other instructions in its response
+        response = re.sub(ANTI_RESPONSE_RE, '', response)
+        response = re.sub(ANTI_CONTINUATION_RE, '', response)
+
+        prompt_tokens = get_token_count(prompt, self.backend_url)
+        response_tokens = get_token_count(response, self.backend_url)
+        running_model = redis.get('running_model', 'ERROR', dtype=str)
+
+        response = make_response(jsonify({
+            "id": f"chatcmpl-{generate_oai_string(30)}",
+            "object": "chat.completion",
+            "created": int(time.time()),
+            "model": running_model if opts.openai_expose_our_model else model,
+            "choices": [{
+                "index": 0,
+                "message": {
+                    "role": "assistant",
+                    "content": response,
+                },
+                "logprobs": None,
+                "finish_reason": "stop"
+            }],
+            "usage": {
+                "prompt_tokens": prompt_tokens,
+                "completion_tokens": response_tokens,
+                "total_tokens": prompt_tokens + response_tokens
+            }
+        }), 200)
+        return response
+
+    def validate_request(self, prompt: str = None, do_log: bool = False) -> Tuple[bool, Tuple[Response | None, int]]:
+        self.parameters, parameters_invalid_msg = self.get_parameters()
+        if not self.parameters:
+            print('OAI BACKEND VALIDATION ERROR:', parameters_invalid_msg)
+            return False, (Response('Invalid request, check your parameters and try again.'), 400)
+        invalid_oai_err_msg = validate_oai(self.parameters)
+        if invalid_oai_err_msg:
+            return False, invalid_oai_err_msg
+        # self.request_json_body = oai_to_vllm(self.request_json_body, stop_hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
+        # If the parameters were invalid, let the superclass deal with it.
+        return super().validate_request(prompt, do_log)

llm_server/routes/queue.py

@@ -1,12 +1,15 @@
-import json
 import pickle
 import time
+from typing import Tuple
 from uuid import uuid4
 
+import ujson as json
 from redis import Redis
 
 from llm_server import opts
-from llm_server.routes.cache import redis
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.custom_redis import RedisCustom, redis
+from llm_server.database.database import get_token_ratelimit
 
 
 def increment_ip_count(client_ip: str, redis_key):
@@ -20,24 +23,30 @@ def decrement_ip_count(client_ip: str, redis_key):
 
 
 class RedisPriorityQueue:
-    def __init__(self):
-        self.redis = Redis(host='localhost', port=6379, db=15)
-        self.pubsub = self.redis.pubsub()
-        self.pubsub.subscribe('events')
+    """
+    A queue for a specific backend.
+    """
 
-    def put(self, item, priority):
-        event = DataEvent()
+    def __init__(self, name, db: int = 12):
+        self.name = name
+        self.redis = RedisCustom(name, db=db)
+
+    def put(self, item, priority: int, selected_model: str, do_stream: bool = False):
+        # TODO: remove this when we're sure nothing strange is happening
+        assert item is not None
+        assert priority is not None
+        assert selected_model is not None
 
         # Check if the IP is already in the dictionary and if it has reached the limit
-        ip_count = self.redis.hget('queued_ip_count', item[1])
-        if ip_count:
-            ip_count = int(ip_count)
-        if ip_count and int(ip_count) >= opts.simultaneous_requests_per_ip and priority != 0:
-            print(f'Rejecting request from {item[1]} - {ip_count} requests in progress.')
+        ip_count = self.get_ip_request_count(item[1])
+        _, simultaneous_ip = get_token_ratelimit(item[2])
+        if ip_count and int(ip_count) >= simultaneous_ip and priority != 0:
+            print(f'Rejecting request from {item[1]} - {ip_count} request queued.')
             return None  # reject the request
 
-        self.redis.zadd('queue', {json.dumps((item, event.event_id)): -priority})
-        self.increment_ip_count(item[1], 'queued_ip_count')
+        timestamp = time.time()
+        event = DataEvent()
+        self.redis.zadd('queue', {json.dumps((item, event.event_id, selected_model, timestamp, do_stream)): -priority})
         return event
 
     def get(self):
@@ -45,31 +54,59 @@ class RedisPriorityQueue:
             data = self.redis.zpopmin('queue')
             if data:
                 item = json.loads(data[0][0])
-                client_ip = item[0][1]
-                self.decrement_ip_count(client_ip, 'queued_ip_count')
                 return item
             time.sleep(0.1)  # wait for something to be added to the queue
 
-    def increment_ip_count(self, client_ip: str, redis_key):
-        self.redis.hincrby(redis_key, client_ip, 1)
-
-    def decrement_ip_count(self, client_ip: str, redis_key):
-        new_count = self.redis.hincrby(redis_key, client_ip, -1)
-        if new_count <= 0:
-            self.redis.hdel(redis_key, client_ip)
-
     def __len__(self):
         return self.redis.zcard('queue')
 
-    def get_queued_ip_count(self, client_ip: str):
-        q = self.redis.hget('queued_ip_count', client_ip)
-        if not q:
-            return 0
-        return 0
+    def get_ip_request_count(self, client_ip: str):
+        """
+        Get the number of requests in the queue from a specific IP.
+        This is a bit inefficient since we iterate over the entire queue, but
+        keeps the queue as a single point of truth instead of tracking a separate hashed
+        set which can get confusing.
+        If we run into slowdowns in the future, we should go back to the hashed set approach.
+        :param client_ip:
+        :return:
+        """
+        start_time = time.time()
+        items = self.redis.zrange('queue', 0, -1)
+        count = 0
+        for item in items:
+            item_data = json.loads(item)
+            if item_data[0][1] == client_ip:
+                count += 1
+        elapsed_time = time.time() - start_time
+        if elapsed_time > 0.5:
+            raise Exception(f"!!! get_ip_request_count took {elapsed_time} seconds to execute !!!")
+        return count
+
+    def flush(self):
+        self.redis.flush()
+
+    def items(self):
+        return self.redis.zrange('queue', 0, -1)
+
+    def cleanup(self):
+        now = time.time()
+        for item in self.items():
+            item_data = json.loads(item)
+            timestamp = item_data[-2]
+            if now - timestamp > opts.backend_generate_request_timeout:
+                self.redis.zrem('queue', 0, item)
+                event_id = item_data[1]
+                event = DataEvent(event_id)
+                event.set((False, None, 'closed'))
+                print('Removed timed-out item from queue:', event_id)
 
 
 class DataEvent:
-    def __init__(self, event_id=None):
+    """
+    Class to simplify pub/sub communication between consumers and producers (MASTERS and SLAVES lololololol).
+    """
+
+    def __init__(self, event_id: str = None):
         self.event_id = event_id if event_id else str(uuid4())
         self.redis = Redis(host='localhost', port=6379, db=14)
         self.pubsub = self.redis.pubsub()
@@ -84,15 +121,89 @@ class DataEvent:
                 return pickle.loads(item['data'])
 
 
-priority_queue = RedisPriorityQueue()
+def update_active_workers(key: str, operation: str):
+    if operation == 'incr':
+        redis.incr(f'active_gen_workers:{key}')
+    elif operation == 'decr':
+        redis.decr(f'active_gen_workers:{key}')
+        if redis.get(f'active_gen_workers:{key}', default=0, dtype=int) < 0:
+            redis.set(f'active_gen_workers:{key}', 0)
 
 
-def incr_active_workers():
-    redis.incr('active_gen_workers')
+def incr_active_workers(selected_model: str, backend_url: str):
+    update_active_workers(selected_model, 'incr')
+    update_active_workers(backend_url, 'incr')
 
 
-def decr_active_workers():
-    redis.decr('active_gen_workers')
-    new_count = redis.get('active_gen_workers', int, 0)
-    if new_count < 0:
-        redis.set('active_gen_workers', 0)
+def decr_active_workers(selected_model: str, backend_url: str):
+    update_active_workers(selected_model, 'decr')
+    update_active_workers(backend_url, 'decr')
+
+
+class PriorityQueue:
+    """
+    Helper class to wrangler all the different queues.
+    """
+
+    def __init__(self, backends: set = None):
+        """
+        Only have to load the backends once.
+        :param backends:
+        """
+        self.redis = Redis(host='localhost', port=6379, db=9)
+        if backends:
+            for item in backends:
+                self.redis.sadd('backends', item)
+
+    def get_backends(self):
+        return {x.decode('utf-8') for x in self.redis.smembers('backends')}
+
+    def get_queued_ip_count(self, client_ip: str):
+        count = 0
+        for backend_url in self.get_backends():
+            queue = RedisPriorityQueue(backend_url)
+            count += queue.get_ip_request_count(client_ip)
+        return count
+
+    def put(self, backend_url, item: Tuple[dict, str, str, dict], priority: int, selected_model: str, do_stream: bool = False):
+        queue = RedisPriorityQueue(backend_url)
+        return queue.put(item, priority, selected_model, do_stream)
+
+    def activity(self):
+        lines = []
+        status_redis = RedisCustom('worker_status')
+        for worker in status_redis.keys():
+            lines.append((worker, status_redis.getp(worker)))
+        return sorted(lines)
+
+    def len(self, model_name):
+        count = 0
+        backends_with_models = set()
+        for k in self.get_backends():
+            info = cluster_config.get_backend(k)
+            if info.get('model') == model_name:
+                backends_with_models.add(k)
+        for backend_url in backends_with_models:
+            count += len(RedisPriorityQueue(backend_url))
+        return count
+
+    def __len__(self):
+        count = 0
+        p = set()
+        for backend_url in self.get_backends():
+            queue = RedisPriorityQueue(backend_url)
+            p.add((backend_url, len(queue)))
+            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()

llm_server/routes/request_handler.py

@@ -5,23 +5,22 @@ import flask
 from flask import Response, request
 
 from llm_server import opts
-from llm_server.database.conn import database
-from llm_server.database.database import log_prompt
+from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
+from llm_server.custom_redis import redis
+from llm_server.database.database import get_token_ratelimit
+from llm_server.database.log_to_db import log_to_db
 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.routes.cache import redis
 from llm_server.routes.helpers.http import require_api_key, validate_json
 from llm_server.routes.queue import priority_queue
 
-DEFAULT_PRIORITY = 9999
-
 
 class RequestHandler:
-    def __init__(self, incoming_request: flask.Request, incoming_json: Union[dict, str] = None):
+    def __init__(self, incoming_request: flask.Request, selected_model: str = None, incoming_json: Union[dict, str] = None):
         self.request = incoming_request
-        self.enable_backend_blind_rrd = request.headers.get('LLM-Blind-RRD', False) == 'true'
+        # self.enable_backend_blind_rrd = request.headers.get('LLM-Blind-RRD', False) == 'true'
 
         # Routes need to validate it, here we just load it
         if incoming_json:
@@ -34,11 +33,38 @@ class RequestHandler:
         self.start_time = time.time()
         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.token_priority, self.token_simultaneous_ip = get_token_ratelimit(self.token)
         self.parameters = None
         self.used = False
-        redis.zadd('recent_prompters', {self.client_ip: time.time()})
+
+        # This is null by default since most handlers need to transform the prompt in a specific way.
+        self.prompt = None
+
+        self.selected_model = selected_model
+        self.backend_url = get_a_cluster_backend(selected_model)
+        self.cluster_backend_info = cluster_config.get_backend(self.backend_url)
+
+        # Debug stuff
+        # if not self.cluster_backend_info.get('mode'):
+        #     print('keyerror: mode -', selected_model, self.backend_url, self.cluster_backend_info)
+        # if not self.cluster_backend_info.get('model'):
+        #     print('keyerror: model -', selected_model, self.backend_url, self.cluster_backend_info)
+        # if not self.cluster_backend_info.get('model_config'):
+        #     print('keyerror: model_config -', selected_model, self.backend_url, self.cluster_backend_info)
+
+        if not self.cluster_backend_info.get('mode') or not self.cluster_backend_info.get('model') or not self.cluster_backend_info.get('model_config'):
+            self.offline = True
+        else:
+            self.offline = False
+            self.selected_model = self.cluster_backend_info['model']
+            self.backend = get_backend_handler(self.cluster_backend_info['mode'], self.backend_url)
+            if self.token and not self.token.startswith('SYSTEM__'):
+                # "recent_prompters" is only used for stats.
+                redis.zadd('recent_prompters', {self.client_ip: time.time()})
+
+    def check_online(self) -> bool:
+        self.cluster_backend_info = cluster_config.get_backend(self.backend_url)
+        return self.cluster_backend_info['online']
 
     def get_auth_token(self):
         if self.request_json_body.get('X-API-KEY'):
@@ -49,6 +75,8 @@ class RequestHandler:
             return parse_token(self.request.headers['Authorization'])
 
     def get_client_ip(self):
+        if self.request.headers.get('Llm-Connecting-Ip'):
+            return self.request.headers['Llm-Connecting-Ip']
         if self.request.headers.get('X-Connecting-IP'):
             return self.request.headers.get('X-Connecting-IP')
         elif self.request.headers.get('Cf-Connecting-Ip'):
@@ -58,26 +86,7 @@ class RequestHandler:
         else:
             return self.request.remote_addr
 
-    def get_token_ratelimit(self):
-        priority = DEFAULT_PRIORITY
-        simultaneous_ip = opts.simultaneous_requests_per_ip
-        if self.token:
-            cursor = database.cursor()
-            try:
-                cursor.execute("SELECT priority, simultaneous_ip FROM token_auth WHERE token = %s", (self.token,))
-                result = cursor.fetchone()
-                if result:
-                    priority, simultaneous_ip = result
-                    if simultaneous_ip is None:
-                        # No ratelimit for this token if null
-                        simultaneous_ip = 999999999
-            finally:
-                cursor.close()
-        return priority, simultaneous_ip
-
     def get_parameters(self):
-        if self.request_json_body.get('max_tokens'):
-            self.request_json_body['max_new_tokens'] = self.request_json_body.pop('max_tokens')
         parameters, parameters_invalid_msg = self.backend.get_parameters(self.request_json_body)
         return parameters, parameters_invalid_msg
 
@@ -119,7 +128,7 @@ 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_to_db(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.backend_url, is_error=True)
             return False, backend_response
         return True, (None, 0)
 
@@ -131,14 +140,18 @@ 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(self.backend_url, (llm_request, self.client_ip, self.token, self.parameters), self.token_priority, self.selected_model)
         else:
             event = None
 
         if not event:
             return (False, None, None, 0), self.handle_ratelimited()
 
+        # TODO: add wait timeout
         success, response, error_msg = event.wait()
+        if error_msg == 'closed':
+            return (False, None, None, 0), (self.handle_error('Request Timeout')[0], 408)
+
         end_time = time.time()
         elapsed_time = end_time - self.start_time
 
@@ -160,7 +173,17 @@ 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_to_db(ip=self.client_ip,
+                      token=self.token,
+                      prompt=prompt,
+                      response=backend_response[0].data.decode('utf-8'),
+                      gen_time=None,
+                      parameters=self.parameters,
+                      headers=dict(self.request.headers),
+                      backend_response_code=response_status_code,
+                      request_url=self.request.url,
+                      backend_url=self.backend_url,
+                      is_error=True)
             return (False, None, None, 0), backend_response
 
         # ===============================================
@@ -180,7 +203,7 @@ 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_to_db(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.backend_url, is_error=True)
             return (False, None, None, 0), backend_response
 
         # ===============================================
@@ -189,22 +212,29 @@ class RequestHandler:
         return (success, response, error_msg, elapsed_time), self.backend.handle_response(success, self.request, response_json_body, response_status_code, self.client_ip, self.token, prompt, elapsed_time, self.parameters, dict(self.request.headers))
 
     def is_client_ratelimited(self) -> bool:
+        if self.token_priority == 0:
+            return False
+
         queued_ip_count = int(priority_queue.get_queued_ip_count(self.client_ip))
         x = redis.hget('processing_ips', self.client_ip)
         if x:
             processing_ip = int(x)
         else:
             processing_ip = 0
-        if queued_ip_count + processing_ip < self.token_simultaneous_ip or self.token_priority == 0:
-            return False
-        else:
-            print(f'Rejecting request from {self.client_ip} - {queued_ip_count + processing_ip} queued + processing.')
+
+        if queued_ip_count + processing_ip >= self.token_simultaneous_ip:
+            print(f'Rejecting request from {self.client_ip} - {processing_ip} processing, {queued_ip_count} queued')
             return True
+        else:
+            return False
 
     def handle_request(self) -> Tuple[flask.Response, int]:
         # Must include this in your child.
-        # if self.used:
-        #     raise Exception('Can only use a RequestHandler object once.')
+        # assert not self.used
+        # if self.offline:
+        #     msg = f'{self.selected_model} is not a valid model choice.'
+        #     print(msg)
+        #     return format_sillytavern_err(msg)
         raise NotImplementedError
 
     def handle_ratelimited(self, do_log: bool = True) -> Tuple[flask.Response, int]:
@@ -214,11 +244,11 @@ class RequestHandler:
         raise NotImplementedError
 
 
-def get_backend():
-    if opts.mode == 'oobabooga':
-        return OobaboogaBackend()
-    elif opts.mode == 'vllm':
-        return VLLMBackend()
+def get_backend_handler(mode, backend_url: str):
+    if mode == 'oobabooga':
+        return OobaboogaBackend(backend_url)
+    elif mode == 'vllm':
+        return VLLMBackend(backend_url)
     else:
         raise Exception
 

llm_server/routes/server_error.py

@@ -1,3 +1,3 @@
 def handle_server_error(e):
-    print(e)
-    return {'error': True}, 500
+    print('Internal Error:', e)
+    return {'error': True, 'code': 500, 'message': 'Internal Server Error :('}, 500

llm_server/routes/stats.py

@@ -1,33 +1,11 @@
 from datetime import datetime
 
-from llm_server.routes.cache import redis
-
-# proompters_5_min = 0
-# concurrent_semaphore = Semaphore(concurrent_gens)
+from llm_server.custom_redis import redis
+from llm_server.helpers import round_up_base
 
 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:
@@ -37,10 +15,27 @@ def get_total_proompts():
     return count
 
 
-def get_active_gen_workers():
-    active_gen_workers = redis.get('active_gen_workers')
-    if active_gen_workers is None:
-        count = 0
+def get_active_gen_workers_model(selected_model: str = None):
+    return redis.get(f'active_gen_workers:{selected_model}', dtype=int, default=0)
+
+
+def calculate_wait_time(gen_time_calc, proompters_in_queue, concurrent_gens, active_gen_workers):
+    if active_gen_workers < concurrent_gens:
+        return 0
+    elif active_gen_workers >= concurrent_gens:
+        # Calculate how long it will take to complete the currently running gens and the queued requests.
+        # If the proompters in the queue are equal to the number of workers, just use the calculated generation time.
+        # Otherwise, use how many requests we can process concurrently times the calculated generation time. Then, round
+        # that number up to the nearest base gen_time_calc (ie. if gen_time_calc is 8 and the calculated number is 11.6, we will get 18). Finally,
+        # Add gen_time_calc to the time to account for the currently running generations.
+        # This assumes that all active workers will finish at the same time, which is unlikely.
+        # Regardless, this is the most accurate estimate we can get without tracking worker elapsed times.
+        proompters_in_queue_wait_time = gen_time_calc if (proompters_in_queue / concurrent_gens) <= 1 \
+            else round_up_base(((proompters_in_queue / concurrent_gens) * gen_time_calc), base=gen_time_calc)
+        return proompters_in_queue_wait_time + gen_time_calc if active_gen_workers > 0 else 0
+    elif proompters_in_queue == 0 and active_gen_workers == 0:
+        # No queue, no workers
+        return 0
     else:
-        count = int(active_gen_workers)
-    return count
+        # No queue
+        return gen_time_calc

llm_server/routes/v1/generate.py

@@ -3,18 +3,18 @@ import traceback
 from flask import jsonify, request
 
 from . import bp
-from ..helpers.client import format_sillytavern_err
 from ..helpers.http import validate_json
 from ..ooba_request_handler import OobaRequestHandler
 
 
-@bp.route('/generate', methods=['POST'])
-def generate():
+@bp.route('/v1/generate', methods=['POST'])
+@bp.route('/<model_name>/v1/generate', methods=['POST'])
+def generate(model_name=None):
     request_valid_json, request_json_body = validate_json(request)
     if not request_valid_json or not request_json_body.get('prompt'):
         return jsonify({'code': 400, 'msg': 'Invalid JSON'}), 400
     else:
-        handler = OobaRequestHandler(request)
+        handler = OobaRequestHandler(request, selected_model=model_name)
         try:
             return handler.handle_request()
         except Exception:

llm_server/routes/v1/generate_stats.py

@@ -2,83 +2,30 @@ import time
 from datetime import datetime
 
 from llm_server import opts
+from llm_server.cluster.backend import get_model_choices
+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.routes.cache 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
+from llm_server.helpers import deep_sort
+from llm_server.routes.stats import get_total_proompts, server_start_time
 
 
-def calculate_wait_time(gen_time_calc, proompters_in_queue, concurrent_gens, active_gen_workers):
-    if active_gen_workers < concurrent_gens:
-        return 0
-    elif active_gen_workers >= concurrent_gens:
-        # Calculate how long it will take to complete the currently running gens and the queued requests.
-        # If the proompters in the queue are equal to the number of workers, just use the calculated generation time.
-        # Otherwise, use how many requests we can process concurrently times the calculated generation time. Then, round
-        # that number up to the nearest base gen_time_calc (ie. if gen_time_calc is 8 and the calculated number is 11.6, we will get 18). Finally,
-        # Add gen_time_calc to the time to account for the currently running generations.
-        # This assumes that all active workers will finish at the same time, which is unlikely.
-        # Regardless, this is the most accurate estimate we can get without tracking worker elapsed times.
-        proompters_in_queue_wait_time = gen_time_calc if (proompters_in_queue / concurrent_gens) <= 1 \
-            else round_up_base(((proompters_in_queue / concurrent_gens) * gen_time_calc), base=gen_time_calc)
-        return proompters_in_queue_wait_time + gen_time_calc if active_gen_workers > 0 else 0
-    elif proompters_in_queue == 0 and active_gen_workers == 0:
-        # No queue, no workers
-        return 0
-    else:
-        # No queue
-        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.getp('proxy_stats')
         if c:
             return c
 
-    model_name, error = get_running_model()  # will return False when the fetch fails
-    if isinstance(model_name, bool):
-        online = False
-    else:
-        online = True
-        redis.set('running_model', model_name)
+    model_choices, default_model = get_model_choices(regen=True)
 
-    # t = elapsed_times.copy()  # copy since we do multiple operations and don't want it to change
-    # if len(t) == 0:
-    #     estimated_wait = 0
-    # else:
-    #     waits = [elapsed for end, elapsed in t]
-    #     estimated_wait = int(sum(waits) / len(waits))
-
-    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.
-    # estimated_avg_tps = redis.get('estimated_avg_tps', float, default=0)
-
-    average_generation_time = redis.get('average_generation_elapsed_sec', float, default=0)
-    estimated_wait_sec = calculate_wait_time(average_generation_time, 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)
+    base_client_api = redis.get('base_client_api', dtype=str)
     proompters_5_min = len(redis.zrangebyscore('recent_prompters', time.time() - 5 * 60, '+inf'))
 
     output = {
+        'models': {
+            'choices': model_choices,
+            'default': default_model,
+        },
         'stats': {
             'proompters': {
                 '5_min': proompters_5_min,
@@ -86,39 +33,49 @@ 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),
             # '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': {
             'blocking': f'https://{base_client_api}',
             'streaming': f'wss://{base_client_api}/v1/stream' if opts.enable_streaming else None,
         },
-        'queue': {
-            'processing': active_gen_workers,
-            'queued': proompters_in_queue,
-            'estimated_wait_sec': int(estimated_wait_sec),
-        },
         '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,
+            'api_mode': opts.frontend_api_mode
         },
         'keys': {
             'openaiKeys': '∞',
             'anthropicKeys': '∞',
         },
-        'backend_info': redis.get_dict('backend_info') if opts.show_backend_info else None,
-        'nvidia': netdata_stats
+        'backends': {},
+        'online': len(model_choices) > 0
     }
+
+    # TODO: have get_model_choices() return all the info so we don't have to loop over the backends ourself
+
+    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
+            backend_uptime = int((datetime.now() - datetime.fromtimestamp(backend_info['startup_time'])).total_seconds()) if opts.show_uptime else None
+            output['backends'][backend_info['hash']] = {
+                'uptime': backend_uptime,
+                'max_tokens': backend_info['model_config'].get('max_position_embeddings', -1),
+                'model': backend_info['model'],
+                'mode': backend_info['mode'],
+                'nvidia': backend_info['nvidia'],
+                'priority': backend_info['priority'],
+            }
+
     result = deep_sort(output)
 
     # It may take a bit to get the base client API, so don't cache until then.
     if base_client_api:
-        redis.set_dict('proxy_stats', result)  # Cache with no expiry
+        redis.setp('proxy_stats', result)
+
     return result

llm_server/routes/v1/generate_stream.py

@@ -1,186 +1,200 @@
 import json
-import threading
 import time
 import traceback
-from typing import Union
 
+import ujson
 from flask import request
+from redis import Redis
 
-from ..cache import redis
+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 ...database.database import log_prompt
-from ...llm.generator import generator
-from ...llm.vllm import tokenize
-from ...stream import sock
+from ...custom_redis import redis
+from ...database.log_to_db import log_to_db
+from ...sock import sock
 
 
-# TODO: have workers process streaming requests
-# TODO: make sure to log the token as well (seems to be missing in the DB right now)
+# Stacking the @sock.route() creates a TypeError error on the /v1/stream endpoint.
+# We solve this by splitting the routes
 
-@sock.route('/api/v1/stream')
-def stream(ws):
-    def send_err_and_quit(quitting_err_msg):
-        ws.send(json.dumps({
-            'event': 'text_stream',
-            'message_num': 0,
-            'text': quitting_err_msg
-        }))
-        ws.send(json.dumps({
-            'event': 'stream_end',
-            'message_num': 1
-        }))
-        ws.close()
-        log_in_bg(quitting_err_msg, is_error=True)
+@bp.route('/v1/stream')
+@bp.route('/<model_name>/v1/stream')
+def stream(model_name=None):
+    return 'This is a websocket endpoint.', 400
 
-    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)
+@sock.route('/v1/stream', bp=bp)
+def stream_without_model(ws):
+    do_stream(ws, model_name=None)
 
-        # 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
+@sock.route('/<model_name>/v1/stream', bp=bp)
+def stream_with_model(ws, model_name=None):
+    do_stream(ws, model_name)
 
-    r_headers = dict(request.headers)
-    r_url = request.url
-    message_num = 0
-    while ws.connected:
-        message = ws.receive()
-        request_valid_json, request_json_body = validate_json(message)
-        if not request_valid_json or not request_json_body.get('prompt'):
-            return 'Invalid JSON', 400
-        else:
-            if opts.mode != 'vllm':
-                # TODO: implement other backends
-                raise NotImplementedError
 
-            auth_failure = require_api_key(request_json_body)
-            if auth_failure:
-                return auth_failure
+def do_stream(ws, model_name):
+    event_id = None
+    try:
+        def send_err_and_quit(quitting_err_msg):
+            ws.send(json.dumps({
+                'event': 'text_stream',
+                'message_num': 0,
+                'text': quitting_err_msg
+            }))
+            ws.send(json.dumps({
+                'event': 'stream_end',
+                'message_num': 1
+            }))
+            ws.close()
+            log_to_db(ip=handler.client_ip,
+                      token=handler.token,
+                      prompt=input_prompt,
+                      response=quitting_err_msg,
+                      gen_time=None,
+                      parameters=handler.parameters,
+                      headers=r_headers,
+                      backend_response_code=response_status_code,
+                      request_url=r_url,
+                      backend_url=handler.backend_url,
+                      response_tokens=None,
+                      is_error=True
+                      )
 
-            handler = OobaRequestHandler(request, request_json_body)
-            generated_text = ''
-            input_prompt = request_json_body['prompt']
-            response_status_code = 0
-            start_time = time.time()
+        if not opts.enable_streaming:
+            return 'Streaming disabled', 403
 
-            err_msg = None
-            if handler.is_client_ratelimited():
-                r, _ = handler.handle_ratelimited(do_log=False)
-                err_msg = r.json['results'][0]['text']
+        r_headers = dict(request.headers)
+        r_url = request.url
+        message_num = 0
+
+        while ws.connected:
+            message = ws.receive()
+            request_valid_json, request_json_body = validate_json(message)
+
+            if not request_valid_json or not request_json_body.get('prompt'):
+                return 'Invalid JSON', 400
             else:
-                request_valid, invalid_response = handler.validate_request(prompt=input_prompt)
-                if not request_valid:
-                    err_msg = invalid_response[0].json['results'][0]['text']
-            if err_msg:
-                send_err_and_quit(err_msg)
-                return
+                # We have to do auth ourselves since the details are sent in the message.
+                auth_failure = require_api_key(request_json_body)
+                if auth_failure:
+                    return auth_failure
 
-            llm_request = {
-                **handler.parameters,
-                'prompt': input_prompt,
-                '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.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)
-                if not response:
-                    error_msg = 'Failed to reach backend while streaming.'
-                    print('Streaming failed:', error_msg)
-                    msg = handler.handle_error(error_msg)[0].json['results'][0]['text']
+                handler = OobaRequestHandler(incoming_request=request, selected_model=model_name, incoming_json=request_json_body)
+                if handler.offline:
+                    msg = f'{handler.selected_model} is not a valid model choice.'
+                    print(msg)
                     ws.send(json.dumps({
                         'event': 'text_stream',
-                        'message_num': message_num,
+                        'message_num': 0,
                         'text': msg
                     }))
+                    return
+
+                if handler.cluster_backend_info['mode'] != 'vllm':
+                    # TODO: implement other backends
+                    raise NotImplementedError
+
+                input_prompt = request_json_body['prompt']
+                response_status_code = 0
+                start_time = time.time()
+
+                err_msg = None
+                if handler.is_client_ratelimited():
+                    r, _ = handler.handle_ratelimited(do_log=False)
+                    err_msg = r.json['results'][0]['text']
                 else:
-                    # Be extra careful when getting attributes from the response object
-                    try:
-                        response_status_code = response.status_code
-                    except:
-                        response_status_code = 0
+                    request_valid, invalid_response = handler.validate_request(prompt=input_prompt)
+                    if not request_valid:
+                        err_msg = invalid_response[0].json['results'][0]['text']
+                if err_msg:
+                    send_err_and_quit(err_msg)
+                    return
 
-                    partial_response = b''
+                handler.parameters, _ = handler.get_parameters()
+                handler.prompt = input_prompt
+                handler.request_json_body = {
+                    'prompt': handler.prompt,
+                    **handler.parameters
+                }
 
-                    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(input_prompt + generated_text)[1]
-                                        generated_text = generated_text + new
-                                    except IndexError:
-                                        # ????
-                                        continue
-                                    try:
-                                        ws.send(json.dumps({
-                                            'event': 'text_stream',
-                                            'message_num': message_num,
-                                            'text': new
-                                        }))
-                                    except:
-                                        # The has client closed the stream.
-                                        if request:
-                                            request.close()
-                                        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))
-                                        return
+                event = None
+                if not handler.is_client_ratelimited():
+                    event = priority_queue.put(handler.backend_url, (handler.request_json_body, handler.client_ip, handler.token, handler.parameters), handler.token_priority, handler.selected_model, do_stream=True)
+                if not event:
+                    r = handler.handle_ratelimited()
+                    send_err_and_quit(r[0].data)
+                    return
+                event_id = event.event_id
 
+                _, stream_name, error_msg = event.wait()
+                if error_msg:
+                    print('Stream failed to start streaming:', error_msg)
+                    ws.close(reason=1014, message='Request Timeout')
+                    return
+
+                stream_redis = Redis(db=8)
+                generated_text = ''
+
+                try:
+                    last_id = '0-0'  # The ID of the last entry we read.
+                    while True:
+                        stream_data = stream_redis.xread({stream_name: last_id}, block=opts.redis_stream_timeout)
+                        if not stream_data:
+                            print(f"No message received in {opts.redis_stream_timeout / 1000} seconds, closing stream.")
+                            return
+                        else:
+                            for stream_index, item in stream_data[0][1]:
+                                last_id = stream_index
+                                data = ujson.loads(item[b'data'])
+                                if data['error']:
+                                    print(data['error'])
+                                    send_err_and_quit('Encountered exception while streaming.')
+                                    return
+                                elif data['new']:
+                                    ws.send(json.dumps({
+                                        'event': 'text_stream',
+                                        'message_num': message_num,
+                                        'text': data['new']
+                                    }))
                                     message_num += 1
-                                    partial_response = b''  # Reset the partial response
-
-                        # If there is no more data, break the loop
-                        if not chunk:
-                            break
-
-                end_time = time.time()
-                elapsed_time = end_time - start_time
-                log_in_bg(generated_text, elapsed_time_bg=elapsed_time, is_error=not response, status_code=response_status_code)
-            except:
-                traceback.print_exc()
-                generated_text = generated_text + '\n\n' + handler.handle_error('Encountered error while streaming.', 'exception')[0].json['results'][0]['text']
-                ws.send(json.dumps({
-                    'event': 'text_stream',
-                    'message_num': message_num,
-                    'text': generated_text
-                }))
-                if request:
-                    request.close()
-                ws.close()
-                log_in_bg(generated_text, is_error=True, status_code=response_status_code)
-                return
-            finally:
-                # The worker incremented it, we'll decrement it.
-                decrement_ip_count(handler.client_ip, 'processing_ips')
-                decr_active_workers()
-            try:
-                ws.send(json.dumps({
-                    'event': 'stream_end',
-                    'message_num': message_num
-                }))
-            except:
-                # 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))
-    ws.close()  # this is important if we encountered and error and exited early.
+                                    generated_text = generated_text + data['new']
+                                elif data['completed']:
+                                    return
+                except:
+                    send_err_and_quit('Encountered exception while streaming.')
+                    traceback.print_exc()
+                finally:
+                    try:
+                        ws.send(json.dumps({
+                            'event': 'stream_end',
+                            'message_num': message_num
+                        }))
+                    except:
+                        # The client closed the stream.
+                        pass
+                    if stream_name:
+                        stream_redis.delete(stream_name)
+                        end_time = time.time()
+                        elapsed_time = end_time - start_time
+                        log_to_db(ip=handler.client_ip,
+                                  token=handler.token,
+                                  prompt=input_prompt,
+                                  response=generated_text,
+                                  gen_time=elapsed_time,
+                                  parameters=handler.parameters,
+                                  headers=r_headers,
+                                  backend_response_code=response_status_code,
+                                  request_url=r_url,
+                                  backend_url=handler.backend_url
+                                  )
+    finally:
+        if event_id:
+            redis.publish(f'notifications:{event_id}', 'canceled')
+        try:
+            # Must close the connection or greenlets will complain.
+            ws.close()
+        except:
+            pass

llm_server/routes/v1/info.py

@@ -2,22 +2,16 @@ import time
 
 from flask import jsonify, request
 
+from llm_server.custom_redis import flask_cache
 from . import bp
-from ..auth import requires_auth
-from ..cache import flask_cache
 from ... import opts
-from ...llm.info import get_running_model
+from ...cluster.backend import get_backends_from_model, is_valid_model
+from ...cluster.cluster_config import cluster_config, get_a_cluster_backend
 
 
-# @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('/v1/model', methods=['GET'])
+@bp.route('/<model_name>/v1/model', methods=['GET'])
+def get_model(model_name=None):
     # 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
@@ -26,24 +20,21 @@ def get_model():
     if cached_response:
         return cached_response
 
-    model_name, error = get_running_model()
     if not model_name:
+        model_name = cluster_config.get_backend(get_a_cluster_backend()).get('model')
+
+    if not is_valid_model(model_name):
         response = jsonify({
-            'code': 502,
-            'msg': 'failed to reach backend',
-            'type': error.__class__.__name__
-        }), 500  # return 500 so Cloudflare doesn't intercept us
+            'code': 400,
+            'msg': 'Model does not exist.',
+        }), 400
     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)
 
     return response
-
-
-@bp.route('/backend', methods=['GET'])
-@requires_auth
-def get_backend():
-    return jsonify({'backend': opts.backend_url, 'mode': opts.mode}), 200

llm_server/routes/v1/proxy.py

@@ -1,8 +1,10 @@
 from flask import jsonify
 
+from llm_server.custom_redis import flask_cache
 from . import bp
 from .generate_stats import generate_stats
-from ..cache import flask_cache
+from ..auth import requires_auth
+from ...cluster.cluster_config import cluster_config, get_backends
 from ...helpers import jsonify_pretty
 
 
@@ -10,3 +12,14 @@ from ...helpers import jsonify_pretty
 @flask_cache.cached(timeout=5, query_string=True)
 def get_stats():
     return jsonify_pretty(generate_stats())
+
+
+@bp.route('/backends', methods=['GET'])
+@requires_auth
+def get_backend():
+    online, offline = get_backends()
+    result = {}
+    for i in online + offline:
+        info = cluster_config.get_backend(i)
+        result[info['hash']] = info
+    return jsonify(result), 200

llm_server/sock.py

@@ -3,6 +3,6 @@ from flask_sock import Sock
 sock = Sock()
 
 
-def init_socketio(app):
+def init_wssocket(app):
     global sock
     sock.init_app(app)

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/blocking.py

@@ -1,51 +0,0 @@
-import threading
-import time
-
-from llm_server import opts
-from llm_server.llm.generator import generator
-from llm_server.routes.cache import redis
-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()
-        need_to_wait()
-
-        increment_ip_count(client_ip, 'processing_ips')
-        incr_active_workers()
-
-        if not request_json_body:
-            # This was a dummy request from the websocket handler.
-            # We're going to let the websocket handler decrement processing_ips and active_gen_workers.
-            continue
-
-        try:
-            success, response, error_msg = generator(request_json_body)
-            event = DataEvent(event_id)
-            event.set((success, response, error_msg))
-        finally:
-            decrement_ip_count(client_ip, 'processing_ips')
-            decr_active_workers()
-
-
-def start_workers(num_workers: int):
-    i = 0
-    for _ in range(num_workers):
-        t = threading.Thread(target=worker)
-        t.daemon = True
-        t.start()
-        i += 1
-    print(f'Started {i} inference workers.')
-
-
-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)
-    s = time.time()
-    while active_workers >= opts.concurrent_gens:
-        time.sleep(0.01)
-    e = time.time()
-    if e - s > 0.5:
-        print(f'Worker was delayed {e - s} seconds.')

llm_server/workers/cleaner.py

@@ -0,0 +1,32 @@
+import time
+
+from redis import Redis
+
+from llm_server.workers.inferencer import STREAM_NAME_PREFIX
+
+
+# NOT NEEDED
+
+def cleaner():
+    r = Redis(db=8)
+    stream_info = {}
+
+    while True:
+        all_streams = r.keys(f'{STREAM_NAME_PREFIX}:*')
+        processed_streams = []
+        for stream in all_streams:
+            stream = stream.decode()
+            current_size = r.xlen(stream)
+
+            # If the stream is new or its size has changed, update the size and time in the dictionary
+            if stream not in stream_info or current_size != stream_info[stream]['size']:
+                stream_info[stream] = {'size': current_size, 'time': time.time()}
+                processed_streams.append(stream)
+            else:
+                # If the size hasn't changed for 5 minutes, delete the stream
+                if time.time() - stream_info[stream]['time'] >= 300:
+                    r.delete(stream)
+                    print(f"Stream '{stream}' deleted due to inactivity.")
+                    del stream_info[stream]
+
+        time.sleep(60)

llm_server/workers/inferencer.py

@@ -0,0 +1,160 @@
+import json
+import threading
+import time
+import traceback
+from uuid import uuid4
+
+import ujson
+from redis import Redis
+
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.custom_redis import RedisCustom, redis
+from llm_server.llm.generator import generator
+from llm_server.logging import create_logger
+from llm_server.routes.queue import DataEvent, RedisPriorityQueue, decr_active_workers, decrement_ip_count, incr_active_workers, increment_ip_count
+
+stream_redis = Redis(db=8)
+
+STREAM_NAME_PREFIX = 'stream'
+
+
+def check_cancellation(event, event_id):
+    """
+    This thread checks the pub/sub channel in the background so the main process
+    isn't bogged down with Redis calls. Otherwise, the main process slows down to 1 token/sec.
+    :param event:
+    :param event_id:
+    :return:
+    """
+    pubsub = redis.pubsub()
+    pubsub.subscribe(f'notifications:{event_id}')
+    while not event.is_set():
+        message = pubsub.get_message()
+        if message and message['data'] == b'canceled':
+            event.set()
+        time.sleep(0.5)  # check every half second
+
+
+def get_stream_name(name: str):
+    return f'{STREAM_NAME_PREFIX}:{name}'
+
+
+def inference_do_stream(stream_name: str, msg_to_backend: dict, backend_url: str, event_id: str):
+    logger = create_logger('inferencer')
+    prompt = msg_to_backend['prompt']
+    stream_name = get_stream_name(stream_name)
+    stream_redis.delete(get_stream_name(stream_name))  # be extra sure
+    event = threading.Event()
+    threading.Thread(target=check_cancellation, args=(event, event_id)).start()
+    try:
+        response = generator(msg_to_backend, backend_url)
+        generated_text = ''
+        partial_response = b''
+        for chunk in response.iter_content(chunk_size=1):
+            # If there is no more data, break the loop
+            if not chunk:
+                break
+            if event.is_set():
+                logger.debug('Client canceled generation')
+                response.close()
+                return
+
+            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(prompt + generated_text)[1]
+                            generated_text = generated_text + new
+                        except IndexError:
+                            # ????
+                            continue
+                        stream_redis.xadd(stream_name, {'data': ujson.dumps({'new': new, 'completed': False, 'error': None})})
+    except AttributeError as e:
+        if str(e) == "'bool' object has no attribute 'iter_content'":
+            # We don't care about these errors.
+            logger.debug('failed to stream from backend - no response')
+        else:
+            raise
+    except Exception as e:
+        stream_redis.xadd(stream_name, {'data': ujson.dumps({'new': None, 'completed': True, 'error': f'{e.__class__.__name__}: {e}'})})
+        raise  # We won't handle the exception here.
+    finally:
+        # Publish final message to Redis stream
+        stream_redis.xadd(stream_name, {'data': ujson.dumps({'new': None, 'completed': True, 'error': None})})
+        event.set()  # stop the cancellation checking thread
+
+
+#
+def worker(backend_url):
+    logger = create_logger('inferencer')
+    status_redis = RedisCustom('worker_status')
+    worker_id = str(uuid4())
+    status_redis.setp(str(worker_id), None)
+    redis_queue = RedisPriorityQueue(backend_url)
+    while True:
+        status_redis.setp(str(worker_id), 'waiting...')
+        (request_json_body, client_ip, token, parameters), event_id, selected_model, timestamp, do_stream = redis_queue.get()
+        event = DataEvent(event_id)
+
+        try:
+            backend_info = cluster_config.get_backend(backend_url)
+        except:
+            # This is not a critical error because it usually means that the backend is
+            # offline and this backend is in a state of transition from online to offline.
+            logger.debug(f'got an exception while getting info for backend {backend_url} - ', traceback.format_exc())
+            event.set((False, None, 'exception'))
+            continue
+
+        if not backend_info['online']:
+            event.set((False, None, 'canceled'))
+            continue
+
+        if not selected_model:
+            selected_model = backend_info['model']
+
+        logger.debug(f"Starting using {backend_url} and {selected_model}. Online: {backend_info['online']}. Streaming: {do_stream}")
+
+        try:
+            stream_redis.delete(get_stream_name(worker_id))  # clean up any old streams
+            increment_ip_count(client_ip, 'processing_ips')
+            incr_active_workers(selected_model, backend_url)
+
+            if do_stream:
+                status_redis.setp(str(worker_id), ('streaming', client_ip))
+
+                # Return the name of the stream that the slave should connect to.
+                event.set((True, get_stream_name(worker_id), None))
+
+                msg_to_backend = {
+                    **parameters,
+                    'prompt': request_json_body['prompt'],
+                    'stream': True,
+                }
+                inference_do_stream(worker_id, msg_to_backend, backend_url, event_id)
+            else:
+                # Normal inference (not streaming).
+                status_redis.setp(str(worker_id), ('generating', client_ip))
+                success, response, error_msg = generator(request_json_body, backend_url)
+                event.set((success, response, error_msg))
+        except:
+            logger.error(traceback.format_exc())
+            event.set((False, None, 'exception'))
+        finally:
+            decrement_ip_count(client_ip, 'processing_ips')
+            decr_active_workers(selected_model, backend_url)
+            status_redis.setp(str(worker_id), None)
+
+
+def start_workers(cluster: dict):
+    logger = create_logger('inferencer')
+    i = 0
+    for item in cluster:
+        for _ in range(item['concurrent_gens']):
+            t = threading.Thread(target=worker, args=(item['backend_url'],))
+            t.daemon = True
+            t.start()
+            i += 1
+    logger.info(f'Started {i} inference workers.')

llm_server/workers/logger.py

@@ -0,0 +1,31 @@
+import pickle
+import traceback
+
+import redis
+
+from llm_server.database.database import do_db_log
+
+
+def db_logger():
+    """
+    We don't want the logging operation to be blocking, so we will use a background worker
+    to do the logging.
+    :return:
+    """
+
+    r = redis.Redis(host='localhost', port=6379, db=3)
+    p = r.pubsub()
+    p.subscribe('database-logger')
+
+    for message in p.listen():
+        try:
+            if message['type'] == 'message':
+                data = pickle.loads(message['data'])
+                function_name = data['function']
+                args = data['args']
+                kwargs = data['kwargs']
+
+                if function_name == 'log_prompt':
+                    do_db_log(*args, **kwargs)
+        except:
+            traceback.print_exc()

llm_server/workers/main.py

@@ -1,56 +0,0 @@
-import time
-from threading import Thread
-
-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.routes.cache 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 +1,57 @@
+import time
+
+import requests
+
+from llm_server import opts
+from llm_server.cluster.cluster_config import cluster_config, get_backends
+from llm_server.custom_redis import redis
+from llm_server.database.database import weighted_average_column_for_model
+from llm_server.llm.info import get_info
+from llm_server.routes.queue import RedisPriorityQueue, priority_queue
+
+
+def main_background_thread():
+    while True:
+        online, offline = get_backends()
+        for backend_url in online:
+            backend_info = cluster_config.get_backend(backend_url)
+            backend_mode = backend_info['mode']
+            backend_info = get_info(backend_url, backend_mode)
+            running_model = backend_info.get('model')
+            if not running_model:
+                continue
+
+            average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps = calc_stats_for_backend(backend_url, running_model, backend_mode)
+            if average_generation_elapsed_sec:  # returns None on exception
+                cluster_config.set_backend_value(backend_url, 'average_generation_elapsed_sec', average_generation_elapsed_sec)
+            if average_output_tokens:
+                cluster_config.set_backend_value(backend_url, 'average_output_tokens', average_output_tokens)
+            if average_generation_elapsed_sec and average_output_tokens:
+                cluster_config.set_backend_value(backend_url, 'estimated_avg_tps', estimated_avg_tps)
+
+        if opts.background_homepage_cacher:
+            try:
+                base_client_api = redis.get('base_client_api', dtype=str)
+                r = requests.get('https://' + base_client_api, timeout=5)
+            except Exception as e:
+                print(f'Failed fetch the homepage - {e.__class__.__name__}: {e}')
+
+        backends = priority_queue.get_backends()
+        for backend_url in backends:
+            queue = RedisPriorityQueue(backend_url)
+            queue.cleanup()
+
+        time.sleep(30)
+
+
+def calc_stats_for_backend(backend_url, running_model, backend_mode):
+    # exclude_zeros=True filters out rows where an error message was returned. Previously, if there was an error, 0
+    # was entered into the column. The new code enters null instead but we need to be backwards compatible for now.
+    average_generation_elapsed_sec = weighted_average_column_for_model('prompts', 'generation_time',
+                                                                       running_model, backend_mode, backend_url, exclude_zeros=True,
+                                                                       include_system_tokens=opts.include_system_tokens_in_stats) or 0
+    average_output_tokens = weighted_average_column_for_model('prompts', 'response_tokens',
+                                                              running_model, backend_mode, backend_url, exclude_zeros=True,
+                                                              include_system_tokens=opts.include_system_tokens_in_stats) or 0
+    estimated_avg_tps = round(average_output_tokens / average_generation_elapsed_sec, 2) if average_generation_elapsed_sec > 0 else 0  # Avoid division by zero
+    return average_generation_elapsed_sec, average_output_tokens, estimated_avg_tps

llm_server/workers/moderator.py

@@ -1,10 +1,13 @@
 import json
 import threading
+import time
 import traceback
 
 import redis as redis_redis
 
+from llm_server import opts
 from llm_server.llm.openai.moderation import check_moderation_endpoint
+from llm_server.logging import create_logger
 
 redis_moderation = redis_redis.Redis()
 
@@ -16,36 +19,43 @@ def start_moderation_workers(num_workers):
         t.daemon = True
         t.start()
         i += 1
-    print(f'Started {i} moderation workers.')
 
 
-def moderation_worker():
-    while True:
-        result = redis_moderation.blpop('queue:msgs_to_check')
-        try:
-            msg, tag = json.loads(result[1])
-            _, categories = check_moderation_endpoint(msg)
-            redis_moderation.rpush('queue:flagged_categories', json.dumps((tag, categories)))
-        except:
-            print(result)
-            traceback.print_exc()
-            continue
-
-
-def add_moderation_task(msg, tag):
-    redis_moderation.rpush('queue:msgs_to_check', json.dumps((msg, str(tag))))
-
+# TODO: don't use UUID tags to identify items. Use native redis
 
 def get_results(tag, num_tasks):
-    tag = str(tag)  # Required for comparison with Redis results.
+    tag = str(tag)  # Cast a UUID4 to a string.
     flagged_categories = set()
     num_results = 0
+    start_time = time.time()
     while num_results < num_tasks:
-        result = redis_moderation.blpop('queue:flagged_categories')
+        result = redis_moderation.blpop(['queue:flagged_categories'], timeout=opts.openai_moderation_timeout)
+        if result is None:
+            break  # Timeout occurred, break the loop.
         result_tag, categories = json.loads(result[1])
         if result_tag == tag:
             if categories:
                 for item in categories:
                     flagged_categories.add(item)
             num_results += 1
+        if time.time() - start_time > opts.openai_moderation_timeout:
+            logger.warning('Timed out waiting for result from moderator')
+            break
     return list(flagged_categories)
+
+
+def moderation_worker():
+    logger = create_logger('moderator')
+    while True:
+        result = redis_moderation.blpop(['queue:msgs_to_check'])
+        try:
+            msg, tag = json.loads(result[1])
+            _, categories = check_moderation_endpoint(msg)
+            redis_moderation.rpush('queue:flagged_categories', json.dumps((tag, categories)))
+        except:
+            logger.error(traceback.format_exc())
+            continue
+
+
+def add_moderation_task(msg, tag):
+    redis_moderation.rpush('queue:msgs_to_check', json.dumps((msg, str(tag))))

llm_server/workers/printer.py

@@ -1,25 +1,34 @@
-import logging
 import time
+import traceback
 
-from llm_server.routes.cache import redis
+from llm_server.cluster.backend import get_running_models
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.custom_redis import redis
+from llm_server.logging import create_logger
 from llm_server.routes.queue import priority_queue
 
-logger = logging.getLogger('console_printer')
-if not logger.handlers:
-    handler = logging.StreamHandler()
-    handler.setLevel(logging.INFO)
-    logger.setLevel(logging.INFO)
-    formatter = logging.Formatter("%(asctime)s: %(levelname)s:%(name)s - %(message)s")
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-
 
 def console_printer():
+    logger = create_logger('console_printer')
     time.sleep(3)
     while True:
-        processing = redis.hkeys('processing_ips')
-        processing_count = 0
-        for ip in processing:
-            processing_count += int(redis.hget('processing_ips', ip))
-        logger.info(f'REQUEST QUEUE -> Processing: {processing_count} | Queued: {len(priority_queue)}')
+        try:
+            processing = redis.keys('active_gen_workers:http*')  # backends always start with http
+            processing_count = 0
+            if len(processing):
+                for k in processing:
+                    processing_count += redis.get(k, default=0, dtype=int)
+            backends = [k for k, v in cluster_config.all().items() if v['online']]
+            activity = priority_queue.activity()
+
+            # Calculate the queue size the same way it's done on the stats.
+            queue_size = 0
+            running_models = get_running_models()
+            for model in running_models:
+                queue_size += priority_queue.len(model)
+
+            # Active Workers and Processing should read the same. If not, that's an issue.
+            logger.info(f'Active Workers: {len([i for i in activity if (i[1] and i[1] != "waiting...")])} | Processing: {processing_count} | Queued: {queue_size} | Backends Online: {len(backends)}')
+        except:
+            logger.error(traceback.format_exc())
         time.sleep(10)

llm_server/workers/recenter.py

@@ -1,6 +1,6 @@
 import time
 
-from llm_server.routes.cache import redis
+from llm_server.custom_redis import redis
 
 
 def recent_prompters_thread():

llm_server/workers/threader.py

@@ -0,0 +1,58 @@
+import time
+from threading import Thread
+
+from llm_server import opts
+from llm_server.cluster.worker import cluster_worker
+from llm_server.logging import create_logger
+from llm_server.routes.v1.generate_stats import generate_stats
+from llm_server.workers.inferencer import start_workers
+from llm_server.workers.logger import db_logger
+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(5)
+
+
+def start_background():
+    logger = create_logger('threader')
+    start_workers(opts.cluster)
+
+    t = Thread(target=main_background_thread)
+    t.daemon = True
+    t.start()
+    logger.info('Started the main background thread.')
+
+    num_moderators = opts.cluster_workers * 3
+    start_moderation_workers(num_moderators)
+    logger.info(f'Started {num_moderators} moderation workers.')
+
+    t = Thread(target=cache_stats)
+    t.daemon = True
+    t.start()
+    logger.info('Started the stats cacher.')
+
+    t = Thread(target=recent_prompters_thread)
+    t.daemon = True
+    t.start()
+    logger.info('Started the recent proompters thread.')
+
+    t = Thread(target=console_printer)
+    t.daemon = True
+    t.start()
+    logger.info('Started the console logger.infoer.')
+
+    t = Thread(target=cluster_worker)
+    t.daemon = True
+    t.start()
+    logger.info('Started the cluster worker.')
+
+    t = Thread(target=db_logger)
+    t.daemon = True
+    t.start()
+    logger.info('Started background logger.')

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/gradio/gradio_chat.py

@@ -0,0 +1,103 @@
+import os
+import sys
+import time
+import traceback
+import warnings
+from threading import Thread
+
+import gradio as gr
+import openai
+import requests
+
+warnings.filterwarnings("ignore")
+
+API_BASE = os.getenv('API_BASE')
+if not API_BASE:
+    print('Must set the secret variable API_BASE to your https://your-site/api')
+    sys.exit(1)
+API_BASE = API_BASE.strip('/')
+
+APP_TITLE = os.getenv('APP_TITLE')
+PRIMARY_MODEL_CHOICE = os.getenv('PRIMARY_MODEL_CHOICE')
+TRACKING_CODE = os.getenv('TRACKING_CODE')
+
+
+def background():
+    while True:
+        previous = openai.api_base
+        try:
+            r = requests.get(API_BASE + '/stats').json()
+            if PRIMARY_MODEL_CHOICE in r['models']['choices'].keys():
+                openai.api_base = API_BASE + '/openai/' + PRIMARY_MODEL_CHOICE + '/v1'
+            else:
+                openai.api_base = API_BASE + '/openai/v1'
+        except:
+            traceback.print_exc()
+            openai.api_base = API_BASE + '/openai/v1'
+        if openai.api_base != previous:
+            print('Set primary model to', openai.api_base)
+        time.sleep(10)
+
+
+if PRIMARY_MODEL_CHOICE:
+    t = Thread(target=background)
+    t.daemon = True
+    t.start()
+    print('Started the background thread.')
+
+# A system prompt can be injected into the very first spot in the context.
+# If the user sends a message that contains the CONTEXT_TRIGGER_PHRASE,
+# the content in CONTEXT_TRIGGER_INJECTION will be injected.
+# Setting CONTEXT_TRIGGER_PHRASE will also add it to the selectable examples.
+CONTEXT_TRIGGER_PHRASE = os.getenv('CONTEXT_TRIGGER_PHRASE')
+CONTEXT_TRIGGER_INJECTION = os.getenv('CONTEXT_TRIGGER_INJECTION')
+
+openai.api_key = 'null'
+openai.api_base = API_BASE + '/openai/v1'
+
+
+def stream_response(prompt, history):
+    messages = []
+    do_injection = False
+    for human, assistant in history:
+        messages.append({'role': 'user', 'content': str(human)})
+        messages.append({'role': 'assistant', 'content': str(assistant)})
+
+        if CONTEXT_TRIGGER_INJECTION and CONTEXT_TRIGGER_PHRASE in human:
+            do_injection = True
+    messages.append({'role': 'user', 'content': prompt})
+
+    if do_injection or (CONTEXT_TRIGGER_INJECTION and CONTEXT_TRIGGER_PHRASE in prompt):
+        messages.insert(0, {'role': 'system', 'content': CONTEXT_TRIGGER_INJECTION})
+
+    try:
+        response = openai.ChatCompletion.create(
+            model='0',
+            messages=messages,
+            temperature=0,
+            max_tokens=300,
+            stream=True,
+            headers={'LLM-Source': 'huggingface-demo'}
+        )
+    except Exception:
+        raise gr.Error("Failed to reach inference endpoint.")
+
+    message = ''
+    for chunk in response:
+        if len(chunk['choices'][0]['delta']) != 0:
+            message += chunk['choices'][0]['delta']['content']
+            yield message
+
+
+examples = ["hello"]
+if CONTEXT_TRIGGER_PHRASE:
+    examples.insert(0, CONTEXT_TRIGGER_PHRASE)
+
+with gr.Blocks(analytics_enabled=False) as demo:
+    gr.ChatInterface(stream_response, examples=examples, title=APP_TITLE, analytics_enabled=False, cache_examples=False, css='#component-0{height:100%!important}')
+
+    if TRACKING_CODE:
+        print('Inserting tracking code')
+        gr.HTML(TRACKING_CODE)
+
+demo.queue(concurrency_count=1, api_open=False).launch(show_api=False)

other/gradio/requirements.txt

@@ -0,0 +1,3 @@
+gradio
+openai
+requests

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
 

other/nginx-site.conf

@@ -0,0 +1,38 @@
+server
+{
+  listen 443 ssl http2 default_server;
+  server_name _;
+
+  proxy_set_header Host $host;
+  proxy_set_header Connection $http_connection;
+  proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+  proxy_set_header X-Scheme $scheme;
+
+  location ~* ^/api/(.*?|v1|openai)/(v1|(generate|stream)|(chat/completions|completions))$
+  {
+    # Route to inference endpoints
+    proxy_pass http://127.0.0.1:5000;
+
+    # Required for streaming (both websockets and SSE).
+    proxy_buffering off;
+    proxy_cache off;
+    proxy_http_version 1.1;
+    proxy_set_header Upgrade $http_upgrade;
+    proxy_set_header Connection "upgrade";
+
+    # Set long timeouts for inference operations.
+    # Cloudflare has a timeout of 100 seconds.
+    proxy_read_timeout 120;
+    proxy_connect_timeout 120;
+    proxy_send_timeout 120;
+  }
+
+  location /
+  {
+    proxy_pass http://127.0.0.1:5000;
+  }
+
+  ssl_certificate /etc/ssl/certs/nginx-selfsigned.crt;
+  ssl_certificate_key /etc/ssl/private/nginx-selfsigned.key;
+  include /etc/nginx/snippets/ssl-params.conf;
+}

other/tests/config.sh

@@ -0,0 +1,11 @@
+HOST="proxy.chub-archive.evulid.cc"
+
+AUTH_KEY="TEST_1df979f0-6df1-41bd-814a-e99b1680e727"
+
+PROXY_SERVERS=(
+  "http://172.0.4.7:3128"
+  "http://172.0.4.8:3128"
+  "http://172.0.4.10:3128"
+  "http://172.0.4.12:3128"
+  "http://172.0.4.13:3128"
+)

other/tests/generate.sh

@@ -0,0 +1,58 @@
+#!/bin/bash
+
+SLEEP_TIME=2
+
+while getopts p:t: flag; do
+  case "${flag}" in
+  p) PROXY_CHOICE=${OPTARG} ;;
+  t) SLEEP_TIME=${OPTARG} ;;
+  *) ;;
+  esac
+done
+
+SOURCE=${BASH_SOURCE[0]}
+while [ -L "$SOURCE" ]; do # resolve $SOURCE until the file is no longer a symlink
+  DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+  SOURCE=$(readlink "$SOURCE")
+  [[ $SOURCE != /* ]] && SOURCE=$DIR/$SOURCE # if $SOURCE was a relative symlink, we need to resolve it relative to the path where the symlink file was located
+done
+DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+
+source "$DIR/config.sh"
+
+if [ -n "$PROXY_CHOICE" ]; then
+  our_proxy_server="${PROXY_SERVERS[$PROXY_CHOICE]}"
+  echo "Using $our_proxy_server"
+else
+  our_proxy_server=""
+fi
+
+while true; do
+  echo "--> START <--"
+
+  DATA=$(
+    cat <<EOF
+{
+ "prompt": "Write a 300 word story about an apple tree.",
+ "temperature": 1,
+ "max_new_tokens": 100,
+ "top_p": 1.0,
+ "top_k": -1,
+ "use_beam_search": false,
+ "stop": ["TEST"],
+ "ignore_eos": false,
+ "presence_penalty": 0.0,
+ "frequency_penalty": 0.0,
+ "length_penalty": 1.0,
+ "early_stopping": false
+}
+EOF
+  )
+
+  curl "https://$HOST/api/v1/generate" -m 100 -x "$our_proxy_server" \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer $AUTH_KEY" \
+    -d "$DATA"
+  echo -e "--> DONE <--\n"
+  sleep $SLEEP_TIME
+done

other/tests/oai-chat-completion.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+DO_STREAM=false
+SLEEP_TIME=2
+
+while getopts p:t:s flag; do
+  case "${flag}" in
+  s) DO_STREAM=true ;;
+  p) PROXY_CHOICE=${OPTARG} ;;
+  t) SLEEP_TIME=${OPTARG} ;;
+  *) ;;
+  esac
+done
+
+SOURCE=${BASH_SOURCE[0]}
+while [ -L "$SOURCE" ]; do # resolve $SOURCE until the file is no longer a symlink
+  DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+  SOURCE=$(readlink "$SOURCE")
+  [[ $SOURCE != /* ]] && SOURCE=$DIR/$SOURCE # if $SOURCE was a relative symlink, we need to resolve it relative to the path where the symlink file was located
+done
+DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+
+source "$DIR/config.sh"
+
+if [ ! -z "$PROXY_CHOICE" ]; then
+  our_proxy_server="${PROXY_SERVERS[$PROXY_CHOICE]}"
+  echo "Using $our_proxy_server"
+else
+  our_proxy_server=""
+fi
+
+while true; do
+  echo "--> START <--"
+
+  DATA=$(
+    cat <<EOF
+{
+ "model": "gpt-4",
+ "messages": [{"role": "user", "content": "Write a 300 word story about an apple tree."}],
+ "max_tokens": 100,
+ "stream": $DO_STREAM
+}
+EOF
+  )
+
+  curl "https://$HOST/api/openai/v1/chat/completions" -m 100 -x "$our_proxy_server" \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer $AUTH_KEY" \
+    -d "$DATA"
+  echo -e "--> DONE <--\n"
+  sleep $SLEEP_TIME
+done

other/tests/oai-completion.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+DO_STREAM=false
+SLEEP_TIME=2
+
+while getopts p:t:s flag; do
+  case "${flag}" in
+  s) DO_STREAM=true ;;
+  p) PROXY_CHOICE=${OPTARG} ;;
+  t) SLEEP_TIME=${OPTARG} ;;
+  *) ;;
+  esac
+done
+
+SOURCE=${BASH_SOURCE[0]}
+while [ -L "$SOURCE" ]; do # resolve $SOURCE until the file is no longer a symlink
+  DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+  SOURCE=$(readlink "$SOURCE")
+  [[ $SOURCE != /* ]] && SOURCE=$DIR/$SOURCE # if $SOURCE was a relative symlink, we need to resolve it relative to the path where the symlink file was located
+done
+DIR=$(cd -P "$(dirname "$SOURCE")" >/dev/null 2>&1 && pwd)
+
+source "$DIR/config.sh"
+
+if [ ! -z "$PROXY_CHOICE" ]; then
+  our_proxy_server="${PROXY_SERVERS[$PROXY_CHOICE]}"
+  echo "Using $our_proxy_server"
+else
+  our_proxy_server=""
+fi
+
+while true; do
+  echo "--> START <--"
+
+  DATA=$(
+    cat <<EOF
+{
+ "model": "gpt-4",
+ "prompt": "Write a 300 word story about an apple tree.",
+ "max_tokens": 100,
+ "stream": $DO_STREAM
+}
+EOF
+  )
+
+  curl "https://$HOST/api/openai/v1/completions" -m 100 -x "$our_proxy_server" \
+    -H "Content-Type: application/json" \
+    -H "Authorization: Bearer $AUTH_KEY" \
+    -d "$DATA"
+  echo -e "--> DONE <--\n"
+  sleep $SLEEP_TIME
+done

other/tests/start-bulk.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Function to display help message
+function display_help {
+  echo "Usage: $0 -n num_windows -c command"
+  echo
+  echo "   -n, --number     Number of windows to create"
+  echo "   -c, --command    Command to run in each window"
+  echo
+  exit 1
+}
+
+# Parse command line arguments
+while getopts "n:c:h" opt; do
+  case ${opt} in
+  n)
+    num_windows=${OPTARG}
+    ;;
+  c)
+    command=${OPTARG}
+    ;;
+  h)
+    display_help
+    ;;
+  \?)
+    echo "Invalid option: -$OPTARG" 1>&2
+    display_help
+    ;;
+  :)
+    echo "Option -$OPTARG requires an argument." 1>&2
+    display_help
+    ;;
+  esac
+done
+
+# Check if number of windows and command are provided
+if [ -z "$num_windows" ] || [ -z "$command" ]; then
+  echo "Both number of windows and command are required."
+  display_help
+fi
+
+# Calculate rows and columns
+rows=$(echo "sqrt($num_windows)" | bc)
+columns=$(echo "($num_windows + $rows - 1) / $rows" | bc)
+
+# Create a new tmux session
+tmux new-session -d -s llm_tester "$command -p 0"
+
+# Create the remaining windows
+for ((i = 1; i < $num_windows; i++)); do
+  if ((i % $columns == 0)); then
+    tmux select-layout -t llm_tester:0 tiled
+    tmux select-pane -t 0
+    tmux split-window -t llm_tester:0 -v "$command -p $i"
+  else
+    tmux split-window -t llm_tester:0 -h "$command -p $i"
+  fi
+done
+
+# Balance the windows
+tmux select-layout -t llm_tester:0 tiled
+
+# Attach to the session
+tmux attach-session -t llm_tester

other/tests/stream.py

@@ -1,37 +1,50 @@
 import asyncio
 import json
 import sys
+import os
+import time
+from pathlib import Path
 
 try:
     import websockets
 except ImportError:
     print("Websockets package not found. Make sure it's installed.")
 
-# For local streaming, the websockets are hosted without ssl - ws://
-HOST = 'localhost:5000'
-URI = f'ws://{HOST}/api/v1/stream'
+script_path = os.path.dirname(os.path.realpath(__file__))
 
-# For reverse-proxied streaming, the remote will likely host with ssl - wss://
-# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+def parse_bash_config(file_path):
+    config = {}
+    with open(file_path, 'r') as f:
+        for line in f:
+            if line.startswith('#') or '=' not in line:
+                continue
+            key, value = line.strip().split('=', 1)
+            if value.startswith('"') and value.endswith('"'):
+                value = value[1:-1]
+            elif value.startswith('(') and value.endswith(')'):
+                value = value[1:-1].split()
+                value = [v.strip('"') for v in value]
+            config[key] = value
+    return config
+
+
+config = parse_bash_config(Path(script_path, 'config.sh'))
 
 
 async def run(context):
-    # Note: the selected defaults change from time to time.
     request = {
         'prompt': context,
         'max_new_tokens': 250,
         'auto_max_new_tokens': False,
         'max_tokens_second': 0,
-
-        # Generation params. If 'preset' is set to different than 'None', the values
-        # in presets/preset-name.yaml are used instead of the individual numbers.
         'preset': 'None',
         'do_sample': True,
         'temperature': 0.7,
         'top_p': 0.1,
         'typical_p': 1,
-        'epsilon_cutoff': 0,  # In units of 1e-4
-        'eta_cutoff': 0,  # In units of 1e-4
+        'epsilon_cutoff': 0,
+        'eta_cutoff': 0,
         'tfs': 1,
         'top_a': 0,
         'repetition_penalty': 1.18,
@@ -48,7 +61,6 @@ async def run(context):
         'mirostat_eta': 0.1,
         'guidance_scale': 1,
         'negative_prompt': '',
-
         'seed': -1,
         'add_bos_token': True,
         'truncation_length': 2048,
@@ -58,7 +70,7 @@ async def run(context):
         'stopping_strings': []
     }
 
-    async with websockets.connect(URI, ping_interval=None) as websocket:
+    async with websockets.connect(f'wss://{config["HOST"]}/api/v1/stream', ping_interval=None) as websocket:
         await websocket.send(json.dumps(request))
 
         yield context  # Remove this if you just want to see the reply
@@ -67,20 +79,28 @@ async def run(context):
             incoming_data = await websocket.recv()
             incoming_data = json.loads(incoming_data)
 
+            print(incoming_data)
+
             match incoming_data['event']:
-                case 'text_stream':
-                    yield incoming_data['text']
+                # case 'text_stream':
+                #     yield incoming_data['text']
                 case 'stream_end':
                     return
 
 
 async def print_response_stream(prompt):
-    async for response in run(prompt):
-        print(response, end='')
-        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
-    print('\n\nfinished')
+    try:
+        async for response in run(prompt):
+            print(response, end='')
+            sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+    except Exception as e:
+        print(e)
 
 
 if __name__ == '__main__':
-    prompt = "In order to make homemade bread, follow these steps:\n1)"
-    asyncio.run(print_response_stream(prompt))
+    prompt = "Write a 300 word story about an apple tree.\n\n"
+    while True:
+        print('--> START <--')
+        asyncio.run(print_response_stream(prompt))
+        print('--> DONE <--')
+        time.sleep(2)

requirements.txt

@@ -1,21 +1,18 @@
 flask~=2.3.3
-flask_cors
 pyyaml~=6.0.1
-flask_caching
+Flask-Caching==2.0.2
 requests~=2.31.0
 tiktoken~=0.5.0
-gunicorn
 gevent~=23.9.0.post1
-async-timeout
-flask-sock
-uvicorn~=0.23.2
-fastapi~=0.103.1
-torch~=2.0.1
 PyMySQL~=1.1.0
-DBUtils~=3.0.3
 simplejson~=3.19.1
 websockets~=11.0.3
 basicauth~=1.0.0
 openai~=0.28.0
-urllib3~=2.0.4
-celery[redis]
+flask-sock==0.6.0
+gunicorn==21.2.0
+redis==5.0.1
+ujson==5.8.0
+vllm==0.2.1.post1
+gradio~=3.46.1
+coloredlogs~=15.0.1

server.py

@@ -1,5 +1,3 @@
-from llm_server.config.config import mode_ui_names
-
 try:
     import gevent.monkey
 
@@ -7,37 +5,46 @@ try:
 except ImportError:
     pass
 
-from llm_server.pre_fork import server_startup
-from llm_server.config.load import load_config
 import os
 import sys
 from pathlib import Path
 
 import simplejson as json
-from flask import Flask, jsonify, render_template, request
+from flask import Flask, jsonify, render_template, request, Response
 
-import llm_server
+import config
+from llm_server import opts
+from llm_server.cluster.backend import get_model_choices
+from llm_server.cluster.cluster_config import cluster_config
+from llm_server.config.config import mode_ui_names
+from llm_server.config.load import load_config
+from llm_server.custom_redis import flask_cache, redis
 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.routes.openai import openai_bp
+from llm_server.helpers import auto_set_base_client_api
+from llm_server.llm.vllm.info import vllm_info
+from llm_server.pre_fork import server_startup
+from llm_server.routes.openai import openai_bp, openai_model_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.routes.v1.generate_stats import generate_stats
+from llm_server.sock import init_wssocket
 
-# 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
-# TODO: implement background thread to test backends via sending test prompts
-# TODO: if backend fails request, mark it as down
-# TODO: allow setting concurrent gens per-backend
-# TODO: set the max tokens to that of the lowest backend
-# TODO: implement RRD backend loadbalancer option
-# TODO: have VLLM reject a request if it already has n == concurrent_gens running
-# TODO: add a way to cancel VLLM gens. Maybe use websockets?
-# TODO: use coloredlogs
-# TODO: need to update opts. for workers
+# TODO: seperate queue item timeout for websockets (make longer, like 5 minutes)
+# TODO: return an `error: True`, error code, and error message rather than just a formatted message
+# TODO: what happens when all backends are offline? What about the "online" key in the stats page?
+# TODO: redis SCAN vs KEYS??
+# TODO: is frequency penalty the same as ooba repetition penalty???
+# TODO: make sure openai_moderation_enabled works on websockets, completions, and chat completions
 
 # Lower priority
+# TODO: if a backend is at its limit of concurrent requests, choose a different one
+# TODO: make error messages consitient
+# TODO: support logit_bias on OpenAI and Ooba endpoints.
+# TODO: add a way to cancel VLLM gens. Maybe use websockets?
+# TODO: validate openai_silent_trim works as expected and only when enabled
+# TODO: rewrite config storage. Store in redis so we can reload it.
+# TODO: set VLLM to stream ALL data using socket.io. If the socket disconnects, cancel generation.
 # TODO: estiamted wait time needs to account for full concurrent_gens but the queue is less than concurrent_gens
 # TODO: the estiamted wait time lags behind the stats
 # TODO: simulate OpenAI error messages regardless of endpoint
@@ -59,19 +66,16 @@ except ModuleNotFoundError as e:
     print('Please see README.md for install instructions.')
     sys.exit(1)
 
-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.routes.cache import RedisWrapper, 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
-
 app = Flask(__name__)
-init_socketio(app)
-app.register_blueprint(bp, url_prefix='/api/v1/')
+
+# Fixes ConcurrentObjectUseError
+# https://github.com/miguelgrinberg/simple-websocket/issues/24
+app.config['SOCK_SERVER_OPTIONS'] = {'ping_interval': 25}
+
+app.register_blueprint(bp, url_prefix='/api/')
 app.register_blueprint(openai_bp, url_prefix='/api/openai/v1/')
+app.register_blueprint(openai_model_bp, url_prefix='/api/openai/')
+init_wssocket(app)
 flask_cache.init_app(app)
 flask_cache.clear()
 
@@ -82,18 +86,13 @@ 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 = RedisWrapper('local_llm')
-create_db()
-
-
-# print(app.url_map)
 
 
 @app.route('/')
@@ -101,20 +100,30 @@ create_db()
 @app.route('/api/openai')
 @flask_cache.cached(timeout=10)
 def home():
+    base_client_api = redis.get('base_client_api', dtype=str)
     stats = generate_stats()
+    model_choices, default_model = get_model_choices()
 
-    if not stats['online']:
-        running_model = estimated_wait_sec = 'offline'
-    else:
-        running_model = redis.get('running_model', str, 'ERROR')
+    if default_model:
+        if not model_choices.get(default_model):
+            return 'The server is still starting up. Please wait...'
 
-        active_gen_workers = get_active_gen_workers()
-        if stats['queue']['queued'] == 0 and active_gen_workers >= opts.concurrent_gens:
+        default_model_info = model_choices[default_model]
+
+        if default_model_info['queued'] == 0 and default_model_info['queued'] >= default_model_info['concurrent_gens']:
             # There will be a wait if the queue is empty but prompts are processing, but we don't
             # know how long.
-            estimated_wait_sec = f"less than {stats['stats']['average_generation_elapsed_sec']} seconds"
+            default_estimated_wait_sec = f"less than {int(default_model_info['estimated_wait'])} seconds"
         else:
-            estimated_wait_sec = f"{stats['queue']['estimated_wait_sec']} seconds"
+            default_estimated_wait_sec = f"{int(default_model_info['estimated_wait'])} seconds"
+    else:
+        default_model_info = {
+            'model': 'OFFLINE',
+            'processing': '-',
+            'queued': '-',
+            'context_size': '-',
+        }
+        default_estimated_wait_sec = 'OFFLINE'
 
     if len(config['analytics_tracking_code']):
         analytics_tracking_code = f"<script>\n{config['analytics_tracking_code']}\n</script>"
@@ -127,32 +136,47 @@ def home():
         info_html = ''
 
     mode_info = ''
-    if opts.mode == 'vllm':
-        mode_info = vllm_info
-
-    base_client_api = redis.get('base_client_api', str)
+    for k, v in cluster_config.all().items():
+        if v['mode'] == 'vllm':
+            mode_info = vllm_info
+            break
 
     return render_template('home.html',
                            llm_middleware_name=opts.llm_middleware_name,
                            analytics_tracking_code=analytics_tracking_code,
                            info_html=info_html,
-                           current_model=opts.manual_model_name if opts.manual_model_name else running_model,
+                           default_model=default_model_info['model'],
+                           default_active_gen_workers=default_model_info['processing'],
+                           default_proompters_in_queue=default_model_info['queued'],
+                           current_model=opts.manual_model_name if opts.manual_model_name else None,  # else running_model,
                            client_api=f'https://{base_client_api}',
-                           ws_client_api=f'wss://{base_client_api}/v1/stream' if opts.enable_streaming else None,
-                           estimated_wait=estimated_wait_sec,
-                           mode_name=mode_ui_names[opts.mode][0],
-                           api_input_textbox=mode_ui_names[opts.mode][1],
-                           streaming_input_textbox=mode_ui_names[opts.mode][2],
-                           context_size=opts.context_size,
+                           ws_client_api=f'wss://{base_client_api}/v1/stream' if opts.enable_streaming else 'disabled',
+                           default_estimated_wait=default_estimated_wait_sec,
+                           mode_name=mode_ui_names[opts.frontend_api_mode][0],
+                           api_input_textbox=mode_ui_names[opts.frontend_api_mode][1],
+                           streaming_input_textbox=mode_ui_names[opts.frontend_api_mode][2],
+                           default_context_size=default_model_info['context_size'],
                            stats_json=json.dumps(stats, indent=4, ensure_ascii=False),
                            extra_info=mode_info,
                            openai_client_api=f'https://{base_client_api}/openai/v1' if opts.enable_openi_compatible_backend else 'disabled',
                            expose_openai_system_prompt=opts.expose_openai_system_prompt,
                            enable_streaming=opts.enable_streaming,
+                           model_choices=model_choices,
+                           proompters_5_min=stats['stats']['proompters']['5_min'],
+                           proompters_24_hrs=stats['stats']['proompters']['24_hrs'],
                            )
 
 
-# TODO: add authenticated route to get the current backend URL. Add it to /v1/backend
+@app.route('/robots.txt')
+def robots():
+    # TODO: have config value to deny all
+    # TODO: https://developers.google.com/search/docs/crawling-indexing/robots/create-robots-txt
+    t = """User-agent: *
+Allow: /"""
+    r = Response(t)
+    r.headers['Content-Type'] = 'text/plain'
+    return r
+
 
 @app.route('/<first>')
 @app.route('/<first>/<path:rest>')

templates/home.html

@@ -65,6 +65,19 @@
         .hidden {
             display: none;
         }
+
+        .header-workers {
+            font-weight: normal;
+            font-size: 14pt;
+        }
+
+        h3 {
+            font-size: 16pt;
+        }
+
+        .no-marker {
+            list-style: none;
+        }
     </style>
 </head>
 
@@ -76,8 +89,12 @@
     <h1 style="text-align: center;margin-top: 0;">{{ llm_middleware_name }}</h1>
 
     <div class="info-box">
-        <p><strong>Current Model:</strong> <span id="model">{{ current_model }}</span></p>
-        <p><strong>Estimated Wait Time:</strong> <span id="estimatedWait">{{ estimated_wait }}</span></p>
+        <p><strong>Current Model:</strong> <span id="model">{{ default_model }}</span></p>
+        <p>
+            <strong>Estimated Wait Time:</strong> <span id="estimatedWait">{{ default_estimated_wait }}</span><br>
+            Processing: {{ default_active_gen_workers }}<br>
+            Queued: {{ default_proompters_in_queue }}
+        </p>
         <br>
         <p><strong>Client API URL:</strong> {{ client_api }}</p>
         <p><strong>Streaming API URL:</strong> {{ ws_client_api if enable_streaming else 'Disabled' }}</p>
@@ -91,17 +108,20 @@
     <br>
 
     <div class="info-box">
-        <div id="oobabooga">
-            <strong>Instructions:</strong>
+        <h3>Instructions</h3>
+        <div id="instructions">
             <ol>
+                <li>In Settings > Power User Options, enable <kbd>Relaxed API URLS</kbd>.</li>
                 <li>Set your API type to <kbd>{{ mode_name }}</kbd></li>
                 <li>Enter <kbd>{{ client_api }}</kbd> in the <kbd>{{ api_input_textbox }}</kbd> textbox.</li>
-                {% if enable_streaming %}<li>Enter <kbd>{{ ws_client_api }}</kbd> in the <kbd>{{ streaming_input_textbox }}</kbd> textbox.</li>{% endif %}
+                {% if enable_streaming %}
+                <li>Enter <kbd>{{ ws_client_api }}</kbd> in the <kbd>{{ streaming_input_textbox }}</kbd> textbox.</li>
+                {% endif %}
                 <li>If you have a token, check the <kbd>Mancer AI</kbd> checkbox and enter your token in the <kbd>Mancer
                     API key</kbd> textbox.
                 </li>
                 <li>Click <kbd>Connect</kbd> to test the connection.</li>
-                <li>Open your preset config and set <kbd>Context Size</kbd> to {{ context_size }}.</li>
+                <li>Open your preset config and set <kbd>Context Size</kbd> to {{ default_context_size }}.</li>
                 <li>Follow this guide to get set up: <a href="https://rentry.org/freellamas" target="_blank">rentry.org/freellamas</a>
                 </li>
             </ol>
@@ -120,13 +140,45 @@
     <br>
 
     <div class="info-box">
-        <pre><code class="language-json" style="background-color: white">{{ stats_json|safe }}</code></pre>
+        <h3>Statistics</h3>
+        Proompters:
+        <ul style="margin-top: 5px;">
+            <li class="no-marker">5 minutes: {{ proompters_5_min }}</li>
+            <li class="no-marker">24 hours: {{ proompters_24_hrs }}</li>
+        </ul>
     </div>
+    <br>
+
+    {% for key, value in model_choices.items() %}
+    <div class="info-box">
+        <h3>{{ key }} <span class="header-workers">- {{ value.backend_count }} {% if value.backend_count == 1 %}worker{% else %}workers{% endif %}</span></h3>
+
+        {% if value.estimated_wait == 0 and value.estimated_wait >= value.concurrent_gens %}
+            {# There will be a wait if the queue is empty but prompts are processing, but we don't know how long. #}
+            {% set estimated_wait_sec = "less than " + value.estimated_wait|int|string + " seconds" %}
+        {% else %}
+            {% set estimated_wait_sec = value.estimated_wait|int|string + " seconds" %}
+        {% endif %}
+
+        <p>
+            <strong>Estimated Wait Time:</strong> {{ estimated_wait_sec }}<br>
+            Processing: {{ value.processing }}<br>
+            Queued: {{ value.queued }}<br>
+        </p>
+        <p>
+            <strong>Client API URL:</strong> {{ value.client_api }}<br>
+            <strong>Streaming API URL:</strong> {{ value.ws_client_api }}<br>
+            <strong>OpenAI-Compatible API URL:</strong> {{ value.openai_client_api }}
+        </p>
+        <p><strong>Context Size:</strong> {{ value.context_size }}</p>
+        <p><strong>Average Generation Time:</strong> {{ value.avg_generation_time | int }} seconds</p>
+    </div>
+    <br>
+    {% endfor %}
 </div>
 <div class="footer">
     <a href="https://git.evulid.cc/cyberes/local-llm-server" target="_blank">git.evulid.cc/cyberes/local-llm-server</a>
 </div>
-<script>hljs.highlightAll();</script>
 </body>
 
 </html>