llm_server/cluster/backend.py

@@ -1,10 +1,33 @@
+import numpy as np
+
 from llm_server import opts
-from llm_server.cluster.cluster_config import cluster_config
-from llm_server.cluster.redis_cycle import add_backend_cycler, redis_cycle
+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.routes.helpers.model import estimate_model_size
+from llm_server.routes.queue import priority_queue
+from llm_server.routes.stats import get_active_gen_workers_model, calculate_wait_time
+
+
+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):
@@ -28,81 +51,64 @@ def test_backend(backend_url: str, test_prompt: bool = False):
     return True, i
 
 
-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}
 
-    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
-        )
+
+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:
-        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]
+            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}' 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
+        }
 
-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 len(context_size):
+            model_choices[model]['context_size'] = min(context_size)
 
-        # 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]
+    # 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, None
+    default_model = default_backend_info['model']
 
-def get_backends_from_model(model_name: str):
-    return [x.decode('utf-8') for x in redis_running_models.smembers(model_name)]
-
-
-# def verify_context_size(model_name:str):
-#     b = get_backends_from_model(model_name)
-#     for backend_url in b:
-#         backend_info = cluster_config.get_backend(backend_url)
-#         backend_info.get()
-
-
-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)
+    redis.setp('model_choices', (model_choices, default_model))
+    return model_choices, default_model

llm_server/cluster/cluster_config.py

@@ -1,3 +1,60 @@
+from llm_server import opts
 from llm_server.cluster.redis_config_cache import RedisClusterStore
+from llm_server.cluster.redis_cycle import add_backend_cycler, redis_cycle
+from llm_server.cluster.stores import redis_running_models
+from llm_server.routes.helpers.model import estimate_model_size
 
 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}
+
+    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]
+
+
+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/model_choices.py

@@ -1,70 +1 @@
-import numpy as np
-
-from llm_server import opts
-from llm_server.cluster.backend import get_a_cluster_backend, get_backends_from_model, get_running_models
-from llm_server.cluster.cluster_config import cluster_config
-from llm_server.custom_redis import redis
-from llm_server.routes.queue import priority_queue
-from llm_server.routes.stats import calculate_wait_time, get_active_gen_workers_model
-
-
 # TODO: give this a better name!
-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}' 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, None
-    default_model = default_backend_info['model']
-
-    redis.setp('model_choices', (model_choices, default_model))
-    return model_choices, default_model

llm_server/cluster/redis_config_cache.py

@@ -45,5 +45,16 @@ class RedisClusterStore:
         else:
             return {}
 
-    # def get(self, name: str):
-    #     return self.all().get(name)
+    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
+

llm_server/cluster/worker.py

@@ -1,8 +1,8 @@
 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.backend import test_backend
 from llm_server.cluster.stores import redis_running_models
 
 

llm_server/llm/vllm/tokenize.py

@@ -4,7 +4,6 @@ import requests
 import tiktoken
 
 from llm_server import opts
-from llm_server.cluster.backend import get_a_cluster_backend
 from llm_server.cluster.cluster_config import cluster_config
 
 
@@ -13,15 +12,9 @@ def tokenize(prompt: str, backend_url: str) -> int:
     assert isinstance(prompt, str)
     assert isinstance(backend_url, str)
 
-    # TODO: put this in a shared function
     # The backend could have died between when the request was
     # submitted and now, so let's double check it's still online.
-    backend_info = cluster_config.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}')
-        del old  # gc
+    backend_url = cluster_config.validate_backend(backend_url)
 
     if not prompt:
         # The tokenizers have issues when the prompt is None.

llm_server/routes/openai/models.py

@@ -7,8 +7,7 @@ from llm_server.custom_redis import ONE_MONTH_SECONDS, flask_cache, redis
 from . import openai_bp
 from ..stats import server_start_time
 from ... import opts
-from ...cluster.backend import get_a_cluster_backend
-from ...cluster.cluster_config import cluster_config
+from ...cluster.cluster_config import cluster_config, get_a_cluster_backend
 from ...helpers import jsonify_pretty
 from ...llm.openai.transform import generate_oai_string
 

llm_server/routes/openai_request_handler.py

@@ -9,7 +9,7 @@ import flask
 from flask import Response, jsonify, make_response
 
 from llm_server import opts
-from llm_server.cluster.model_choices import get_model_choices
+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, log_prompt
 from llm_server.llm import get_token_count

llm_server/routes/request_handler.py

@@ -5,8 +5,7 @@ import flask
 from flask import Response, request
 
 from llm_server import opts
-from llm_server.cluster.backend import get_a_cluster_backend
-from llm_server.cluster.cluster_config import cluster_config
+from llm_server.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, log_prompt
 from llm_server.helpers import auto_set_base_client_api

llm_server/routes/v1/generate_stats.py

@@ -3,7 +3,7 @@ from datetime import datetime
 
 from llm_server import opts
 from llm_server.cluster.cluster_config import cluster_config
-from llm_server.cluster.model_choices import get_model_choices
+from llm_server.cluster.backend import get_model_choices
 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

llm_server/routes/v1/info.py

@@ -5,8 +5,8 @@ from flask import jsonify, request
 from llm_server.custom_redis import flask_cache
 from . import bp
 from ... import opts
-from ...cluster.backend import get_a_cluster_backend, get_backends_from_model, is_valid_model
-from ...cluster.cluster_config import cluster_config
+from ...cluster.backend import get_backends_from_model, is_valid_model
+from ...cluster.cluster_config import cluster_config, get_a_cluster_backend
 
 
 @bp.route('/v1/model', methods=['GET'])

llm_server/routes/v1/proxy.py

@@ -4,8 +4,7 @@ from llm_server.custom_redis import flask_cache
 from . import bp
 from .generate_stats import generate_stats
 from ..auth import requires_auth
-from ...cluster.backend import get_backends
-from ...cluster.cluster_config import cluster_config
+from ...cluster.cluster_config import cluster_config, get_backends
 from ...helpers import jsonify_pretty
 
 

llm_server/workers/inferencer.py

@@ -1,8 +1,7 @@
 import threading
 import time
 
-from llm_server.cluster.backend import get_a_cluster_backend
-from llm_server.cluster.cluster_config import cluster_config
+from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
 from llm_server.custom_redis import redis
 from llm_server.llm.generator import generator
 from llm_server.routes.queue import DataEvent, decr_active_workers, decrement_ip_count, incr_active_workers, increment_ip_count, priority_queue

llm_server/workers/mainer.py

@@ -3,8 +3,7 @@ import time
 import requests
 
 from llm_server import opts
-from llm_server.cluster.backend import get_backends
-from llm_server.cluster.cluster_config import cluster_config
+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

server.py

@@ -13,7 +13,7 @@ import simplejson as json
 from flask import Flask, jsonify, render_template, request
 
 from llm_server.cluster.cluster_config import cluster_config
-from llm_server.cluster.model_choices import get_model_choices
+from llm_server.cluster.backend import get_model_choices
 from llm_server.config.config import mode_ui_names
 from llm_server.config.load import load_config
 from llm_server.database.conn import database