fix(router.py): initial commit for semaphores on router

litellm/proxy/_new_secret_config.yaml

@@ -5,7 +5,7 @@ model_list:
     api_key: my-fake-key
     api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
     stream_timeout: 0.001
-    rpm: 10
+    rpm: 1
 - litellm_params:
       model: azure/chatgpt-v-2
       api_base: os.environ/AZURE_API_BASE

litellm/proxy/proxy_server.py

@@ -1836,6 +1836,9 @@ async def _run_background_health_check():
         await asyncio.sleep(health_check_interval)
 
 
+semaphore = asyncio.Semaphore(1)
+
+
 class ProxyConfig:
     """
     Abstraction class on top of config loading/updating logic. Gives us one place to control all config updating logic.
@@ -2425,8 +2428,7 @@ class ProxyConfig:
             for k, v in router_settings.items():
                 if k in available_args:
                     router_params[k] = v
-
-        router = litellm.Router(**router_params)  # type:ignore
+        router = litellm.Router(**router_params, semaphore=semaphore)  # type:ignore
         return router, model_list, general_settings
 
     async def add_deployment(
@@ -3421,6 +3423,7 @@ async def chat_completion(
 ):
     global general_settings, user_debug, proxy_logging_obj, llm_model_list
     try:
+        # async with llm_router.sem
         data = {}
         body = await request.body()
         body_str = body.decode()
@@ -3525,7 +3528,9 @@ async def chat_completion(
         tasks = []
         tasks.append(
             proxy_logging_obj.during_call_hook(
-                data=data, user_api_key_dict=user_api_key_dict, call_type="completion"
+                data=data,
+                user_api_key_dict=user_api_key_dict,
+                call_type="completion",
             )
         )
 

litellm/router.py

@@ -78,6 +78,7 @@ class Router:
             "latency-based-routing",
         ] = "simple-shuffle",
         routing_strategy_args: dict = {},  # just for latency-based routing
+        semaphore: Optional[asyncio.Semaphore] = None,
     ) -> None:
         """
         Initialize the Router class with the given parameters for caching, reliability, and routing strategy.
@@ -143,6 +144,8 @@ class Router:
         router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}])
         ```
         """
+        if semaphore:
+            self.semaphore = semaphore
         self.set_verbose = set_verbose
         self.debug_level = debug_level
         self.enable_pre_call_checks = enable_pre_call_checks
@@ -409,11 +412,18 @@ class Router:
             raise e
 
     async def _acompletion(self, model: str, messages: List[Dict[str, str]], **kwargs):
+        """
+        - Get an available deployment
+        - call it with a semaphore over the call
+        - semaphore specific to it's rpm
+        - in the semaphore,  make a check against it's local rpm before running
+        """
         model_name = None
         try:
             verbose_router_logger.debug(
                 f"Inside _acompletion()- model: {model}; kwargs: {kwargs}"
             )
+
             deployment = await self.async_get_available_deployment(
                 model=model,
                 messages=messages,
@@ -443,6 +453,7 @@ class Router:
             potential_model_client = self._get_client(
                 deployment=deployment, kwargs=kwargs, client_type="async"
             )
+
             # check if provided keys == client keys #
             dynamic_api_key = kwargs.get("api_key", None)
             if (
@@ -465,7 +476,7 @@ class Router:
                 )  # this uses default_litellm_params when nothing is set
             )
 
-            response = await litellm.acompletion(
+            _response = litellm.acompletion(
                 **{
                     **data,
                     "messages": messages,
@@ -475,6 +486,30 @@ class Router:
                     **kwargs,
                 }
             )
+
+            rpm_semaphore = self.semaphore
+
+            if rpm_semaphore is not None and isinstance(
+                rpm_semaphore, asyncio.Semaphore
+            ):
+                async with rpm_semaphore:
+                    """
+                    - Check against in-memory tpm/rpm limits before making the call
+                    """
+                    dt = get_utc_datetime()
+                    current_minute = dt.strftime("%H-%M")
+                    id = kwargs["model_info"]["id"]
+                    rpm_key = "{}:rpm:{}".format(id, current_minute)
+                    curr_rpm = await self.cache.async_get_cache(key=rpm_key)
+                    if (
+                        curr_rpm is not None and curr_rpm >= data["rpm"]
+                    ):  # >= b/c the initial count is 0
+                        raise Exception("Rate Limit error")
+                    await self.cache.async_increment_cache(key=rpm_key, value=1)
+                    response = await _response
+            else:
+                response = await _response
+
             self.success_calls[model_name] += 1
             verbose_router_logger.info(
                 f"litellm.acompletion(model={model_name})\033[32m 200 OK\033[0m"
@@ -1680,12 +1715,27 @@ class Router:
 
     def set_client(self, model: dict):
         """
-        Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278
+        - Initializes Azure/OpenAI clients. Stores them in cache, b/c of this - https://github.com/BerriAI/litellm/issues/1278
+        - Initializes Semaphore for client w/ rpm. Stores them in cache. b/c of this - https://github.com/BerriAI/litellm/issues/2994
         """
         client_ttl = self.client_ttl
         litellm_params = model.get("litellm_params", {})
         model_name = litellm_params.get("model")
         model_id = model["model_info"]["id"]
+        # ### IF RPM SET - initialize a semaphore ###
+        # rpm = litellm_params.get("rpm", None)
+        # print(f"rpm: {rpm}")
+        # if rpm:
+        #     semaphore = asyncio.Semaphore(rpm)
+        #     cache_key = f"{model_id}_rpm_client"
+        #     self.cache.set_cache(
+        #         key=cache_key,
+        #         value=semaphore,
+        #         local_only=True,
+        #     )
+
+        #     print("STORES SEMAPHORE IN CACHE")
+
         ####  for OpenAI / Azure we need to initalize the Client for High Traffic ########
         custom_llm_provider = litellm_params.get("custom_llm_provider")
         custom_llm_provider = custom_llm_provider or model_name.split("/", 1)[0] or ""
@@ -2275,7 +2325,11 @@ class Router:
             The appropriate client based on the given client_type and kwargs.
         """
         model_id = deployment["model_info"]["id"]
-        if client_type == "async":
+        if client_type == "rpm_client":
+            cache_key = "{}_rpm_client".format(model_id)
+            client = self.cache.get_cache(key=cache_key, local_only=True)
+            return client
+        elif client_type == "async":
             if kwargs.get("stream") == True:
                 cache_key = f"{model_id}_stream_async_client"
                 client = self.cache.get_cache(key=cache_key, local_only=True)