fix(router.py): correctly raise no model available error

https://github.com/BerriAI/litellm/issues/1289

litellm/router.py

@@ -1705,7 +1705,6 @@ class Router:
             deployment = self.leastbusy_logger.get_available_deployments(
                 model_group=model, healthy_deployments=healthy_deployments
             )
-            return deployment
         elif self.routing_strategy == "simple-shuffle":
             # if users pass rpm or tpm, we do a random weighted pick - based on rpm/tpm
             ############## Check if we can do a RPM/TPM based weighted pick #################
@@ -1744,24 +1743,24 @@ class Router:
             self.routing_strategy == "latency-based-routing"
             and self.lowestlatency_logger is not None
         ):
-            min_deployment = self.lowestlatency_logger.get_available_deployments(
+            deployment = self.lowestlatency_logger.get_available_deployments(
                 model_group=model, healthy_deployments=healthy_deployments
             )
-            if min_deployment is None:
-                min_deployment = random.choice(healthy_deployments)
-            return min_deployment
         elif (
             self.routing_strategy == "usage-based-routing"
             and self.lowesttpm_logger is not None
         ):
-            min_deployment = self.lowesttpm_logger.get_available_deployments(
-                model_group=model, healthy_deployments=healthy_deployments
+            deployment = self.lowesttpm_logger.get_available_deployments(
+                model_group=model,
+                healthy_deployments=healthy_deployments,
+                messages=messages,
+                input=input,
             )
-            if min_deployment is None:
-                min_deployment = random.choice(healthy_deployments)
-            return min_deployment
 
-        raise ValueError("No models available.")
+        if deployment is None:
+            raise ValueError("No models available.")
+
+        return deployment
 
     def flush_cache(self):
         litellm.cache = None

litellm/router_strategy/lowest_tpm_rpm.py

@@ -2,11 +2,12 @@
 #   identifies lowest tpm deployment
 
 import dotenv, os, requests, random
-from typing import Optional
+from typing import Optional, Union, List, Dict
 from datetime import datetime
 
 dotenv.load_dotenv()  # Loading env variables using dotenv
 import traceback
+from litellm import token_counter
 from litellm.caching import DualCache
 from litellm.integrations.custom_logger import CustomLogger
 
@@ -118,7 +119,13 @@ class LowestTPMLoggingHandler(CustomLogger):
             traceback.print_exc()
             pass
 
-    def get_available_deployments(self, model_group: str, healthy_deployments: list):
+    def get_available_deployments(
+        self,
+        model_group: str,
+        healthy_deployments: list,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+    ):
         """
         Returns a deployment with the lowest TPM/RPM usage.
         """
@@ -145,6 +152,7 @@ class LowestTPMLoggingHandler(CustomLogger):
             if d["model_info"]["id"] not in all_deployments:
                 all_deployments[d["model_info"]["id"]] = 0
 
+        input_tokens = token_counter(messages=messages, text=input)
         for item, item_tpm in all_deployments.items():
             ## get the item from model list
             _deployment = None
@@ -173,12 +181,11 @@ class LowestTPMLoggingHandler(CustomLogger):
                 deployment = _deployment
                 break
             elif (
-                item_tpm > _deployment_tpm or rpm_dict[item] + 1 >= _deployment_rpm
+                item_tpm + input_tokens > _deployment_tpm
+                or rpm_dict[item] + 1 >= _deployment_rpm
             ):  # if user passed in tpm / rpm in the model_list
                 continue
             elif item_tpm < lowest_tpm:
                 lowest_tpm = item_tpm
                 deployment = _deployment
-        if deployment is None:
-            deployment = random.choice(healthy_deployments)
         return deployment

litellm/tests/test_tpm_rpm_routing.py

@@ -215,11 +215,64 @@ def test_router_get_available_deployments():
 
 
 # test_get_available_deployments()
-
-
 # test_router_get_available_deployments()
 
 
+def test_router_skip_rate_limited_deployments():
+    """
+    Test if routers 'get_available_deployments' raises No Models Available error if max tpm would be reached by message
+    """
+    model_list = [
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "azure/gpt-turbo",
+                "api_key": "os.environ/AZURE_FRANCE_API_KEY",
+                "api_base": "https://openai-france-1234.openai.azure.com",
+                "tpm": 1440,
+            },
+            "model_info": {"id": 1},
+        },
+    ]
+    router = Router(
+        model_list=model_list,
+        routing_strategy="usage-based-routing",
+        set_verbose=False,
+        num_retries=3,
+    )  # type: ignore
+
+    ## DEPLOYMENT 1 ##
+    deployment_id = 1
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "azure-model",
+            },
+            "model_info": {"id": deployment_id},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 1439}}
+    end_time = time.time()
+    router.lowesttpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+
+    ## CHECK WHAT'S SELECTED ## - should skip 2, and pick 1
+    # print(router.lowesttpm_logger.get_available_deployments(model_group="azure-model"))
+    try:
+        router.get_available_deployment(
+            model="azure-model",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+        )
+        pytest.fail(f"Should have raised No Models Available error")
+    except Exception as e:
+        pass
+
+
 @pytest.mark.asyncio
 async def test_router_completion_streaming():
     messages = [

litellm/utils.py

@@ -2504,7 +2504,11 @@ def openai_token_counter(
     return num_tokens
 
 
-def token_counter(model="", text=None, messages: Optional[List] = None):
+def token_counter(
+    model="",
+    text: Optional[Union[str, List[str]]] = None,
+    messages: Optional[List] = None,
+):
     """
     Count the number of tokens in a given text using a specified model.
 
@@ -2533,6 +2537,8 @@ def token_counter(model="", text=None, messages: Optional[List] = None):
                             text += function_arguments
         else:
             raise ValueError("text and messages cannot both be None")
+    elif isinstance(text, List):
+        text = "".join(t for t in text if isinstance(t, str))
     num_tokens = 0
     if model is not None:
         tokenizer_json = _select_tokenizer(model=model)
@@ -2545,13 +2551,13 @@ def token_counter(model="", text=None, messages: Optional[List] = None):
                 or model in litellm.azure_llms
             ):
                 num_tokens = openai_token_counter(
-                    text=text, model=model, messages=messages, is_tool_call=is_tool_call
+                    text=text, model=model, messages=messages, is_tool_call=is_tool_call  # type: ignore
                 )
             else:
                 enc = tokenizer_json["tokenizer"].encode(text)
                 num_tokens = len(enc)
     else:
-        num_tokens = len(encoding.encode(text))
+        num_tokens = len(encoding.encode(text))  # type: ignore
     return num_tokens