fix(router.py): fix default retry logic

.gitignore

@@ -51,3 +51,4 @@ loadtest_kub.yaml
 litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_new_secret_config.yaml
 litellm/proxy/_super_secret_config.yaml
+litellm/proxy/_super_secret_config.yaml

litellm/llms/openai.py

@@ -447,6 +447,7 @@ class OpenAIChatCompletion(BaseLLM):
                 )
             else:
                 openai_aclient = client
+
             ## LOGGING
             logging_obj.pre_call(
                 input=data["messages"],

litellm/proxy/_super_secret_config.yaml

@@ -1,51 +1,8 @@
-environment_variables:
-  SLACK_WEBHOOK_URL: SQD2/FQHvDuj6Q9/Umyqi+EKLNKKLRCXETX2ncO0xCIQp6EHCKiYD7jPW0+1QdrsQ+pnEzhsfVY2r21SiQV901n/9iyJ2tSnEyWViP7FKQVtTvwutsAqSqbiVHxLHbpjPCu03fhS/idjZrtK7dJLbLBB3RgudjNjHg==
-general_settings:
-  alerting:
-  - slack
-  alerting_threshold: 300
-  database_connection_pool_limit: 100
-  database_connection_timeout: 60
-  health_check_interval: 300
-  proxy_batch_write_at: 10
-  ui_access_mode: all
-litellm_settings:
-  allowed_fails: 3
-  failure_callback:
-  - prometheus
-  fallbacks:
-  - gpt-3.5-turbo:
-    - fake-openai-endpoint
-    - gpt-4
-  num_retries: 3
-  service_callback:
-  - prometheus_system
-  success_callback:
-  - prometheus
 model_list:
 - litellm_params:
-    api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
+    api_base: http://0.0.0.0:8080
     api_key: my-fake-key
     model: openai/my-fake-model
   model_name: fake-openai-endpoint
-- litellm_params:
-    model: gpt-3.5-turbo
-  model_name: gpt-3.5-turbo
-- model_name: llama-3
-  litellm_params:
-    model: replicate/meta/meta-llama-3-8b-instruct
 router_settings:
-  allowed_fails: 3
+  num_retries: 0
-  context_window_fallbacks: null
-  cooldown_time: 1
-  fallbacks:
-  - gpt-3.5-turbo:
-    - fake-openai-endpoint
-    - gpt-4
-  - gpt-3.5-turbo-3:
-    - fake-openai-endpoint
-  num_retries: 3
-  retry_after: 0
-  routing_strategy: simple-shuffle
-  routing_strategy_args: {}
-  timeout: 6000

litellm/router.py

@@ -50,7 +50,7 @@ class Router:
     model_names: List = []
     cache_responses: Optional[bool] = False
     default_cache_time_seconds: int = 1 * 60 * 60  # 1 hour
-    num_retries: int = 0
+    num_retries: int = openai.DEFAULT_MAX_RETRIES
     tenacity = None
     leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
     lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
@@ -70,7 +70,7 @@ class Router:
         ] = None,  # if you want to cache across model groups
         client_ttl: int = 3600,  # ttl for cached clients - will re-initialize after this time in seconds
         ## RELIABILITY ##
-        num_retries: int = 0,
+        num_retries: Optional[int] = None,
         timeout: Optional[float] = None,
         default_litellm_params={},  # default params for Router.chat.completion.create
         default_max_parallel_requests: Optional[int] = None,
@@ -229,7 +229,12 @@ class Router:
         self.failed_calls = (
             InMemoryCache()
         )  # cache to track failed call per deployment, if num failed calls within 1 minute > allowed fails, then add it to cooldown
-        self.num_retries = num_retries or litellm.num_retries or 0
+
+        if num_retries is not None:
+            self.num_retries = num_retries
+        elif litellm.num_retries is not None:
+            self.num_retries = litellm.num_retries
+
         self.timeout = timeout or litellm.request_timeout
 
         self.retry_after = retry_after
@@ -428,6 +433,7 @@ class Router:
             kwargs["messages"] = messages
             kwargs["original_function"] = self._acompletion
             kwargs["num_retries"] = kwargs.get("num_retries", self.num_retries)
+
             timeout = kwargs.get("request_timeout", self.timeout)
             kwargs.setdefault("metadata", {}).update({"model_group": model})
 
@@ -1415,10 +1421,12 @@ class Router:
         context_window_fallbacks = kwargs.pop(
             "context_window_fallbacks", self.context_window_fallbacks
         )
-        verbose_router_logger.debug(
+
-            f"async function w/ retries: original_function - {original_function}"
-        )
         num_retries = kwargs.pop("num_retries")
+
+        verbose_router_logger.debug(
+            f"async function w/ retries: original_function - {original_function}, num_retries - {num_retries}"
+        )
         try:
             # if the function call is successful, no exception will be raised and we'll break out of the loop
             response = await original_function(*args, **kwargs)
@@ -1986,7 +1994,9 @@ class Router:
                 stream_timeout = litellm.get_secret(stream_timeout_env_name)
                 litellm_params["stream_timeout"] = stream_timeout
 
-            max_retries = litellm_params.pop("max_retries", 2)
+            max_retries = litellm_params.pop(
+                "max_retries", 0
+            )  # router handles retry logic
             if isinstance(max_retries, str) and max_retries.startswith("os.environ/"):
                 max_retries_env_name = max_retries.replace("os.environ/", "")
                 max_retries = litellm.get_secret(max_retries_env_name)

litellm/tests/test_router.py

@@ -1,7 +1,7 @@
 #### What this tests ####
 # This tests litellm router
 
-import sys, os, time
+import sys, os, time, openai
 import traceback, asyncio
 import pytest
 
@@ -18,6 +18,45 @@ from dotenv import load_dotenv
 load_dotenv()
 
 
+@pytest.mark.parametrize("num_retries", [None, 2])
+@pytest.mark.parametrize("max_retries", [None, 4])
+def test_router_num_retries_init(num_retries, max_retries):
+    """
+    - test when num_retries set v/s not
+    - test client value when max retries set v/s not
+    """
+    router = Router(
+        model_list=[
+            {
+                "model_name": "gpt-3.5-turbo",  # openai model name
+                "litellm_params": {  # params for litellm completion/embedding call
+                    "model": "azure/chatgpt-v-2",
+                    "api_key": "bad-key",
+                    "api_version": os.getenv("AZURE_API_VERSION"),
+                    "api_base": os.getenv("AZURE_API_BASE"),
+                    "max_retries": max_retries,
+                },
+                "model_info": {"id": 12345},
+            },
+        ],
+        num_retries=num_retries,
+    )
+
+    if num_retries is not None:
+        assert router.num_retries == num_retries
+    else:
+        assert router.num_retries == openai.DEFAULT_MAX_RETRIES
+
+    model_client = router._get_client(
+        {"model_info": {"id": 12345}}, client_type="async", kwargs={}
+    )
+
+    if max_retries is not None:
+        assert getattr(model_client, "max_retries") == max_retries
+    else:
+        assert getattr(model_client, "max_retries") == 0
+
+
 def test_exception_raising():
     # this tests if the router raises an exception when invalid params are set
     # in this test both deployments have bad keys - Keep this test. It validates if the router raises the most recent exception

litellm/types/router.py

@@ -108,7 +108,7 @@ class LiteLLM_Params(BaseModel):
     stream_timeout: Optional[Union[float, str]] = (
         None  # timeout when making stream=True calls, if str, pass in as os.environ/
     )
-    max_retries: int = 2  # follows openai default of 2
+    max_retries: Optional[int] = None
     organization: Optional[str] = None  # for openai orgs
     ## VERTEX AI ##
     vertex_project: Optional[str] = None
@@ -146,9 +146,7 @@ class LiteLLM_Params(BaseModel):
         args.pop("self", None)
         args.pop("params", None)
         args.pop("__class__", None)
-        if max_retries is None:
+        if max_retries is not None and isinstance(max_retries, str):
-            max_retries = 2
-        elif isinstance(max_retries, str):
             max_retries = int(max_retries)  # cast to int
         super().__init__(max_retries=max_retries, **args, **params)