feat(router.py): support fastest response batch completion call

returns fastest response. cancels others.

litellm/router.py

@@ -356,7 +356,8 @@ class Router:
                 raise ValueError(f"Item '{fallback_dict}' is not a dictionary.")
             if len(fallback_dict) != 1:
                 raise ValueError(
-                    f"Dictionary '{fallback_dict}' must have exactly one key, but has {len(fallback_dict)} keys.")
+                    f"Dictionary '{fallback_dict}' must have exactly one key, but has {len(fallback_dict)} keys."
+                )
 
     def routing_strategy_init(self, routing_strategy: str, routing_strategy_args: dict):
         if routing_strategy == "least-busy":
@@ -737,6 +738,76 @@ class Router:
         response = await asyncio.gather(*_tasks)
         return response
 
+    # fmt: off
+
+    @overload
+    async def abatch_completion_fastest_response(
+        self, models: List[str], messages: List[Dict[str, str]], stream: Literal[True], **kwargs
+    ) -> CustomStreamWrapper:
+        ...
+
+
+
+    @overload
+    async def abatch_completion_fastest_response(
+        self, models: List[str], messages: List[Dict[str, str]], stream: Literal[False] = False, **kwargs
+    ) -> ModelResponse:
+        ...
+
+    # fmt: on
+
+    async def abatch_completion_fastest_response(
+        self,
+        models: List[str],
+        messages: List[Dict[str, str]],
+        stream: bool = False,
+        **kwargs,
+    ):
+        """Send 1 completion call to many models: Return Fastest Response."""
+
+        async def _async_completion_no_exceptions(
+            model: str, messages: List[Dict[str, str]], **kwargs
+        ):
+            """
+            Wrapper around self.async_completion that catches exceptions and returns them as a result
+            """
+            try:
+                return await self.acompletion(model=model, messages=messages, **kwargs)
+            except Exception as e:
+                return e
+
+        _tasks = []
+        pending_tasks = []  # type: ignore
+
+        async def check_response(task):
+            nonlocal pending_tasks
+            result = await task
+            if isinstance(result, (ModelResponse, CustomStreamWrapper)):
+                # If a desired response is received, cancel all other pending tasks
+                for t in pending_tasks:
+                    t.cancel()
+                return result
+            else:
+                try:
+                    pending_tasks.remove(task)
+                except Exception as e:
+                    pass
+
+        for model in models:
+            task = asyncio.create_task(
+                _async_completion_no_exceptions(
+                    model=model, messages=messages, **kwargs
+                )
+            )
+            task.add_done_callback(check_response)
+            _tasks.append(task)
+            pending_tasks.append(task)
+
+        responses = await asyncio.gather(*_tasks, return_exceptions=True)
+        if isinstance(responses[0], Exception):
+            raise responses[0]
+        return responses[0]  # return first value from list
+
     def image_generation(self, prompt: str, model: str, **kwargs):
         try:
             kwargs["model"] = model

litellm/tests/test_router_batch_completion.py

@@ -19,8 +19,9 @@ import os, httpx
 load_dotenv()
 
 
+@pytest.mark.parametrize("mode", ["all_responses", "fastest_response"])
 @pytest.mark.asyncio
-async def test_batch_completion_multiple_models():
+async def test_batch_completion_multiple_models(mode):
     litellm.set_verbose = True
 
     router = litellm.Router(
@@ -40,21 +41,34 @@ async def test_batch_completion_multiple_models():
         ]
     )
 
-    response = await router.abatch_completion(
-        models=["gpt-3.5-turbo", "groq-llama"],
-        messages=[
-            {"role": "user", "content": "is litellm becoming a better product ?"}
-        ],
-        max_tokens=15,
-    )
+    if mode == "all_responses":
+        response = await router.abatch_completion(
+            models=["gpt-3.5-turbo", "groq-llama"],
+            messages=[
+                {"role": "user", "content": "is litellm becoming a better product ?"}
+            ],
+            max_tokens=15,
+        )
 
-    print(response)
-    assert len(response) == 2
+        print(response)
+        assert len(response) == 2
 
-    models_in_responses = []
-    for individual_response in response:
-        _model = individual_response["model"]
-        models_in_responses.append(_model)
+        models_in_responses = []
+        for individual_response in response:
+            _model = individual_response["model"]
+            models_in_responses.append(_model)
 
-    # assert both models are different
-    assert models_in_responses[0] != models_in_responses[1]
+        # assert both models are different
+        assert models_in_responses[0] != models_in_responses[1]
+    elif mode == "fastest_response":
+        from openai.types.chat.chat_completion import ChatCompletion
+
+        response = await router.abatch_completion_fastest_response(
+            models=["gpt-3.5-turbo", "groq-llama"],
+            messages=[
+                {"role": "user", "content": "is litellm becoming a better product ?"}
+            ],
+            max_tokens=15,
+        )
+
+        ChatCompletion.model_validate(response.model_dump(), strict=True)