chore: remove deprecated inference.chat_completion implementations (#3654)

# What does this PR do?

remove unused chat_completion implementations

vllm features ported -
 - requires max_tokens be set, use config value
 - set tool_choice to none if no tools provided


## Test Plan

ci

llama_stack/core/routers/inference.py

@@ -27,7 +27,6 @@ from llama_stack.apis.inference import (
     CompletionResponseStreamChunk,
     Inference,
     ListOpenAIChatCompletionResponse,
-    LogProbConfig,
     Message,
     OpenAIAssistantMessageParam,
     OpenAIChatCompletion,
@@ -42,12 +41,7 @@ from llama_stack.apis.inference import (
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
     Order,
-    ResponseFormat,
-    SamplingParams,
     StopReason,
-    ToolChoice,
-    ToolConfig,
-    ToolDefinition,
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model, ModelType
@@ -185,88 +179,6 @@ class InferenceRouter(Inference):
             raise ModelTypeError(model_id, model.model_type, expected_model_type)
         return model
 
-    async def chat_completion(
-        self,
-        model_id: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = None,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
-        logger.debug(
-            f"InferenceRouter.chat_completion: {model_id=}, {stream=}, {messages=}, {tools=}, {tool_config=}, {response_format=}",
-        )
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self._get_model(model_id, ModelType.llm)
-        if tool_config:
-            if tool_choice and tool_choice != tool_config.tool_choice:
-                raise ValueError("tool_choice and tool_config.tool_choice must match")
-            if tool_prompt_format and tool_prompt_format != tool_config.tool_prompt_format:
-                raise ValueError("tool_prompt_format and tool_config.tool_prompt_format must match")
-        else:
-            params = {}
-            if tool_choice:
-                params["tool_choice"] = tool_choice
-            if tool_prompt_format:
-                params["tool_prompt_format"] = tool_prompt_format
-            tool_config = ToolConfig(**params)
-
-        tools = tools or []
-        if tool_config.tool_choice == ToolChoice.none:
-            tools = []
-        elif tool_config.tool_choice == ToolChoice.auto:
-            pass
-        elif tool_config.tool_choice == ToolChoice.required:
-            pass
-        else:
-            # verify tool_choice is one of the tools
-            tool_names = [t.tool_name if isinstance(t.tool_name, str) else t.tool_name.value for t in tools]
-            if tool_config.tool_choice not in tool_names:
-                raise ValueError(f"Tool choice {tool_config.tool_choice} is not one of the tools: {tool_names}")
-
-        params = dict(
-            model_id=model_id,
-            messages=messages,
-            sampling_params=sampling_params,
-            tools=tools,
-            tool_choice=tool_choice,
-            tool_prompt_format=tool_prompt_format,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-            tool_config=tool_config,
-        )
-        provider = await self.routing_table.get_provider_impl(model_id)
-        prompt_tokens = await self._count_tokens(messages, tool_config.tool_prompt_format)
-
-        if stream:
-            response_stream = await provider.chat_completion(**params)
-            return self.stream_tokens_and_compute_metrics(
-                response=response_stream,
-                prompt_tokens=prompt_tokens,
-                model=model,
-                tool_prompt_format=tool_config.tool_prompt_format,
-            )
-
-        response = await provider.chat_completion(**params)
-        metrics = await self.count_tokens_and_compute_metrics(
-            response=response,
-            prompt_tokens=prompt_tokens,
-            model=model,
-            tool_prompt_format=tool_config.tool_prompt_format,
-        )
-        # these metrics will show up in the client response.
-        response.metrics = (
-            metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics
-        )
-        return response
-
     async def openai_completion(
         self,
         model: str,