fix: runpod adapter

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -3,125 +3,131 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from collections.abc import AsyncGenerator
 
-from openai import OpenAI
+from typing import Any
 
-from llama_stack.apis.inference import *  # noqa: F403
-from llama_stack.apis.inference import OpenAIEmbeddingsResponse
-
-# from llama_stack.providers.datatypes import ModelsProtocolPrivate
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, build_hf_repo_model_entry
-from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
-    get_sampling_options,
-    process_chat_completion_response,
-    process_chat_completion_stream_response,
-)
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    chat_completion_request_to_prompt,
+from llama_stack.apis.inference import (
+    Inference,
+    OpenAIEmbeddingsResponse,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
 )
+from llama_stack.apis.models import Model
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import RunpodImplConfig
 
-# https://docs.runpod.io/serverless/vllm/overview#compatible-models
-# https://github.com/runpod-workers/worker-vllm/blob/main/README.md#compatible-model-architectures
-RUNPOD_SUPPORTED_MODELS = {
-    "Llama3.1-8B": "meta-llama/Llama-3.1-8B",
-    "Llama3.1-70B": "meta-llama/Llama-3.1-70B",
-    "Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
-    "Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
-    "Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
-    "Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
-    "Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
-    "Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
-    "Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
-    "Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
-    "Llama3.2-1B": "meta-llama/Llama-3.2-1B",
-    "Llama3.2-3B": "meta-llama/Llama-3.2-3B",
-}
-
-SAFETY_MODELS_ENTRIES = []
-
-# Create MODEL_ENTRIES from RUNPOD_SUPPORTED_MODELS for compatibility with starter template
-MODEL_ENTRIES = [
-    build_hf_repo_model_entry(provider_model_id, model_descriptor)
-    for provider_model_id, model_descriptor in RUNPOD_SUPPORTED_MODELS.items()
-] + SAFETY_MODELS_ENTRIES
+MODEL_ENTRIES = []
 
 
 class RunpodInferenceAdapter(
+    OpenAIMixin,
     ModelRegistryHelper,
     Inference,
-    OpenAIChatCompletionToLlamaStackMixin,
 ):
+    """
+    Adapter for RunPod's OpenAI-compatible API endpoints.
+    Supports VLLM for serverless endpoint self-hosted or public endpoints.
+    Can work with any runpod endpoints that support OpenAI-compatible API
+    """
+
     def __init__(self, config: RunpodImplConfig) -> None:
-        ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)
+        OpenAIMixin.__init__(self)
+        ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
         self.config = config
 
+    def get_api_key(self) -> str:
+        """Get API key for OpenAI client."""
+        return self.config.api_token
+
+    def get_base_url(self) -> str:
+        """Get base URL for OpenAI client."""
+        return self.config.url
+
     async def initialize(self) -> None:
-        return
+        pass
 
     async def shutdown(self) -> None:
         pass
 
-    async def chat_completion(
+    async def openai_chat_completion(
         self,
         model: str,
-        messages: list[Message],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_choice: ToolChoice | None = ToolChoice.auto,
-        tool_prompt_format: ToolPromptFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-        tool_config: ToolConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        request = ChatCompletionRequest(
+        messages: list[OpenAIMessageParam],
+        frequency_penalty: float | None = None,
+        function_call: str | dict[str, Any] | None = None,
+        functions: list[dict[str, Any]] | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_completion_tokens: int | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        parallel_tool_calls: bool | None = None,
+        presence_penalty: float | None = None,
+        response_format: OpenAIResponseFormatParam | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        tool_choice: str | dict[str, Any] | None = None,
+        tools: list[dict[str, Any]] | None = None,
+        top_logprobs: int | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+    ):
+        """Override to add RunPod-specific stream_options requirement."""
+        if stream and not stream_options:
+            stream_options = {"include_usage": True}
+
+        return await super().openai_chat_completion(
             model=model,
             messages=messages,
-            sampling_params=sampling_params,
-            tools=tools or [],
-            stream=stream,
+            frequency_penalty=frequency_penalty,
+            function_call=function_call,
+            functions=functions,
+            logit_bias=logit_bias,
             logprobs=logprobs,
-            tool_config=tool_config,
+            max_completion_tokens=max_completion_tokens,
+            max_tokens=max_tokens,
+            n=n,
+            parallel_tool_calls=parallel_tool_calls,
+            presence_penalty=presence_penalty,
+            response_format=response_format,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            tool_choice=tool_choice,
+            tools=tools,
+            top_logprobs=top_logprobs,
+            top_p=top_p,
+            user=user,
         )
 
-        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
-        if stream:
-            return self._stream_chat_completion(request, client)
-        else:
-            return await self._nonstream_chat_completion(request, client)
-
-    async def _nonstream_chat_completion(
-        self, request: ChatCompletionRequest, client: OpenAI
-    ) -> ChatCompletionResponse:
-        params = self._get_params(request)
-        r = client.completions.create(**params)
-        return process_chat_completion_response(r, request)
-
-    async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
-        params = self._get_params(request)
-
-        async def _to_async_generator():
-            s = client.completions.create(**params)
-            for chunk in s:
-                yield chunk
-
-        stream = _to_async_generator()
-        async for chunk in process_chat_completion_stream_response(stream, request):
-            yield chunk
-
-    def _get_params(self, request: ChatCompletionRequest) -> dict:
-        return {
-            "model": self.map_to_provider_model(request.model),
-            "prompt": chat_completion_request_to_prompt(request),
-            "stream": request.stream,
-            **get_sampling_options(request.sampling_params),
-        }
+    async def register_model(self, model: Model) -> Model:
+        """
+        Register a model and verify it's available on the RunPod endpoint.
+        In the .yaml file the model: can be defined as example
+        models:
+            - metadata: {}
+            model_id: qwen3-32b-awq
+            model_type: llm
+            provider_id: runpod
+            provider_model_id: Qwen/Qwen3-32B-AWQ
+        """
+        provider_model_id = model.provider_resource_id or model.identifier
+        is_available = await self.check_model_availability(provider_model_id)
+
+        if not is_available:
+            raise ValueError(
+                f"Model {provider_model_id} is not available on RunPod endpoint. "
+                f"Check your RunPod endpoint configuration."
+            )
+
+        return model
 
     async def openai_embeddings(
         self,
@@ -131,4 +137,16 @@ async def openai_embeddings(
         dimensions: int | None = None,
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
+        # Resolve model_id to provider_resource_id
+        model_obj = await self.model_store.get_model(model)
+        provider_model_id = model_obj.provider_resource_id or model
+
+        response = await self.client.embeddings.create(
+            model=provider_model_id,
+            input=input,
+            encoding_format=encoding_format,
+            dimensions=dimensions,
+            user=user,
+        )
+
+        return response