diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 77c5c7187..be7238628 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -3,71 +3,175 @@ # # 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 +import asyncio +from typing import Any -from openai import OpenAI +from openai import AsyncOpenAI -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.apis.inference import * +from llama_stack.apis.inference import ( + OpenAIMessageParam, + OpenAIResponseFormatParam, +) +from llama_stack.apis.common.content_types import InterleavedContentItem +from llama_stack.apis.models import Model, ModelType +from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper from llama_stack.providers.utils.inference.openai_compat import ( - OpenAIChatCompletionToLlamaStackMixin, + convert_message_to_openai_dict, get_sampling_options, process_chat_completion_response, process_chat_completion_stream_response, + process_completion_response, + process_completion_stream_response, ) from llama_stack.providers.utils.inference.prompt_adapter import ( - chat_completion_request_to_prompt, + completion_request_to_prompt, + interleaved_content_as_str, ) - +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( + def get_extra_client_params(self) -> dict[str, Any]: + """Override to add RunPod-specific client parameters if needed.""" + return {} + + async def openai_chat_completion( self, model: str, + 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, + frequency_penalty=frequency_penalty, + function_call=function_call, + functions=functions, + logit_bias=logit_bias, + logprobs=logprobs, + 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, + ) + + async def register_model(self, model: Model) -> Model: + """ + Pass-through registration - accepts any model that the RunPod endpoint serves. + 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 + """ + return model + + async def completion( + self, + model_id: str, + content: InterleavedContent, + sampling_params: SamplingParams | None = None, + response_format: ResponseFormat | None = None, + stream: bool | None = False, + logprobs: LogProbConfig | None = None, + ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]: + if sampling_params is None: + sampling_params = SamplingParams() + + # Resolve model_id to provider_resource_id + model = await self.model_store.get_model(model_id) + provider_model_id = model.provider_resource_id or model_id + + request = CompletionRequest( + model=provider_model_id, + content=content, + sampling_params=sampling_params, + response_format=response_format, + stream=stream, + logprobs=logprobs, + ) + + if stream: + return self._stream_completion(request, self.client) + else: + return await self._nonstream_completion(request, self.client) + + async def chat_completion( + self, + model_id: str, messages: list[Message], sampling_params: SamplingParams | None = None, response_format: ResponseFormat | None = None, @@ -77,11 +181,17 @@ class RunpodInferenceAdapter( stream: bool | None = False, logprobs: LogProbConfig | None = None, tool_config: ToolConfig | None = None, - ) -> AsyncGenerator: + ) -> ChatCompletionResponse | AsyncGenerator[ChatCompletionResponseStreamChunk, None]: + """Process chat completion requests using RunPod's OpenAI-compatible API.""" if sampling_params is None: sampling_params = SamplingParams() + + # Resolve model_id to provider_resource_id + model = await self.model_store.get_model(model_id) + provider_model_id = model.provider_resource_id or model_id + request = ChatCompletionRequest( - model=model, + model=provider_model_id, messages=messages, sampling_params=sampling_params, tools=tools or [], @@ -90,39 +200,100 @@ class RunpodInferenceAdapter( tool_config=tool_config, ) - client = OpenAI(base_url=self.config.url, api_key=self.config.api_token) if stream: - return self._stream_chat_completion(request, client) + return self._stream_chat_completion(request, self.client) else: - return await self._nonstream_chat_completion(request, client) + return await self._nonstream_chat_completion(request, self.client) async def _nonstream_chat_completion( - self, request: ChatCompletionRequest, client: OpenAI + self, request: ChatCompletionRequest, client: AsyncOpenAI ) -> ChatCompletionResponse: - params = self._get_params(request) - r = client.completions.create(**params) + params = await self._get_chat_params(request) + # Make actual RunPod API call + r = await client.chat.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 def _stream_chat_completion( + self, request: ChatCompletionRequest, client: AsyncOpenAI + ) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]: + params = await self._get_chat_params(request) + # Make actual RunPod API call for streaming + stream = await client.chat.completions.create(**params) 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), + async def _get_chat_params(self, request: ChatCompletionRequest) -> dict: + """Convert Llama Stack request to RunPod API parameters.""" + messages = [await convert_message_to_openai_dict(m, download=False) for m in request.messages] + + params = { + "model": request.model, + "messages": messages, "stream": request.stream, **get_sampling_options(request.sampling_params), } + if request.stream: + params["stream_options"] = {"include_usage": True} + + return params + + async def _nonstream_completion( + self, request: CompletionRequest, client: AsyncOpenAI + ) -> CompletionResponse: + params = await self._get_completion_params(request) + # Make actual RunPod API call + r = await client.completions.create(**params) + return process_completion_response(r) + + async def _stream_completion( + self, request: CompletionRequest, client: AsyncOpenAI + ) -> AsyncGenerator: + params = await self._get_completion_params(request) + # Make actual RunPod API call for streaming + stream = await client.completions.create(**params) + async for chunk in process_completion_stream_response(stream): + yield chunk + + async def _get_completion_params(self, request: CompletionRequest) -> dict: + """Convert Llama Stack request to RunPod API parameters.""" + params = { + "model": request.model, + "prompt": await completion_request_to_prompt(request), + "stream": request.stream, + **get_sampling_options(request.sampling_params), + } + + if request.stream: + params["stream_options"] = {"include_usage": True} + + return params + + async def embeddings( + self, + model_id: str, + contents: list[str] | list[InterleavedContentItem], + text_truncation: TextTruncation | None = TextTruncation.none, + output_dimension: int | None = None, + task_type: EmbeddingTaskType | None = None, + ) -> EmbeddingsResponse: + # Resolve model_id to provider_resource_id + model_obj = await self.model_store.get_model(model_id) + model = model_obj.provider_resource_id or model_id + + kwargs = {} + if output_dimension: + kwargs["dimensions"] = output_dimension + + response = await self.client.embeddings.create( + model=model, + input=[interleaved_content_as_str(content) for content in contents], + **kwargs, + ) + + embeddings = [data.embedding for data in response.data] + return EmbeddingsResponse(embeddings=embeddings) + async def openai_embeddings( self, model: str, @@ -131,4 +302,16 @@ class RunpodInferenceAdapter( 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