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Update runpod.py
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Justin
parent
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commit
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1 changed files with 188 additions and 48 deletions
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@ -3,52 +3,29 @@
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from collections.abc import AsyncGenerator
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from openai import OpenAI
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.inference import *
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from llama_stack.apis.inference import OpenAIEmbeddingsResponse
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# from llama_stack.providers.datatypes import ModelsProtocolPrivate
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, build_hf_repo_model_entry
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from llama_stack.apis.models import Model, ModelType
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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OpenAIChatCompletionToLlamaStackMixin,
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OpenAICompletionToLlamaStackMixin,
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get_sampling_options,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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process_completion_response,
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process_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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completion_request_to_prompt,
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interleaved_content_as_str,
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)
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from .config import RunpodImplConfig
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# https://docs.runpod.io/serverless/vllm/overview#compatible-models
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# https://github.com/runpod-workers/worker-vllm/blob/main/README.md#compatible-model-architectures
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RUNPOD_SUPPORTED_MODELS = {
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"Llama3.1-8B": "meta-llama/Llama-3.1-8B",
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"Llama3.1-70B": "meta-llama/Llama-3.1-70B",
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"Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
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"Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
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"Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
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"Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
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"Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
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"Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
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"Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
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"Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
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"Llama3.2-1B": "meta-llama/Llama-3.2-1B",
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"Llama3.2-3B": "meta-llama/Llama-3.2-3B",
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}
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SAFETY_MODELS_ENTRIES = []
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# Create MODEL_ENTRIES from RUNPOD_SUPPORTED_MODELS for compatibility with starter template
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MODEL_ENTRIES = [
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build_hf_repo_model_entry(provider_model_id, model_descriptor)
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for provider_model_id, model_descriptor in RUNPOD_SUPPORTED_MODELS.items()
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] + SAFETY_MODELS_ENTRIES
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MODEL_ENTRIES = []
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class RunpodInferenceAdapter(
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@ -57,30 +34,83 @@ class RunpodInferenceAdapter(
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OpenAIChatCompletionToLlamaStackMixin,
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OpenAICompletionToLlamaStackMixin,
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):
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"""
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Adapter for RunPod's OpenAI-compatible API endpoints.
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Supports VLLM for serverless endpoint self-hosted or public endpoints.
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Can work with any runpod endpoints that support OpenAI-compatible API
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"""
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def __init__(self, config: RunpodImplConfig) -> None:
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ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)
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ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
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self.config = config
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async def initialize(self) -> None:
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return
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pass
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async def shutdown(self) -> None:
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pass
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async def register_model(self, model: Model) -> Model:
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"""
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Register any model with the runpod provider_id.
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Pass-through registration - accepts any model string that the RunPod endpoint serves.
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No static model validation since RunPod endpoints can serve arbitrary vLLM models.
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YAML Configuration Example:
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models:
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- metadata: {}
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model_id: runpod/qwen/qwen3-8b
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model_type: llm
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provider_id: runpod
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provider_model_id: qwen/qwen3-8b
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- metadata: {}
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model_id: runpod/deepcogito/cogito-v2-preview-llama-70B
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model_type: llm
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provider_id: runpod
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provider_model_id: deepcogito/cogito-v2-preview-llama-70B
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The provider strips 'runpod/' prefix before API calls:
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"runpod/qwen/qwen3-8b" -> "qwen/qwen3-8b"
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"""
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if model.provider_id == "runpod":
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logger.info(
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f"Registering model: {model.identifier} -> {model.provider_resource_id}"
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)
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return model
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return await super().register_model(model)
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async def completion(
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self,
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model: str,
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model_id: str,
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content: InterleavedContent,
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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) -> AsyncGenerator:
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raise NotImplementedError()
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if sampling_params is None:
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sampling_params = SamplingParams()
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request = CompletionRequest(
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model=model_id,
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content=content,
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sampling_params=sampling_params,
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response_format=response_format,
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stream=stream,
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logprobs=logprobs,
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)
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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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if stream:
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return self._stream_completion(request, client)
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else:
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return await self._nonstream_completion(request, client)
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async def chat_completion(
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self,
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model: str,
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model_id: str,
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messages: list[Message],
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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@ -91,10 +121,12 @@ class RunpodInferenceAdapter(
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logprobs: LogProbConfig | None = None,
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tool_config: ToolConfig | None = None,
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) -> AsyncGenerator:
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"""Process chat completion requests using RunPod's OpenAI-compatible API."""
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if sampling_params is None:
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sampling_params = SamplingParams()
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request = ChatCompletionRequest(
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model=model,
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model=model_id,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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@ -104,6 +136,7 @@ class RunpodInferenceAdapter(
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)
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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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if stream:
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return self._stream_chat_completion(request, client)
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else:
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@ -112,15 +145,17 @@ class RunpodInferenceAdapter(
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> ChatCompletionResponse:
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params = self._get_params(request)
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r = client.completions.create(**params)
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params = await self._get_chat_params(request)
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r = client.chat.completions.create(**params)
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return process_chat_completion_response(r, request)
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async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
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params = self._get_params(request)
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> AsyncGenerator:
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params = await self._get_chat_params(request)
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async def _to_async_generator():
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s = client.completions.create(**params)
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s = client.chat.completions.create(**params)
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for chunk in s:
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yield chunk
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@ -128,14 +163,102 @@ class RunpodInferenceAdapter(
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async for chunk in process_chat_completion_stream_response(stream, request):
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yield chunk
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def _get_params(self, request: ChatCompletionRequest) -> dict:
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return {
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"model": self.map_to_provider_model(request.model),
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"prompt": chat_completion_request_to_prompt(request),
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async def _get_chat_params(self, request: ChatCompletionRequest) -> dict:
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"""Convert Llama Stack request to RunPod API parameters."""
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messages = [
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{"role": msg.role, "content": msg.content} for msg in request.messages
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]
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# Resolve model_id to provider_resource_id
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model_obj = await self.model_store.get_model(request.model)
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model = model_obj.provider_resource_id or request.model
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if model.startswith("runpod/"):
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model = model.replace("runpod/", "", 1)
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params = {
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"model": model,
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"messages": messages,
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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if request.stream:
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params["stream_options"] = {"include_usage": True}
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return params
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async def _nonstream_completion(
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self, request: CompletionRequest, client: OpenAI
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) -> CompletionResponse:
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params = await self._get_completion_params(request)
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r = client.completions.create(**params)
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return process_completion_response(r)
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async def _stream_completion(
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self, request: CompletionRequest, client: OpenAI
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) -> AsyncGenerator:
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params = await self._get_completion_params(request)
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async def _to_async_generator():
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s = client.completions.create(**params)
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for chunk in s:
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yield chunk
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stream = _to_async_generator()
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async for chunk in process_completion_stream_response(stream):
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yield chunk
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async def _get_completion_params(self, request: CompletionRequest) -> dict:
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# Resolve model_id to provider_resource_id
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model_obj = await self.model_store.get_model(request.model)
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model = model_obj.provider_resource_id or request.model
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if model.startswith("runpod/"):
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model = model.replace("runpod/", "", 1)
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params = {
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"model": model,
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"prompt": completion_request_to_prompt(request),
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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if request.stream:
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params["stream_options"] = {"include_usage": True}
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return params
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async def embeddings(
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self,
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model_id: str,
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contents: list[str] | list[InterleavedContentItem],
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text_truncation: TextTruncation | None = TextTruncation.none,
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output_dimension: int | None = None,
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task_type: EmbeddingTaskType | None = None,
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) -> EmbeddingsResponse:
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# Resolve model_id to provider_resource_id
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model_obj = await self.model_store.get_model(model_id)
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model = model_obj.provider_resource_id or model_id
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if model.startswith("runpod/"):
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model = model.replace("runpod/", "", 1)
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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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kwargs = {}
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if output_dimension:
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kwargs["dimensions"] = output_dimension
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response = client.embeddings.create(
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model=model,
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input=[interleaved_content_as_str(content) for content in contents],
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**kwargs,
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)
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embeddings = [data.embedding for data in response.data]
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return EmbeddingsResponse(embeddings=embeddings)
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async def openai_embeddings(
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self,
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model: str,
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@ -144,4 +267,21 @@ class RunpodInferenceAdapter(
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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raise NotImplementedError()
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# Resolve model_id to provider_resource_id
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model_obj = await self.model_store.get_model(model)
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model_stripped = model_obj.provider_resource_id or model
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if model_stripped.startswith("runpod/"):
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model_stripped = model_stripped.replace("runpod/", "", 1)
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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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response = client.embeddings.create(
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model=model_stripped,
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input=input,
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encoding_format=encoding_format,
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dimensions=dimensions,
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user=user,
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)
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return response
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