forked from phoenix-oss/llama-stack-mirror
impls -> inline, adapters -> remote (#381)
This commit is contained in:
Ashwin Bharambe
GitHub
parent
b10e9f46bb
commit
994732e2e0
169 changed files with 106 additions and 105 deletions
299
llama_stack/providers/remote/inference/ollama/ollama.py
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299
llama_stack/providers/remote/inference/ollama/ollama.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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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 typing import AsyncGenerator
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import httpx
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message
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from llama_models.llama3.api.tokenizer import Tokenizer
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from ollama import AsyncClient
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.datatypes import ModelsProtocolPrivate
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_options,
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OpenAICompatCompletionChoice,
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OpenAICompatCompletionResponse,
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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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convert_image_media_to_url,
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request_has_media,
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)
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OLLAMA_SUPPORTED_MODELS = {
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"Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
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"Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
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"Llama3.2-1B-Instruct": "llama3.2:1b-instruct-fp16",
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"Llama3.2-3B-Instruct": "llama3.2:3b-instruct-fp16",
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"Llama-Guard-3-8B": "llama-guard3:8b",
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"Llama-Guard-3-1B": "llama-guard3:1b",
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"Llama3.2-11B-Vision-Instruct": "x/llama3.2-vision:11b-instruct-fp16",
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}
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class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
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def __init__(self, url: str) -> None:
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self.url = url
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self.formatter = ChatFormat(Tokenizer.get_instance())
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@property
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def client(self) -> AsyncClient:
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return AsyncClient(host=self.url)
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async def initialize(self) -> None:
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print("Initializing Ollama, checking connectivity to server...")
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try:
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await self.client.ps()
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except httpx.ConnectError as e:
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raise RuntimeError(
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"Ollama Server is not running, start it using `ollama serve` in a separate terminal"
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) from e
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async def shutdown(self) -> None:
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pass
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async def register_model(self, model: ModelDef) -> None:
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raise ValueError("Dynamic model registration is not supported")
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async def list_models(self) -> List[ModelDef]:
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ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
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ret = []
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res = await self.client.ps()
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for r in res["models"]:
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if r["model"] not in ollama_to_llama:
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print(f"Ollama is running a model unknown to Llama Stack: {r['model']}")
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continue
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llama_model = ollama_to_llama[r["model"]]
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ret.append(
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ModelDef(
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identifier=llama_model,
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llama_model=llama_model,
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metadata={
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"ollama_model": r["model"],
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},
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)
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)
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return ret
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async def completion(
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self,
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model: str,
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content: InterleavedTextMedia,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = CompletionRequest(
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model=model,
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content=content,
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sampling_params=sampling_params,
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stream=stream,
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logprobs=logprobs,
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)
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if stream:
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return self._stream_completion(request)
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else:
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return await self._nonstream_completion(request)
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async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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async def _generate_and_convert_to_openai_compat():
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s = await self.client.generate(**params)
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async for chunk in s:
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choice = OpenAICompatCompletionChoice(
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finish_reason=chunk["done_reason"] if chunk["done"] else None,
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text=chunk["response"],
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_completion_stream_response(stream, self.formatter):
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yield chunk
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async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
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params = await self._get_params(request)
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r = await self.client.generate(**params)
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assert isinstance(r, dict)
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choice = OpenAICompatCompletionChoice(
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finish_reason=r["done_reason"] if r["done"] else None,
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text=r["response"],
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)
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response = OpenAICompatCompletionResponse(
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choices=[choice],
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)
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return process_completion_response(response, self.formatter)
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async def chat_completion(
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self,
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model: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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response_format: Optional[ResponseFormat] = None,
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = ChatCompletionRequest(
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model=model,
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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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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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if stream:
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return self._stream_chat_completion(request)
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else:
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return await self._nonstream_chat_completion(request)
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async def _get_params(
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self, request: Union[ChatCompletionRequest, CompletionRequest]
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) -> dict:
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sampling_options = get_sampling_options(request.sampling_params)
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# This is needed since the Ollama API expects num_predict to be set
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# for early truncation instead of max_tokens.
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if sampling_options.get("max_tokens") is not None:
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sampling_options["num_predict"] = sampling_options["max_tokens"]
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input_dict = {}
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media_present = request_has_media(request)
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if isinstance(request, ChatCompletionRequest):
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if media_present:
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contents = [
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await convert_message_to_dict_for_ollama(m)
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for m in request.messages
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]
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# flatten the list of lists
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input_dict["messages"] = [
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item for sublist in contents for item in sublist
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]
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else:
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input_dict["raw"] = True
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input_dict["prompt"] = chat_completion_request_to_prompt(
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request, self.formatter
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)
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else:
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assert (
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not media_present
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), "Ollama does not support media for Completion requests"
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input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
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input_dict["raw"] = True
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return {
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"model": OLLAMA_SUPPORTED_MODELS[request.model],
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**input_dict,
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"options": sampling_options,
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"stream": request.stream,
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}
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest
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) -> ChatCompletionResponse:
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params = await self._get_params(request)
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if "messages" in params:
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r = await self.client.chat(**params)
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else:
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r = await self.client.generate(**params)
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assert isinstance(r, dict)
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if "message" in r:
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choice = OpenAICompatCompletionChoice(
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finish_reason=r["done_reason"] if r["done"] else None,
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text=r["message"]["content"],
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)
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else:
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choice = OpenAICompatCompletionChoice(
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finish_reason=r["done_reason"] if r["done"] else None,
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text=r["response"],
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)
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response = OpenAICompatCompletionResponse(
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choices=[choice],
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)
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return process_chat_completion_response(response, self.formatter)
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest
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) -> AsyncGenerator:
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params = await self._get_params(request)
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async def _generate_and_convert_to_openai_compat():
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if "messages" in params:
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s = await self.client.chat(**params)
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else:
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s = await self.client.generate(**params)
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async for chunk in s:
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if "message" in chunk:
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choice = OpenAICompatCompletionChoice(
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finish_reason=chunk["done_reason"] if chunk["done"] else None,
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text=chunk["message"]["content"],
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)
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else:
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choice = OpenAICompatCompletionChoice(
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finish_reason=chunk["done_reason"] if chunk["done"] else None,
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text=chunk["response"],
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)
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yield OpenAICompatCompletionResponse(
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choices=[choice],
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)
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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_chat_completion_stream_response(
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stream, self.formatter
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):
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yield chunk
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async def embeddings(
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self,
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model: str,
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contents: List[InterleavedTextMedia],
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) -> EmbeddingsResponse:
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raise NotImplementedError()
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async def convert_message_to_dict_for_ollama(message: Message) -> List[dict]:
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async def _convert_content(content) -> dict:
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if isinstance(content, ImageMedia):
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return {
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"role": message.role,
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"images": [
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await convert_image_media_to_url(
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content, download=True, include_format=False
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)
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],
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}
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else:
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return {
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"role": message.role,
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"content": content,
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}
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if isinstance(message.content, list):
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return [await _convert_content(c) for c in message.content]
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else:
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return [await _convert_content(message.content)]
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