# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from typing import AsyncGenerator import httpx from llama_models.llama3.api.chat_format import ChatFormat from llama_models.llama3.api.datatypes import Message, StopReason from llama_models.llama3.api.tokenizer import Tokenizer from llama_models.sku_list import resolve_model from ollama import AsyncClient from llama_stack.apis.inference import * # noqa: F403 from llama_stack.providers.utils.inference.augment_messages import ( augment_messages_for_tools, ) # TODO: Eventually this will move to the llama cli model list command # mapping of Model SKUs to ollama models OLLAMA_SUPPORTED_SKUS = { # "Llama3.1-8B-Instruct": "llama3.1", "Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16", "Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16", } class OllamaInferenceAdapter(Inference): def __init__(self, url: str) -> None: self.url = url tokenizer = Tokenizer.get_instance() self.formatter = ChatFormat(tokenizer) @property def client(self) -> AsyncClient: return AsyncClient(host=self.url) async def initialize(self) -> None: print("Initializing Ollama, checking connectivity to server...") try: await self.client.ps() except httpx.ConnectError as e: raise RuntimeError( "Ollama Server is not running, start it using `ollama serve` in a separate terminal" ) from e async def shutdown(self) -> None: pass async def completion( self, model: str, content: InterleavedTextMedia, sampling_params: Optional[SamplingParams] = SamplingParams(), stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: raise NotImplementedError() def _messages_to_ollama_messages(self, messages: list[Message]) -> list: ollama_messages = [] for message in messages: if message.role == "ipython": role = "tool" else: role = message.role ollama_messages.append({"role": role, "content": message.content}) return ollama_messages def resolve_ollama_model(self, model_name: str) -> str: model = resolve_model(model_name) assert ( model is not None and model.descriptor(shorten_default_variant=True) in OLLAMA_SUPPORTED_SKUS ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}" return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True)) def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict: options = {} if request.sampling_params is not None: for attr in {"temperature", "top_p", "top_k", "max_tokens"}: if getattr(request.sampling_params, attr): options[attr] = getattr(request.sampling_params, attr) if ( request.sampling_params.repetition_penalty is not None and request.sampling_params.repetition_penalty != 1.0 ): options["repeat_penalty"] = request.sampling_params.repetition_penalty return options async def chat_completion( self, model: str, messages: List[Message], sampling_params: Optional[SamplingParams] = SamplingParams(), tools: Optional[List[ToolDefinition]] = None, tool_choice: Optional[ToolChoice] = ToolChoice.auto, tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json, stream: Optional[bool] = False, logprobs: Optional[LogProbConfig] = None, ) -> AsyncGenerator: request = ChatCompletionRequest( model=model, messages=messages, sampling_params=sampling_params, tools=tools or [], tool_choice=tool_choice, tool_prompt_format=tool_prompt_format, stream=stream, logprobs=logprobs, ) messages = augment_messages_for_tools(request) # accumulate sampling params and other options to pass to ollama options = self.get_ollama_chat_options(request) ollama_model = self.resolve_ollama_model(request.model) res = await self.client.ps() need_model_pull = True for r in res["models"]: if ollama_model == r["model"]: need_model_pull = False break if need_model_pull: print(f"Pulling model: {ollama_model}") status = await self.client.pull(ollama_model) assert ( status["status"] == "success" ), f"Failed to pull model {self.model} in ollama" if not request.stream: r = await self.client.chat( model=ollama_model, messages=self._messages_to_ollama_messages(messages), stream=False, options=options, ) stop_reason = None if r["done"]: if r["done_reason"] == "stop": stop_reason = StopReason.end_of_turn elif r["done_reason"] == "length": stop_reason = StopReason.out_of_tokens completion_message = self.formatter.decode_assistant_message_from_content( r["message"]["content"], stop_reason ) yield ChatCompletionResponse( completion_message=completion_message, logprobs=None, ) else: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.start, delta="", ) ) stream = await self.client.chat( model=ollama_model, messages=self._messages_to_ollama_messages(messages), stream=True, options=options, ) buffer = "" ipython = False stop_reason = None async for chunk in stream: if chunk["done"]: if stop_reason is None and chunk["done_reason"] == "stop": stop_reason = StopReason.end_of_turn elif stop_reason is None and chunk["done_reason"] == "length": stop_reason = StopReason.out_of_tokens break text = chunk["message"]["content"] # check if its a tool call ( aka starts with <|python_tag|> ) if not ipython and text.startswith("<|python_tag|>"): ipython = True yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.started, ), ) ) buffer += text continue if ipython: if text == "<|eot_id|>": stop_reason = StopReason.end_of_turn text = "" continue elif text == "<|eom_id|>": stop_reason = StopReason.end_of_message text = "" continue buffer += text delta = ToolCallDelta( content=text, parse_status=ToolCallParseStatus.in_progress, ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=delta, stop_reason=stop_reason, ) ) else: buffer += text yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=text, stop_reason=stop_reason, ) ) # parse tool calls and report errors message = self.formatter.decode_assistant_message_from_content( buffer, stop_reason ) parsed_tool_calls = len(message.tool_calls) > 0 if ipython and not parsed_tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content="", parse_status=ToolCallParseStatus.failure, ), stop_reason=stop_reason, ) ) for tool_call in message.tool_calls: yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta=ToolCallDelta( content=tool_call, parse_status=ToolCallParseStatus.success, ), stop_reason=stop_reason, ) ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.complete, delta="", stop_reason=stop_reason, ) )