# 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.prepare_messages import prepare_messages from termcolor import cprint # TODO: Eventually this will move to the llama cli model list command # mapping of Model SKUs to ollama models OLLAMA_SUPPORTED_SKUS = { # "Meta-Llama3.1-8B-Instruct": "llama3.1", "Meta-Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16", "Meta-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: pass # 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, request: CompletionRequest) -> 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: cprint("!! calling remote ollama {}, url={}!!".format(model, self.url), "red") yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.start, delta="", ) ) yield ChatCompletionResponseStreamChunk( event=ChatCompletionResponseEvent( event_type=ChatCompletionResponseEventType.progress, delta="model={}, url={}".format(model, self.url), ) ) # 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 = prepare_messages(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, # ) # )