mirror of
https://github.com/meta-llama/llama-stack.git
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299 lines
11 KiB
Python
299 lines
11 KiB
Python
import httpx
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import uuid
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from typing import AsyncGenerator
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from ollama import AsyncClient
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from llama_models.sku_list import resolve_model
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from llama_models.llama3_1.api.datatypes import (
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BuiltinTool,
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CompletionMessage,
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Message,
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StopReason,
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ToolCall,
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)
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from llama_models.llama3_1.api.tool_utils import ToolUtils
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from .api.config import OllamaImplConfig
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from .api.datatypes import (
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ChatCompletionResponseEvent,
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ChatCompletionResponseEventType,
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ToolCallDelta,
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ToolCallParseStatus,
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)
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from .api.endpoints import (
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ChatCompletionResponse,
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ChatCompletionRequest,
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ChatCompletionResponseStreamChunk,
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CompletionRequest,
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Inference,
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)
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# TODO: Eventually this will move to the llama cli model list command
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# mapping of Model SKUs to ollama models
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OLLAMA_SUPPORTED_SKUS = {
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"Meta-Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16"
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# TODO: Add other variants for llama3.1
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}
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class OllamaInference(Inference):
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def __init__(self, config: OllamaImplConfig) -> None:
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self.config = config
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self.model = config.model
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async def initialize(self) -> None:
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self.client = AsyncClient(host=self.config.url)
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try:
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status = await self.client.pull(self.model)
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assert status['status'] == 'success', f"Failed to pull model {self.model} in ollama"
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except httpx.ConnectError:
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print("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
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raise
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
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ollama_messages = []
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for message in messages:
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ollama_messages.append(
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{"role": message.role, "content": message.content}
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)
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return ollama_messages
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def resolve_ollama_model(self, model_name: str) -> str:
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model = resolve_model(model_name)
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assert (
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model is not None and
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model.descriptor(shorten_default_variant=True) in OLLAMA_SUPPORTED_SKUS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}"
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return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True))
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def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict:
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options = {}
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if request.sampling_params is not None:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(request.sampling_params, attr):
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options[attr] = getattr(request.sampling_params, attr)
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if (
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request.sampling_params.repetition_penalty is not None and
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request.sampling_params.repetition_penalty != 1.0
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):
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options["repeat_penalty"] = request.sampling_params.repetition_penalty
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return options
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async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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# accumulate sampling params and other options to pass to ollama
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options = self.get_ollama_chat_options(request)
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ollama_model = self.resolve_ollama_model(request.model)
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if not request.stream:
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r = await self.client.chat(
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model=ollama_model,
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messages=self._messages_to_ollama_messages(request.messages),
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stream=False,
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options=options,
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)
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stop_reason = None
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if r['done']:
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if r['done_reason'] == 'stop':
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stop_reason = StopReason.end_of_turn
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elif r['done_reason'] == 'length':
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stop_reason = StopReason.out_of_tokens
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completion_message = decode_assistant_message_from_content(
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r['message']['content'],
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stop_reason,
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)
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yield ChatCompletionResponse(
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completion_message=completion_message,
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logprobs=None,
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)
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else:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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stream = await self.client.chat(
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model=ollama_model,
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messages=self._messages_to_ollama_messages(request.messages),
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stream=True,
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options=options,
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)
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buffer = ""
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ipython = False
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stop_reason = None
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async for chunk in stream:
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# check if ollama is done
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if chunk['done']:
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if chunk['done_reason'] == 'stop':
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stop_reason = StopReason.end_of_turn
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elif chunk['done_reason'] == 'length':
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stop_reason = StopReason.out_of_tokens
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break
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text = chunk['message']['content']
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# check if its a tool call ( aka starts with <|python_tag|> )
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if not ipython and text.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer = buffer[len("<|python_tag|>") :]
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continue
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if ipython:
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if text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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continue
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elif text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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continue
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buffer += text
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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else:
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buffer += text
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=text,
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stop_reason=stop_reason,
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)
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)
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# parse tool calls and report errors
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message = decode_assistant_message_from_content(buffer, stop_reason)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta="",
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stop_reason=stop_reason,
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)
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)
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#TODO: Consolidate this with impl in llama-models
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def decode_assistant_message_from_content(
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content: str,
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stop_reason: StopReason,
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) -> CompletionMessage:
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ipython = content.startswith("<|python_tag|>")
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if ipython:
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content = content[len("<|python_tag|>") :]
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if content.endswith("<|eot_id|>"):
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content = content[: -len("<|eot_id|>")]
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stop_reason = StopReason.end_of_turn
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elif content.endswith("<|eom_id|>"):
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content = content[: -len("<|eom_id|>")]
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stop_reason = StopReason.end_of_message
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tool_name = None
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tool_arguments = {}
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custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content)
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if custom_tool_info is not None:
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tool_name, tool_arguments = custom_tool_info
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# Sometimes when agent has custom tools alongside builin tools
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# Agent responds for builtin tool calls in the format of the custom tools
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# This code tries to handle that case
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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tool_arguments = {
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"query": list(tool_arguments.values())[0],
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}
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else:
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builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
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if builtin_tool_info is not None:
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tool_name, query = builtin_tool_info
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tool_arguments = {
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"query": query,
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}
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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elif ipython:
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tool_name = BuiltinTool.code_interpreter
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tool_arguments = {
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"code": content,
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}
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tool_calls = []
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if tool_name is not None and tool_arguments is not None:
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call_id = str(uuid.uuid4())
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tool_calls.append(
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ToolCall(
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call_id=call_id,
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tool_name=tool_name,
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arguments=tool_arguments,
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)
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)
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content = ""
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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return CompletionMessage(
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content=content,
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stop_reason=stop_reason,
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tool_calls=tool_calls,
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)
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