mirror of
https://github.com/meta-llama/llama-stack.git
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introduce openai_compat with the completions (not chat-completions) API
This keeps the prompt encoding layer in our control (see `chat_completion_request_to_prompt()` method)
This commit is contained in:
Ashwin Bharambe
Ashwin Bharambe
parent
0c9eb3341c
commit
05e73d12b3
6 changed files with 354 additions and 513 deletions
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@ -9,17 +9,22 @@ 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, StopReason
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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.utils.inference.augment_messages import (
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augment_messages_for_tools,
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chat_completion_request_to_prompt,
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)
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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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)
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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OLLAMA_SUPPORTED_MODELS = {
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"Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
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@ -30,14 +35,10 @@ OLLAMA_SUPPORTED_MODELS = {
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}
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class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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class OllamaInferenceAdapter(Inference):
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def __init__(self, url: str) -> None:
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ModelRegistryHelper.__init__(
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self, stack_to_provider_models_map=OLLAMA_SUPPORTED_MODELS
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)
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self.url = url
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self.tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(self.tokenizer)
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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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@ -55,6 +56,28 @@ class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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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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if model.identifier not in OLLAMA_SUPPORTED_MODELS:
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raise ValueError(
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f"Unsupported model {model.identifier}. Supported models: {OLLAMA_SUPPORTED_MODELS.keys()}"
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)
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ollama_model = OLLAMA_SUPPORTED_MODELS[model.identifier]
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res = await self.client.ps()
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need_model_pull = True
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for r in res["models"]:
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if ollama_model == r["model"]:
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need_model_pull = False
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break
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print(f"Ollama model `{ollama_model}` needs pull -> {need_model_pull}")
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if need_model_pull:
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print(f"Pulling model: {ollama_model}")
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status = await self.client.pull(ollama_model)
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assert (
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status["status"] == "success"
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), f"Failed to pull model {self.model} in ollama"
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def completion(
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self,
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model: str,
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@ -65,20 +88,6 @@ class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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) -> AsyncGenerator:
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raise NotImplementedError()
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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
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and 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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def chat_completion(
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self,
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model: str,
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@ -90,22 +99,6 @@ class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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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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ollama_model = self.map_to_provider_model(model)
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res = await self.client.ps()
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need_model_pull = True
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for r in res["models"]:
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if ollama_model == r["model"]:
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need_model_pull = False
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break
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if need_model_pull:
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print(f"Pulling model: {ollama_model}")
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status = await self.client.pull(ollama_model)
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assert (
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status["status"] == "success"
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), f"Failed to pull model {self.model} in ollama"
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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@ -116,24 +109,16 @@ class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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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 self._nonstream_chat_completion(request)
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def _get_params(self, request: ChatCompletionRequest) -> dict:
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messages = augment_messages_for_tools(request)
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model_input = self.formatter.encode_dialog_prompt(messages)
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prompt = self.tokenizer.decode(model_input.tokens)
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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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return {
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"model": self.map_to_provider_model(request.model),
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"prompt": prompt,
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"options": options,
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"model": OLLAMA_SUPPORTED_MODELS[request.model],
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"prompt": chat_completion_request_to_prompt(request, self.formatter),
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"options": get_sampling_options(request),
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"raw": True,
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"stream": request.stream,
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}
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@ -143,129 +128,35 @@ class OllamaInferenceAdapter(ModelRegistryHelper, Inference):
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) -> ChatCompletionResponse:
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params = self._get_params(request)
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r = await self.client.generate(**params)
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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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assert isinstance(r, dict)
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completion_message = self.formatter.decode_assistant_message_from_content(
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r["response"], stop_reason
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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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return ChatCompletionResponse(
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completion_message=completion_message,
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logprobs=None,
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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(request, 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 = self._get_params(request)
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stream = await self.client.generate(**params)
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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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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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if chunk["done"]:
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if stop_reason is None and chunk["done_reason"] == "stop":
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stop_reason = StopReason.end_of_turn
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elif stop_reason is None and 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["response"]
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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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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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buffer += text
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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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yield OpenAICompatCompletionResponse(
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choices=[choice],
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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 = self.formatter.decode_assistant_message_from_content(
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buffer, stop_reason
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)
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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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stream = _generate_and_convert_to_openai_compat()
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async for chunk in process_chat_completion_stream_response(
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request, stream, self.formatter
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):
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yield chunk
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