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Yuan Tang
parent
ad4e65e876
commit
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3 changed files with 71 additions and 217 deletions
10
llama_stack/distribution/templates/remote-vllm-build.yaml
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10
llama_stack/distribution/templates/remote-vllm-build.yaml
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@ -0,0 +1,10 @@
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name: remote-vllm
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distribution_spec:
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description: Use remote vLLM for running LLM inference
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providers:
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inference: remote::vllm
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memory: meta-reference
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safety: meta-reference
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agents: meta-reference
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telemetry: meta-reference
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image_type: docker
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@ -9,14 +9,9 @@ from .vllm import VLLMInferenceAdapter
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async def get_adapter_impl(config: VLLMImplConfig, _deps):
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assert isinstance(config, VLLMImplConfig), f"Unexpected config type: {type(config)}"
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if config.url is not None:
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impl = VLLMInferenceAdapter(config)
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else:
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raise ValueError(
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"Invalid configuration. Specify either an URL or HF Inference Endpoint details (namespace and endpoint name)."
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)
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assert isinstance(
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config, VLLMImplConfig
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), f"Unexpected config type: {type(config)}"
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impl = VLLMInferenceAdapter(config)
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await impl.initialize()
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return impl
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@ -3,42 +3,44 @@
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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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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 llama_models.sku_list import resolve_model
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from openai import OpenAI
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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 augment_messages_for_tools
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from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_options,
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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.prompt_adapter import (
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chat_completion_request_to_prompt,
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)
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from .config import VLLMImplConfig
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# Reference: https://docs.vllm.ai/en/latest/models/supported_models.html
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VLLM_SUPPORTED_MODELS = {
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"Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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# "Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
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# "Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
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"Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3-70B-Instruct",
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"Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
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}
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class VLLMInferenceAdapter(Inference):
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class VLLMInferenceAdapter(ModelRegistryHelper, Inference):
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def __init__(self, config: VLLMImplConfig) -> None:
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self.config = config
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tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(tokenizer)
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@property
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def client(self) -> OpenAI:
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return OpenAI(
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api_key=self.config.api_token,
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base_url=self.config.url
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ModelRegistryHelper.__init__(
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self, stack_to_provider_models_map=VLLM_SUPPORTED_MODELS
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)
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self.config = config
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self.formatter = ChatFormat(Tokenizer.get_instance())
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async def initialize(self) -> None:
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return
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@ -46,41 +48,10 @@ class VLLMInferenceAdapter(Inference):
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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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def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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def _messages_to_vllm_messages(self, messages: list[Message]) -> list:
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vllm_messages = []
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for message in messages:
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if message.role == "ipython":
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role = "tool"
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else:
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role = message.role
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vllm_messages.append({"role": role, "content": message.content})
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return vllm_messages
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def resolve_vllm_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
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and model.descriptor(shorten_default_variant=True)
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in VLLM_SUPPORTED_MODELS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(VLLM_SUPPORTED_MODELS.keys())}"
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return VLLM_SUPPORTED_MODELS.get(
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model.descriptor(shorten_default_variant=True)
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)
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def get_vllm_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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return options
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async def chat_completion(
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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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@ -91,7 +62,6 @@ class VLLMInferenceAdapter(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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# wrapper request to make it easier to pass around (internal only, not exposed to API)
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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@ -103,167 +73,46 @@ class VLLMInferenceAdapter(Inference):
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logprobs=logprobs,
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)
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# accumulate sampling params and other options to pass to vLLM
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options = self.get_vllm_chat_options(request)
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vllm_model = self.resolve_vllm_model(request.model)
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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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input_tokens = len(model_input.tokens)
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max_new_tokens = min(
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request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
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self.max_tokens - input_tokens - 1,
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)
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print(f"Calculated max_new_tokens: {max_new_tokens}")
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assert (
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request.model == self.model_name
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), f"Model mismatch, expected {self.model_name}, got {request.model}"
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if not request.stream:
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r = self.client.chat.completions.create(
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model=vllm_model,
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messages=self._messages_to_vllm_messages(messages),
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max_tokens=max_new_tokens,
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stream=False,
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**options,
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)
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stop_reason = None
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if r.choices[0].finish_reason:
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if (
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r.choices[0].finish_reason == "stop"
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or r.choices[0].finish_reason == "eos"
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):
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stop_reason = StopReason.end_of_turn
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elif r.choices[0].finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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completion_message = self.formatter.decode_assistant_message_from_content(
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r.choices[0].message.content, 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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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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if stream:
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return self._stream_chat_completion(request, client)
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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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return self._nonstream_chat_completion(request, client)
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buffer = ""
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ipython = False
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stop_reason = None
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> ChatCompletionResponse:
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params = self._get_params(request)
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r = client.completions.create(**params)
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return process_chat_completion_response(request, r, self.formatter)
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for chunk in self.client.chat.completions.create(
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model=vllm_model,
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messages=self._messages_to_vllm_messages(messages),
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max_tokens=max_new_tokens,
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stream=True,
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**options,
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):
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if chunk.choices[0].finish_reason:
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if (
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stop_reason is None and chunk.choices[0].finish_reason == "stop"
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) or (
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stop_reason is None and chunk.choices[0].finish_reason == "eos"
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):
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stop_reason = StopReason.end_of_turn
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elif (
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stop_reason is None
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and chunk.choices[0].finish_reason == "length"
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):
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stop_reason = StopReason.out_of_tokens
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break
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async def _stream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> AsyncGenerator:
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params = self._get_params(request)
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text = chunk.choices[0].message.content
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if text is None:
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continue
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async def _to_async_generator():
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s = client.completions.create(**params)
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for chunk in s:
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yield chunk
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# check if it's 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 += text
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continue
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stream = _to_async_generator()
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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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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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def _get_params(self, request: ChatCompletionRequest) -> dict:
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return {
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"model": self.map_to_provider_model(request.model),
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"prompt": chat_completion_request_to_prompt(request, self.formatter),
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"stream": request.stream,
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**get_sampling_options(request),
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}
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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 = 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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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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