llama-stack/llama_stack/providers/adapters/inference/tgi/tgi.py

# 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.


import logging
from typing import AsyncGenerator, List, Optional

from huggingface_hub import AsyncInferenceClient, HfApi
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import all_registered_models

from llama_stack.apis.inference import *  # noqa: F403
from llama_stack.apis.models import *  # noqa: F403

from llama_stack.providers.datatypes import ModelDef, ModelsProtocolPrivate

from llama_stack.providers.utils.inference.openai_compat import (
    get_sampling_options,
    OpenAICompatCompletionChoice,
    OpenAICompatCompletionResponse,
    process_chat_completion_response,
    process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
    chat_completion_request_to_model_input_info,
)

from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig

logger = logging.getLogger(__name__)


class _HfAdapter(Inference, ModelsProtocolPrivate):
    client: AsyncInferenceClient
    max_tokens: int
    model_id: str

    def __init__(self) -> None:
        self.formatter = ChatFormat(Tokenizer.get_instance())
        self.huggingface_repo_to_llama_model_id = {
            model.huggingface_repo: model.descriptor()
            for model in all_registered_models()
            if model.huggingface_repo
        }

    async def register_model(self, model: ModelDef) -> None:
        raise ValueError("Model registration is not supported for HuggingFace models")

    async def list_models(self) -> List[ModelDef]:
        repo = self.model_id
        identifier = self.huggingface_repo_to_llama_model_id[repo]
        return [
            ModelDef(
                identifier=identifier,
                llama_model=identifier,
                metadata={
                    "huggingface_repo": repo,
                },
            )
        ]

    async def shutdown(self) -> None:
        pass

    def completion(
        self,
        model: str,
        content: InterleavedTextMedia,
        sampling_params: Optional[SamplingParams] = SamplingParams(),
        stream: Optional[bool] = False,
        logprobs: Optional[LogProbConfig] = None,
    ) -> AsyncGenerator:
        raise NotImplementedError()

    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,
        )

        if stream:
            return self._stream_chat_completion(request)
        else:
            return self._nonstream_chat_completion(request)

    async def _nonstream_chat_completion(
        self, request: ChatCompletionRequest
    ) -> ChatCompletionResponse:
        params = self._get_params(request)
        r = await self.client.text_generation(**params)

        choice = OpenAICompatCompletionChoice(
            finish_reason=r.details.finish_reason,
            text="".join(t.text for t in r.details.tokens),
        )
        response = OpenAICompatCompletionResponse(
            choices=[choice],
        )
        return process_chat_completion_response(request, response, self.formatter)

    async def _stream_chat_completion(
        self, request: ChatCompletionRequest
    ) -> AsyncGenerator:
        params = self._get_params(request)

        async def _generate_and_convert_to_openai_compat():
            s = await self.client.text_generation(**params)
            async for chunk in s:
                token_result = chunk.token

                choice = OpenAICompatCompletionChoice(text=token_result.text)
                yield OpenAICompatCompletionResponse(
                    choices=[choice],
                )

        stream = _generate_and_convert_to_openai_compat()
        async for chunk in process_chat_completion_stream_response(
            request, stream, self.formatter
        ):
            yield chunk

    def _get_params(self, request: ChatCompletionRequest) -> dict:
        prompt, input_tokens = chat_completion_request_to_model_input_info(
            request, self.formatter
        )
        max_new_tokens = min(
            request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
            self.max_tokens - input_tokens - 1,
        )
        options = get_sampling_options(request)
        return dict(
            prompt=prompt,
            stream=request.stream,
            details=True,
            max_new_tokens=max_new_tokens,
            stop_sequences=["<|eom_id|>", "<|eot_id|>"],
            **options,
        )

    async def embeddings(
        self,
        model: str,
        contents: List[InterleavedTextMedia],
    ) -> EmbeddingsResponse:
        raise NotImplementedError()


class TGIAdapter(_HfAdapter):
    async def initialize(self, config: TGIImplConfig) -> None:
        self.client = AsyncInferenceClient(model=config.url, token=config.api_token)
        endpoint_info = await self.client.get_endpoint_info()
        self.max_tokens = endpoint_info["max_total_tokens"]
        self.model_id = endpoint_info["model_id"]


class InferenceAPIAdapter(_HfAdapter):
    async def initialize(self, config: InferenceAPIImplConfig) -> None:
        self.client = AsyncInferenceClient(
            model=config.huggingface_repo, token=config.api_token
        )
        endpoint_info = await self.client.get_endpoint_info()
        self.max_tokens = endpoint_info["max_total_tokens"]
        self.model_id = endpoint_info["model_id"]


class InferenceEndpointAdapter(_HfAdapter):
    async def initialize(self, config: InferenceEndpointImplConfig) -> None:
        # Get the inference endpoint details
        api = HfApi(token=config.api_token)
        endpoint = api.get_inference_endpoint(config.endpoint_name)

        # Wait for the endpoint to be ready (if not already)
        endpoint.wait(timeout=60)

        # Initialize the adapter
        self.client = endpoint.async_client
        self.model_id = endpoint.repository
        self.max_tokens = int(
            endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
        )