Engine control in Python

When you create a new Truss with truss init, it creates two files: config.yaml and model/model.py. While you configure the Engine Builder in config.yaml, you may use model/model.py to access and control the engine object during inference.

You have two options:

1. Delete the model/model.py file and your TensorRT-LLM engine will run according to its base spec.
2. Update the code to support TensorRT-LLM.

The engine object is a property of the trt_llm argument and must be initialized in __init__ to be accessed in load() (which runs once on server start-up) and predict() (which runs for each request handled by the server).

This example applies a chat template with the Llama 3.1 8B tokenizer to the model prompt:

from typing import Any
from transformers import AutoTokenizer

class Model:
    def __init__(self, trt_llm, **kwargs) -> None:
        self._secrets = kwargs["secrets"]
        self._engine = trt_llm["engine"]
        self._model = None
        self._tokenizer = None

    def load(self) -> None:
        self._tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct", token=self._secrets["hf_access_token"])

    async def predict(self, model_input: Any) -> Any:
        # Apply chat template to prompt
        model_input["prompt"] = self._tokenizer.apply_chat_template(model_input["prompt"], tokenize=False)
        return await self._engine.predict(model_input)