handler.py

from typing import Dict, Any
import logging

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
import torch.cuda


LOGGER = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
device = "cuda" if torch.cuda.is_available() else "cpu"


class EndpointHandler():
    def __init__(self, path=""):
        config = PeftConfig.from_pretrained("jscore2023/falcon-7b-3")
        model = AutoModelForCausalLM.from_pretrained("vilsonrodrigues/falcon-7b-instruct-sharded", device_map={"":0}, trust_remote_code=True, torch_dtype=torch.float16)
        self.tokenizer = AutoTokenizer.from_pretrained("jscore2023/falcon-7b-3")
        # Load the Lora model
        self.model = PeftModel.from_pretrained(model, "jscore2023/falcon-7b-3")

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Args:
            data (Dict): The payload with the text prompt and generation parameters.
        """
        LOGGER.info(f"Received data: {data}")
        # Get inputs
        prompt = data.pop("inputs", None)
        parameters = data.pop("parameters", None)
        if prompt is None:
            raise ValueError("Missing prompt.")
        # Preprocess
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
        # Forward
        LOGGER.info(f"Start generation.")
        if parameters is not None:
            output = self.model.generate(input_ids=input_ids, **parameters)
        else:
            output = self.model.generate(input_ids=input_ids)
        # Postprocess
        prediction = self.tokenizer.decode(output[0])
        LOGGER.info(f"Generated text: {prediction}")
        return {"generated_text": prediction}