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from typing import Dict, Any
import logging
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
LOGGER = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
class EndpointHandler():
def __init__(self, path=""):
config = PeftConfig.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
self.model = PeftModel.from_pretrained(model, path)
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("prompt", data)
parameters = data.pop("parameters", None)
# Preprocess
input = self.tokenizer(prompt, return_tensors="pt")
# Forward
LOGGER.info(f"Start generation.")
if parameters is not None:
output = self.model.generate(**input, **parameters)
else:
output = self.model.generate(**input)
# Postprocess
prediction = self.tokenizer.decode(output[0])
LOGGER.info(f"Generated text: {prediction}")
return {"generated_text": prediction}
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