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from typing import Dict, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
import torch.cuda
device = "cuda" if torch.cuda.is_available() else "cpu"
class EndpointHandler():
def __init__(self, path=""):
config = PeftConfig.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, device_map="auto")#, load_in_8bit=True, device_map='auto')
self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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.
"""
# 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
# if parameters is not None:
# output = self.model.generate(input_ids=input_ids, **parameters)
# else:
# output = self.model.generate(input_ids=input_ids)
output = self.model.generate(input_ids, temperature=0.9, max_new_tokens=50)
# Postprocess
prediction = self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
return {"generated_text": prediction}
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