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from typing import Any, Dict
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.tokenizer = AutoTokenizer.from_pretrained(path)
try:
config = PeftConfig.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
load_in_8bit=True,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True,
)
model.resize_token_embeddings(len(self.tokenizer))
model = PeftModel.from_pretrained(model, path)
except Exception:
model = AutoModelForCausalLM.from_pretrained(
path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True
)
self.model = model
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
# preprocess
inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(**inputs, **parameters)
else:
outputs = self.model.generate(**inputs)
# postprocess the prediction
prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": prediction}]
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