| import torch | |
| from typing import Any, Dict | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| # load model and tokenizer from path | |
| self.tokenizer = AutoTokenizer.from_pretrained(path) | |
| self.model = AutoModelForCausalLM.from_pretrained( | |
| path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True | |
| ) | |
| 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}] |