| import torch | |
| from typing import Dict, List, Any | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| # check for GPU | |
| device = 0 if torch.cuda.is_available() else -1 | |
| class EndpointHandler: | |
| def __init__(self, path=""): | |
| # load the model | |
| tokenizer = AutoTokenizer.from_pretrained(path) | |
| model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True) | |
| # create inference pipeline | |
| self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device) | |
| def __call__(self, data: Any) -> List[List[Dict[str, float]]]: | |
| inputs = data.pop("inputs", data) | |
| parameters = data.pop("parameters", None) | |
| # pass inputs with all kwargs in data | |
| if parameters is not None: | |
| prediction = self.pipeline(inputs, **parameters) | |
| else: | |
| prediction = self.pipeline(inputs) | |
| # postprocess the prediction | |
| return prediction | |