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import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import logging
# get dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler:
def __init__(self, path=""):
# load the model
self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
# create inference pipeline
self.pipeline = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer)
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)
logging.warn("---start---")
logging.warn(prediction)
logging.warn("---end---")
# ignoring parameters! Default to configs in generation_config.json.
messages = [{"role": "user", "content": data.pop("inputs", data)}]
response = self.model.chat(self.tokenizer, messages)
logging.warn("---start chat response---")
logging.warn(response)
logging.warn("---end chat response---")
return [[{response: 1.0}]] |