File size: 1,261 Bytes
37af75f f3d3769 37af75f f3d3769 37af75f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# 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
tokenizer = AutoTokenizer.from_pretrained(
path,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
path,
device_map="auto",
torch_dtype=dtype,
trust_remote_code=True,
revision="main"
)
# create inference pipeline
self.pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
trust_remote_code=True
)
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 |