File size: 1,545 Bytes
eae6b01 d2f3e5e eae6b01 d2f3e5e eae6b01 | 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 42 43 44 45 46 47 48 49 50 51 52 | 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
format_input = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
)
class EndpointHandler:
def __init__(self, path=""):
# load the model
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(
path,
device_map="auto",
torch_dtype=torch.float16,
)
# create inference pipeline
self.pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=device,
max_length=256,
)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
text_input = format_input.format(instruction=inputs)
# pass inputs with all kwargs in data
if parameters is not None:
prediction = self.pipeline(text_input, **parameters)
else:
prediction = self.pipeline(text_input)
# postprocess the prediction
output = [
{"generated_text": pred["generated_text"].split("### Response:")[1].strip()}
for pred in prediction
]
return output |