File size: 1,487 Bytes
414714e |
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 |
# This is the script that will be used in the inference container
import json
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def model_fn(model_dir):
"""
Load the model and tokenizer for inference
"""
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir).to(device).eval()
return {"model": model, "tokenizer": tokenizer}
def predict_fn(input_data, model_dict):
"""
Make a prediction with the model
"""
text = input_data.pop("inputs")
parameters_list = input_data.pop("parameters_list", None)
tokenizer = model_dict["tokenizer"]
model = model_dict["model"]
# Parameters may or may not be passed
input_ids = tokenizer(
text, truncation=True, padding="longest", return_tensors="pt"
).input_ids.to(device)
if parameters_list:
predictions = []
for parameters in parameters_list:
output = model.generate(input_ids, **parameters)
predictions.append(tokenizer.batch_decode(output, skip_special_tokens=True))
else:
output = model.generate(input_ids)
predictions = tokenizer.batch_decode(output, skip_special_tokens=True)
return predictions
def input_fn(request_body, request_content_type):
"""
Transform the input request to a dictionary
"""
return json.loads(request_body)
|