86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
|
import argparse
|
||
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria
|
||
|
|
import torch
|
||
|
|
import os
|
||
|
|
import json
|
||
|
|
from tqdm import tqdm
|
||
|
|
import shortuuid
|
||
|
|
|
||
|
|
from llava.conversation import default_conversation
|
||
|
|
from llava.utils import disable_torch_init
|
||
|
|
|
||
|
|
|
||
|
|
# new stopping implementation
|
||
|
|
class KeywordsStoppingCriteria(StoppingCriteria):
|
||
|
|
def __init__(self, keywords, tokenizer, input_ids):
|
||
|
|
self.keywords = keywords
|
||
|
|
self.tokenizer = tokenizer
|
||
|
|
self.start_len = None
|
||
|
|
self.input_ids = input_ids
|
||
|
|
|
||
|
|
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
|
||
|
|
if self.start_len is None:
|
||
|
|
self.start_len = self.input_ids.shape[1]
|
||
|
|
else:
|
||
|
|
outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
|
||
|
|
for keyword in self.keywords:
|
||
|
|
if keyword in outputs:
|
||
|
|
return True
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
@torch.inference_mode()
|
||
|
|
def eval_model(model_name, questions_file, answers_file):
|
||
|
|
# Model
|
||
|
|
disable_torch_init()
|
||
|
|
model_name = os.path.expanduser(model_name)
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
|
||
|
|
model = AutoModelForCausalLM.from_pretrained(model_name,
|
||
|
|
torch_dtype=torch.float16).cuda()
|
||
|
|
|
||
|
|
|
||
|
|
ques_file = open(os.path.expanduser(questions_file), "r")
|
||
|
|
ans_file = open(os.path.expanduser(answers_file), "w")
|
||
|
|
for i, line in enumerate(tqdm(ques_file)):
|
||
|
|
idx = json.loads(line)["question_id"]
|
||
|
|
qs = json.loads(line)["text"]
|
||
|
|
cat = json.loads(line)["category"]
|
||
|
|
conv = default_conversation.copy()
|
||
|
|
conv.append_message(conv.roles[0], qs)
|
||
|
|
prompt = conv.get_prompt()
|
||
|
|
inputs = tokenizer([prompt])
|
||
|
|
input_ids = torch.as_tensor(inputs.input_ids).cuda()
|
||
|
|
stopping_criteria = KeywordsStoppingCriteria([conv.sep], tokenizer, input_ids)
|
||
|
|
output_ids = model.generate(
|
||
|
|
input_ids,
|
||
|
|
do_sample=True,
|
||
|
|
use_cache=True,
|
||
|
|
temperature=0.7,
|
||
|
|
max_new_tokens=1024,
|
||
|
|
stopping_criteria=[stopping_criteria])
|
||
|
|
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
|
||
|
|
try:
|
||
|
|
index = outputs.index(conv.sep, len(prompt))
|
||
|
|
except ValueError:
|
||
|
|
outputs += conv.sep
|
||
|
|
index = outputs.index(conv.sep, len(prompt))
|
||
|
|
|
||
|
|
outputs = outputs[len(prompt) + len(conv.roles[1]) + 2:index].strip()
|
||
|
|
ans_id = shortuuid.uuid()
|
||
|
|
ans_file.write(json.dumps({"question_id": idx,
|
||
|
|
"text": outputs,
|
||
|
|
"answer_id": ans_id,
|
||
|
|
"model_id": model_name,
|
||
|
|
"metadata": {}}) + "\n")
|
||
|
|
ans_file.flush()
|
||
|
|
ans_file.close()
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
|
||
|
|
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
|
||
|
|
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
eval_model(args.model_name, args.question_file, args.answers_file)
|