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import argparse | |
from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria | |
import torch | |
import os | |
import json | |
from tqdm import tqdm | |
import shortuuid | |
from cumo.conversation import default_conversation | |
from cumo.utils import disable_torch_init | |
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() | |
output_ids = model.generate( | |
input_ids, | |
do_sample=True, | |
use_cache=True, | |
temperature=0.7, | |
max_new_tokens=1024,) | |
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) | |