| 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 |
|
|
|
|
| @torch.inference_mode() |
| def eval_model(model_name, questions_file, answers_file): |
| |
| 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) |
|
|
