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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 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 | |
| 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) | |