| from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel | |
| from transformers import GPT2TokenizerFast, GPT2Tokenizer | |
| from easyeditor import apply_grace_to_model, GraceHyperParams,nethook | |
| import torch | |
| def edit(prompt, target_new): | |
| request={"prompt":prompt,"target_new":target_new} | |
| hparams = GraceHyperParams.from_hparams("./hparams/GRACE/gpt2-xl.yaml") | |
| model = AutoModelForCausalLM.from_pretrained("./models/gpt2-xl") | |
| tok = GPT2Tokenizer.from_pretrained("./models/gpt2-xl") | |
| tok.pad_token_id = tok.eos_token_id | |
| global edit_model | |
| edit_model,_ = apply_grace_to_model(model,tok,request,hparams,keep_original_weight=True) | |
| return "finish" | |
| def generate(input_text): | |
| tok = GPT2Tokenizer.from_pretrained("./models/gpt2-xl") | |
| hparams = GraceHyperParams.from_hparams("./hparams/GRACE/gpt2-xl.yaml") | |
| tok.pad_token_id = tok.eos_token_id | |
| global edit_model | |
| input_ids = tok.encode(input_text, return_tensors='pt').to(f'cuda:{hparams.device}') | |
| edit_output = edit_model.generate(input_ids, max_length=30, pad_token_id=tok.eos_token_id) | |
| edit_reply = tok.decode(edit_output[0], skip_special_tokens=True) | |
| del edit_model | |
| torch.cuda.empty_cache() | |
| ori_model = AutoModelForCausalLM.from_pretrained("./models/gpt2-xl").to(f'cuda:{hparams.device}') | |
| ori_output = ori_model.generate(input_ids, max_length=30, pad_token_id=tok.eos_token_id) | |
| ori_reply = tok.decode(ori_output[0], skip_special_tokens=True) | |
| return ori_reply, edit_reply | |