import re import logging import torch import torch.nn.functional as F from configs.hyperparametric import Generate_config,Reward_config from utils.warp import Warp from transformers.generation import GenerateDecoderOnlyOutput logger = logging.getLogger(__name__) assistant_from_template_in_response = Warp.assistant_from_template_in_response config = Generate_config().to_dict() reward_config = Reward_config().to_dict() def extra_span_from_tokens(tokens,sign='e'): start,end = -1,-1 for i in range(len(tokens)-3): token = ''.join(tokens[i:i+3]) if '<{x}>'.format(x=sign) in token: start = i break for i in range(len(tokens)-3): token = ''.join(tokens[i:i+3]) if ''.format(x=sign) in token: end = i+3 break if start < end and 0 <= start < len(tokens) and 0 < end < len(tokens): return start, end else: return -1, -1 def generate_response(inputs,model,tokenizer,logits_processor=None): ## max_length=2048, truncation=True, max_new_tokens=1024, temperature=0.7, do_sample=False data = tokenizer.encode_plus(inputs, max_length=config['max_length'], truncation=config['truncation'], return_tensors='pt') input_ids = data['input_ids'].to('cuda') attention_mask = data['attention_mask'].to('cuda') #bos_token,eos_token = tokenizer.bos_token, tokenizer.eos_token if logits_processor: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=config['do_sample'], max_new_tokens=config['max_new_tokens'], temperature=config['temperature'], output_hidden_states=config['output_hidden_states'], return_dict_in_generate=config['return_dict_in_generate'], output_logits=config['output_logits'], logits_processor=[logits_processor], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, ) else: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=config['do_sample'], max_new_tokens=config['max_new_tokens'], temperature=config['temperature'], output_hidden_states=config['output_hidden_states'], return_dict_in_generate=config['return_dict_in_generate'], output_logits=config['output_logits'], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, ) if isinstance(output,GenerateDecoderOnlyOutput): #ori_string = tokenizer.decode(output.sequences[0], skip_special_tokens=False) logits = output.logits #prob = [F.softmax(lgt,dim=-1) for lgt in output.logits] generated_ids = torch.stack([torch.argmax(logit, dim=-1) for logit in logits], dim=1) generated_tokens = [tokenizer.decode(t,skip_special_tokens=True) for t in generated_ids[0]] # extract probs of the target tokens start,end = extra_span_from_tokens(generated_tokens,'p') if start > 0: logits_thought = [logits[_] for _ in range(start,end)] else: start,end = extra_span_from_tokens(generated_tokens,'e') if start > 0: logits_thought = [logits[_] for _ in range(start,end)] else: logits_thought = [logit for logit in logits] prob_thought = [F.softmax(logit,dim=-1) for logit in logits_thought] prob_thought = [torch.amax(logit,dim=-1) for logit in prob_thought] prob_thought = torch.stack(prob_thought).mean(0) else: logger.info('GenerateDecoderOnlyOutput error') raise Exception return {'response':''.join(generated_tokens),'prob':prob_thought} def generate_string(inputs,model,tokenizer,logits_processor=None): ## max_length=2048, truncation=True, max_new_tokens=1024, temperature=0.7, do_sample=False data = tokenizer.encode_plus(inputs, max_length=config['max_length'], truncation=config['truncation'], return_tensors='pt') input_ids = data['input_ids'].to('cuda') attention_mask = data['attention_mask'].to('cuda') bos_token,eos_token = tokenizer.bos_token, tokenizer.eos_token if logits_processor: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=config['do_sample'], max_new_tokens=config['max_new_tokens'], temperature=config['temperature'], logits_processor=[logits_processor], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) else: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=config['do_sample'], max_new_tokens=config['max_new_tokens'], temperature=config['temperature'], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) ori_string = tokenizer.decode(output[0], skip_special_tokens=False) response = assistant_from_template_in_response(x=ori_string,bos=bos_token,eos=eos_token) return response def generate_score(inputs,model,tokenizer,reward_processor=None): ## max_length=2048, truncation=True, max_new_tokens=1024, temperature=0.7, do_sample=False data = tokenizer.encode_plus(inputs, max_length=config['max_length'], truncation=config['truncation'], return_tensors='pt') input_ids = data['input_ids'].to('cuda') attention_mask = data['attention_mask'].to('cuda') bos_token,eos_token = tokenizer.bos_token, tokenizer.eos_token if reward_processor: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=reward_config['do_sample'], max_new_tokens=reward_config['max_new_tokens'], temperature=reward_config['temperature'], logits_processor=[reward_processor], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) else: output = model.generate(input_ids, attention_mask=attention_mask, do_sample=reward_config['do_sample'], max_new_tokens=reward_config['max_new_tokens'], temperature=reward_config['temperature'], bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id) ori_string = tokenizer.decode(output[0], skip_special_tokens=False) #response = assistant_from_template_in_response(x=ori_string,bos=bos_token,eos=eos_token) response = ori_string return response