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