67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
# coding=utf-8
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import torch
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from transformers.generation.logits_process import LogitsProcessor
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from transformers.models.llama.modeling_llama import LlamaForCausalLM
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from transformers import AutoTokenizer
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import re
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class FrequencyPenaltyLogitsProcessor(LogitsProcessor):
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def __init__(self, penalty: float, penalty_dialog: torch.LongTensor, input_length: int):
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if not isinstance(penalty, float) or not (penalty > 0):
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raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
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self.penalty = penalty
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self.input_length = input_length
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self.penalty_dialog = penalty_dialog
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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new_scores = []
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if self.penalty == 0.0:
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return scores
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for input_, score in zip(input_ids, scores):
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generated_tokens = torch.cat((self.penalty_dialog, input_[self.input_length:]), dim=-1)
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token_frequency = torch.bincount(generated_tokens, minlength=scores.size(-1)).to(scores.device)
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new_scores.append(score - self.penalty * token_frequency)
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return torch.stack(new_scores).float()
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class LlamaForConditionalGeneration(LlamaForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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def generate(self, **kwargs):
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history_penalty = kwargs.pop("history_penalty", 0.0)
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penalty_turns = kwargs.pop("penalty_turns", 0)
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messages = kwargs.pop("messages", [])
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if history_penalty != 0.0 and penalty_turns >= 0:
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input_ids = kwargs.get("input_ids", torch.tensor([[]]))
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input_length = input_ids.size(-1)
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dialogs = []
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for i in range(len(messages)):
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message = messages[i]
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if message['role'] == 'assistant':
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dialogs.append(message['content'])
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penalty_dialog = []
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for i in range(penalty_turns, 0, -1):
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if i <= len(dialogs):
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dialog = dialogs[-i].replace("("," ").replace(")"," ").replace("（"," ").replace("）"," ")
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penalty_dialog.append(dialog)
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model_id = "Collective-Ai/collective-v0.1-chinese-roleplay-8b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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penalty_token = torch.LongTensor(tokenizer.encode(' '.join(penalty_dialog))).to(input_ids.device)
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logits_processor = []
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logits_processor.append(FrequencyPenaltyLogitsProcessor(penalty=history_penalty, penalty_dialog=penalty_token, input_length=input_length))
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result = super().generate(logits_processor = logits_processor, **kwargs)
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else:
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result = super().generate(**kwargs)
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return result
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