import torch import gradio as gr import torch.nn.functional as F from transformers import BertTokenizer, GPT2LMHeadModel tokenizer = BertTokenizer.from_pretrained("supermy/couplet-gpt2") model = GPT2LMHeadModel.from_pretrained("supermy/couplet-gpt2") model.eval() # top_k或top_p解码策略,仅保留top_k个或累积概率到达top_p的标记,其他标记设为filter_value,后续在选取标记的过程中会取不到值设为无穷小。 # 从模型输出的logit里面,划分出概率最高的几个 def top_k_top_p_filtering( logits, top_k=0, top_p=0.0, filter_value=-float('Inf') ): #确保输出logit维度为1行若干列的矩阵,便于处理 assert logits.dim() == 1 #将top_k的值初始化为logit元素个数和设定的top_k之间的最小值 top_k = min( top_k, logits.size(-1) ) if top_k > 0: # 去除掉概率值小于top_k里最后一个token概率的后续token # torch.topk()返回最后一维最大的top_k个元素,返回值为二维(values,indices) # ...表示其他维度由计算机自行推断 indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value # 对于topk之外的其他元素的logits值设为负无穷 if top_p > 0.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) # 对logits进行递减排序 cumulative_probs = torch.cumsum( F.softmax(sorted_logits, dim=-1), dim=-1 ) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[indices_to_remove] = filter_value return logits def generate(input_text): input_ids = [tokenizer.cls_token_id] input_ids.extend( tokenizer.encode(input_text + "-", add_special_tokens=False) ) input_ids = torch.tensor( [input_ids] ) # 最多生成max_len个token generated = [] for _ in range(100): output = model(input_ids) # 对于已生成的结果generated中的每个token添加一个重复惩罚项,降低其生成概率 next_token_logits = output.logits[0, -1, :] # 对于[UNK]的概率设为无穷小,也就是说模型的预测结果不可能是[UNK]这个token next_token_logits[ tokenizer.convert_tokens_to_ids('[UNK]') ] = -float('Inf') # 使用top_k_top_p_filtering函数,按照top_k和top_p的值,对预测结果进行筛选 filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=8, top_p=1) # torch.multinomial表示从候选集合中无放回地进行抽取num_samples个元素,权重越高,抽到的几率越高,返回元素的下标 next_token = torch.multinomial( F.softmax(filtered_logits, dim=-1), num_samples=1 ) if next_token == tokenizer.sep_token_id: break generated.append( next_token.item() ) input_ids = torch.cat( (input_ids, next_token.unsqueeze(0)), dim=1 ) return "".join( tokenizer.convert_ids_to_tokens(generated) ) if __name__ == "__main__": gr.Interface( fn=generate, inputs=gr.Textbox(lines=1, placeholder="请在此输入上联【居末尾带句号。】:燕子归来,问昔日雕梁何处。",value="燕子归来,问昔日雕梁何处。",label="上联"), outputs=gr.Textbox(lines=1, placeholder="此处显示生成的下联",label="下联"), gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=supermy/couplet/)"), ).launch()