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() def top_k_top_p_filtering( logits, top_k=0, top_p=0.0, filter_value=-float('Inf') ): assert logits.dim() == 1 top_k = min( top_k, logits.size(-1) ) if top_k > 0: indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p > 0.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) 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] ) generated = [] for _ in range(100): output = model(input_ids) next_token_logits = output.logits[0, -1, :] next_token_logits[ tokenizer.convert_tokens_to_ids('[UNK]') ] = -float('Inf') filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=8, top_p=1) 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: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"), ).launch()