5分钟NLP：Python文本生成的Beam Search解码

来源：Deephub Imba本文约800字，建议阅读5分钟本文介绍了Python文本生成的Beam Search的解码。

Pancakes looks so = log（0.2） + log（0.7）= -1.9Pancakes looks fluffy = log（0.2） + log（0.3）= -2.8

import torch.nn.functional as Fdef log_probability_single(logits, labels):  logp = F.log_softmax(logits, dim=-1)  logp_label = torch.gather(logp, 2, labels.unsqueeze(2)).squeeze(-1)  return logp_labeldef sentence_logprob(model, labels, input_len=0):  with torch.no_grad():      result = model(labels)      log_probability = log_probability_single(result.logits[:, :-1, :],                                                labels[:, 1:])  sentence_log_prob = torch.sum(log_probability[:, input_len:])  return sentence_log_prob.cpu().numpy()

input_sentence = "A love story, a mystery, a fantasy, a novel of self-discovery, a dystopia to rival George Orwell’s — 1Q84 is Haruki Murakami’s most ambitious undertaking yet: an instant best seller in his native Japan, and a tremendous feat of imagination from one of our most revered contemporary writers."
max_sequence = 100input_ids = tokenizer(input_sentence,                    return_tensors='pt')['input_ids'].to(device)output = model.generate(input_ids, max_length=max_sequence, do_sample=False)
greedy_search_output = sentence_logprob(model,                                      output,                                      input_len=len(input_ids[0]))print(tokenizer.decode(output[0]))

beam_search_output = model.generate(input_ids,                                  max_length=max_sequence,                                  num_beams=5,                                  do_sample=False,                                  no_repeat_ngram_size=2)beam_search_log_prob = sentence_logprob(model,                                      beam_search_output,                                      input_len=len(input_ids[0]))print(tokenizer.decode(beam_search_output[0]))print(f"\nlog_prob: {beam_search_log_prob:.2f}")