在大模型中，文本解码通常是指在自然语言处理（NLP）任务中使用的大型神经网络模型（如Transformer架构的模型）将编码后的文本数据转换回可读的原始文本的过程。这些模型在处理自然语言时，首先将输入文本（如一段话或一个句子）编码成高维空间中的向量表示，这些向量能够捕捉到文本的语义和上下文信息。

在编码过程中，模型通过多层神经网络将文本的每个字符、单词或标记（token）转换成对应的向量。这些向量随后在模型的解码阶段被处理，以生成或选择最合适的序列来表示原始文本的含义。例如，在机器翻译任务中，解码阶段会生成目标语言的文本；在文本摘要任务中，解码阶段会生成原文的摘要；在问答系统中，解码阶段会生成问题的答案。

 一、自回归语言模型：

1、根据前文预测下一个单词：

2、一个文本序列的概率分布可以分解为每个词基于其上文的条件概率的乘积 ：

• w_0：初始上下文单词序列
• T：时间步
• 当生存ESO标签时停止生成 

3、MindNLP/huggingface Transformers提供的文本生成方法：

 二、环境准备：

首先还是需要下载MindSpore，相关教程可以参考我昇思25天学习打卡营第1天|快速入门这篇博客，之后就需要使用pip命令在终端卸载mindvision和mindinsight包之后，下载mindnlp：

pip uninstall mindvision -y
pip uninstall mindinsight -ypip install mindnlp

相关依赖下载完成之后，就可以开始我们下面的实验了！

三、Greedy Search:

在每个时间步𝑡都简单地选择概率最高的词作为当前输出词:

wt = argmax_w P(w|w(1:t-1))

按照贪心搜索输出序列("The","nice","woman") 的条件概率为：0.5 x 0.4 = 0.2

缺点: 错过了隐藏在低概率词后面的高概率词，如：dog=0.5, has=0.9 

from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')# generate text until the output length (which includes the context length) reaches 50
greedy_output = model.generate(input_ids, max_length=50)print("Output:\n" + 100 * '-')
print(tokenizer.decode(greedy_output[0], skip_special_tokens=True))

四、Beam Search:

Beam search通过在每个时间步保留最可能的 num_beams 个词，并从中最终选择出概率最高的序列来降低丢失潜在的高概率序列的风险。如图以 num_beams=2 为例:

("The","dog","has") : 0.4 * 0.9 = 0.36

("The","nice","woman") : 0.5 * 0.4 = 0.20

优点：一定程度保留最优路径

缺点：1. 无法解决重复问题；2. 开放域生成效果差

from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')# activate beam search and early_stopping
beam_output = model.generate(input_ids, max_length=50, num_beams=5, early_stopping=True
)print("Output:\n" + 100 * '-')
print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
print(100 * '-')# set no_repeat_ngram_size to 2
beam_output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2, early_stopping=True
)print("Beam search with ngram, Output:\n" + 100 * '-')
print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
print(100 * '-')# set return_num_sequences > 1
beam_outputs = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True
)# now we have 3 output sequences
print("return_num_sequences, Output:\n" + 100 * '-')
for i, beam_output in enumerate(beam_outputs):print("{}: {}".format(i, tokenizer.decode(beam_output, skip_special_tokens=True)))
print(100 * '-')

 

缺点的具体表现：

重复性高，这个看我生成的例子就可以很清楚的看到，着几句话几乎一模一样，还有就是开放域的问题，可以看下图：

 

五、超参数：

由于普通的默认索引均存在着难以克服的问题，人们通常会使用各种超参数来减小索引缺陷的影响。

1、n_gram惩罚：

将出现过的候选词的概率设置为 0

设置no_repeat_ngram_size=2 ，任意 2-gram 不会出现两次

Notice: 实际文本生成需要重复出现

 2、Sample:

根据当前条件概率分布随机选择输出词w_t

优点：文本生成多样性高

缺点：生成文本不连续

import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')mindspore.set_seed(0)
# activate sampling and deactivate top_k by setting top_k sampling to 0
sample_output = model.generate(input_ids, do_sample=True, max_length=50, top_k=0
)print("Output:\n" + 100 * '-')
print(tokenizer.decode(sample_output[0], skip_special_tokens=True))

 

3、Temperature:

降低softmax 的temperature使 P(w∣w1:t−1​)分布更陡峭，以增加高概率单词的似然并降低低概率单词的似然。

 

import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')mindspore.set_seed(1234)
# activate sampling and deactivate top_k by setting top_k sampling to 0
sample_output = model.generate(input_ids, do_sample=True, max_length=50, top_k=0,temperature=0.7
)print("Output:\n" + 100 * '-')
print(tokenizer.decode(sample_output[0], skip_special_tokens=True))

 

4、Topk Sample:

选出概率最大的 K 个词，重新归一化，最后在归一化后的 K 个词中采样，确定就是：将采样池限制为固定大小 K 导致在分布比较尖锐的时候产生胡言乱语和在分布比较平坦的时候限制模型的创造力。

 

import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')mindspore.set_seed(0)
# activate sampling and deactivate top_k by setting top_k sampling to 0
sample_output = model.generate(input_ids, do_sample=True, max_length=50, top_k=50
)print("Output:\n" + 100 * '-')
print(tokenizer.decode(sample_output[0], skip_special_tokens=True))

5、Top_P Sample:

在累积概率超过概率 p 的最小单词集中进行采样，重新归一化,缺点就是：采样池可以根据下一个词的概率分布动态增加和减少。

import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')mindspore.set_seed(0)# deactivate top_k sampling and sample only from 92% most likely words
sample_output = model.generate(input_ids, do_sample=True, max_length=50, top_p=0.92, top_k=0
)print("Output:\n" + 100 * '-')
print(tokenizer.decode(sample_output[0], skip_special_tokens=True))

 6、Top_k_Top_p:

import mindspore
from mindnlp.transformers import GPT2Tokenizer, GPT2LMHeadModeltokenizer = GPT2Tokenizer.from_pretrained("iiBcai/gpt2", mirror='modelscope')# add the EOS token as PAD token to avoid warnings
model = GPT2LMHeadModel.from_pretrained("iiBcai/gpt2", pad_token_id=tokenizer.eos_token_id, mirror='modelscope')# encode context the generation is conditioned on
input_ids = tokenizer.encode('I enjoy walking with my cute dog', return_tensors='ms')mindspore.set_seed(0)
# set top_k = 50 and set top_p = 0.95 and num_return_sequences = 3
sample_outputs = model.generate(input_ids,do_sample=True,max_length=50,top_k=5,top_p=0.95,num_return_sequences=3
)print("Output:\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):print("{}: {}".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))