nn.TransformerEncoderLayer

这个类是transformer encoder的组成部分，代表encoder的一个层，而encoder就是将transformerEncoderLayer重复几层。

Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
activation: the activation function of intermediate layer, relu or gelu (default=relu).

Examples::
encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
src = torch.rand(10, 32, 512)
out = encoder_layer(src)

需要注意的是transformer 只能输入 seqlenth x batch x dim 形式的数据。

nn.TransformerEncoder

这里是transformer的encoder部分，即将上述的encoder-layer作为参数输入初始话以后可以获得TransformerEncoder

Args
encoder_layer: an instance of the TransformerEncoderLayer() class (required).
num_layers: the number of sub-encoder-layers in the encoder (required).
norm: the layer normalization component (optional).

Examples::
encoder_layer = nn.TransformerEncoderLayer(d_model=512,nhead=8) transformer_encoder=nn.TransformerEncoder(encoder_layer,num_layers=6)
src = torch.rand(10, 32, 512)
out =transformer_encoder(src)

PostionEncoder

这里的数学原理就不再详细叙述了，因为我也没搞特别明白反正就是获得位置信息，与embedding加起来就行了。

class PositionalEncoding(nn.Module):def __init__(self, d_model, dropout=0.1, max_len=5000):super(PositionalEncoding, self).__init__()self.dropout = nn.Dropout(p=dropout)pe = torch.zeros(max_len, d_model)position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))pe[:, 0::2] = torch.sin(position * div_term)pe[:, 1::2] = torch.cos(position * div_term)pe = pe.unsqueeze(0).transpose(0, 1)self.register_buffer('pe', pe)def forward(self, x):x = x + self.pe[:x.size(0), :]return self.dropout(x)

TransformerModel

这里将参考pytorch tutorial中的内容

class First_TransformerModel(nn.Module):def __init__(self, ninp=300, nhead=4, nhid=128, nlayers=6, dropout=0.5):super(First_TransformerModel, self).__init__()from torch.nn import TransformerEncoder, TransformerEncoderLayerself.model_type = 'Transformer'self.src_mask = Noneself.pos_encoder = PositionalEncoding(ninp, dropout)encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)self.ninp = ninpdef _generate_square_subsequent_mask(self, src, lenths):'''padding_masksrc:max_lenth,num,300lenths:[lenth1,lenth2...]'''# mask num_of_sens x max_lenthmask = torch.ones(src.size(1), src.size(0)) == 1for i in range(len(lenths)):lenth = lenths[i]for j in range(lenth):mask[i][j] = Falsereturn maskdef forward(self, src, mask):'''src:num_of_all_sens,max_lenth,300'''self.src_mask = masksrc = src * math.sqrt(self.ninp)src = self.pos_encoder(src)output = self.transformer_encoder(src, src_key_padding_mask=self.src_mask)output = output[0,:,:]return outputclass PositionalEncoding(nn.Module):def __init__(self, d_model, dropout=0.1, max_len=5000):super(PositionalEncoding, self).__init__()self.dropout = nn.Dropout(p=dropout)pe = torch.zeros(max_len, d_model)position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))pe[:, 0::2] = torch.sin(position * div_term)pe[:, 1::2] = torch.cos(position * div_term)pe = pe.unsqueeze(0).transpose(0, 1)self.register_buffer('pe', pe)def forward(self, x):x = x + self.pe[:x.size(0), :]return self.dropout(x)

在这里我们只需将输入的src （seqlenth x batch x ninp）进行下面的操作即可，先乘上根号下的ninp，经过positionencoder，再经过encoder即可。

    src = src * math.sqrt(self.ninp)src = self.pos_encoder(src)output = self.transformer_encoder(src, src_key_padding_mask=self.src_mask)

这里还需要提一下mask

mask 是什么呢？
mask主要可以分为两种mask，一种是src_mask,一种是src_key_padding_mask, 这里我们主要解释src_key_padding_mask。
nn.Transformer中，提到了src_key_padding_mask的size，必须是 NxS ，即 batch x seqlenths
通过这个mask，就可以将padding的部分忽略掉，让attention注意力机制不再参与这一部分的运算。

需要注意的是，src_key_padding_mask 是一个二值化的tensor，在需要被忽略地方应该是True，在需要保留原值的情况下，是False

这里附上我定义的双层transformer代码
第一层

class First_TransformerModel(nn.Module):def __init__(self, ninp=300, nhead=4, nhid=128, nlayers=6, dropout=0.5):super(First_TransformerModel, self).__init__()from torch.nn import TransformerEncoder, TransformerEncoderLayerself.model_type = 'Transformer'self.src_mask = Noneself.pos_encoder = PositionalEncoding(ninp, dropout)encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)# self.encoder = nn.Embedding(ntoken, ninp)self.ninp = ninp# self.decoder = nn.Linear(ninp, ntoken)def _generate_square_subsequent_mask(self, src, lenths):'''padding_masksrc:max_lenth,num,300lenths:[lenth1,lenth2...]'''# mask num_of_sens x max_lenthmask = torch.ones(src.size(1), src.size(0)) == 1for i in range(len(lenths)):lenth = lenths[i]for j in range(lenth):mask[i][j] = False# mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)#mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))return maskdef forward(self, src, mask):'''src:num_of_all_sens,max_lenth,300'''self.src_mask = masksrc = src * math.sqrt(self.ninp)src = self.pos_encoder(src)output = self.transformer_encoder(src, src_key_padding_mask=self.src_mask)output = output[0,:,:]#output = self.decoder(output)return outputclass PositionalEncoding(nn.Module):def __init__(self, d_model, dropout=0.1, max_len=5000):super(PositionalEncoding, self).__init__()self.dropout = nn.Dropout(p=dropout)pe = torch.zeros(max_len, d_model)position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))pe[:, 0::2] = torch.sin(position * div_term)pe[:, 1::2] = torch.cos(position * div_term)pe = pe.unsqueeze(0).transpose(0, 1)self.register_buffer('pe', pe)def forward(self, x):x = x + self.pe[:x.size(0), :]return self.dropout(x)

第二层

#second levelclass Second_TransformerModel(nn.Module):def __init__(self, ninp=300, nhead=4, nhid=128, nlayers=6, dropout=0.5):super(Second_TransformerModel, self).__init__()from torch.nn import TransformerEncoder, TransformerEncoderLayerself.src_mask = Noneself.pos_encoder = PositionalEncoding(ninp, dropout)encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout)self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)self.ninp = ninpdef _generate_square_subsequent_mask(self, src, lenths):'''padding_masksrc:num_of_sentence x batch(文章数) x 300lenths:[lenth1,lenth2...]'''# mask num_of_sens x max_lenthmask = torch.ones(src.size(1), src.size(0)) == 1for i in range(len(lenths)):lenth = lenths[i]for j in range(lenth):mask[i][j] = Falsereturn maskdef forward(self, src, mask):'''src:max_sentence_num x batch(文章数) x 300'''self.src_mask = masksrc = src * math.sqrt(self.ninp)src = self.pos_encoder(src)output = self.transformer_encoder(src, src_key_padding_mask=self.src_mask)#output = self.decoder(output)return output

最终代码

class segmentmodel(nn.Module):def __init__(self, ninp=300, nhead=4, nhid=128, nlayers=6, dropout=0.5):super(segmentmodel, self).__init__()self.first_layer = First_TransformerModel(ninp,nhead,nhid,nlayers,dropout)self.second_layer = Second_TransformerModel(ninp,nhead,nhid,nlayers,dropout)self.linear = nn.Linear(ninp,2)  def pad(self, s, max_length):s_length = s.size()[0]v = torch.tensor(s.unsqueeze(0).unsqueeze(0))padded = F.pad(v, (0, 0, 0, max_length - s_length))  # (1, 1, max_length, 300)shape = padded.size()return padded.view(shape[2], 1, shape[3])  # (max_length, 1, 300)def pad_document(self, d, max_document_length):d_length = d.size()[0]v = d.unsqueeze(0).unsqueeze(0)padded = F.pad(v, (0, 0,0, max_document_length - d_length ))  # (1, 1, max_length, 300)shape = padded.size()return padded.view(shape[2], 1, shape[3])  # (max_length, 1, 300)def forward(self, batch):batch_size = len(batch)sentences_per_doc = []all_batch_sentences = []for document in batch:all_batch_sentences.extend(document)sentences_per_doc.append(len(document))lengths = [s.size()[0] for s in all_batch_sentences]max_length = max(lengths)#logger.debug('Num sentences: %s, max sentence length: %s',# sum(sentences_per_doc), max_length)padded_sentences = [self.pad(s, max_length) for s in all_batch_sentences]big_tensor = torch.cat(padded_sentences, 1)  # (max_length, batch size, 300)mask = self.first_layer._generate_square_subsequent_mask(big_tensor,lengths).cuda()firstlayer_out = self.first_layer(src = big_tensor,mask = mask)# 句子数 x 300#padded_output  batch x 300# 将各个文章中的句子分别取出来encoded_documents =[]index = 0for sentences_count in sentences_per_doc:end_index = index + sentences_countencoded_documents.append(firstlayer_out[index : end_index, :])index = end_index#docuemnt_paddingdoc_sizes = [doc.size()[0] for doc in encoded_documents]max_doc_size = np.max(doc_sizes)padded_docs = [self.pad_document(d, max_doc_size) for d in encoded_documents]docs_tensor = torch.cat(padded_docs, 1)#docs_tensor max_doc_size x batch x 300mask = self.second_layer._generate_square_subsequent_mask(docs_tensor,doc_sizes).cuda()second_layer_out = self.second_layer(src = docs_tensor,mask = mask)#去除最后一个句子doc_outputs = []for i, doc_len in enumerate(doc_sizes):doc_outputs.append(second_layer_out[0:doc_len - 1, i, :])  # -1 to remove last predicsentence_outputs = torch.cat(doc_outputs, 0)# 句子数 x 300out = self.linear(sentence_outputs)return out

值得注意的是，这里的第一层提取的句子信息，是采用的第一层的输出的一个向量来表示的，即从 seqlenth x N x 300 中选出 seqlenth维度的第一个作为句子表示，得到Nx300的tensor。

torch.nn.Transformer解读与应用相关推荐

【PyTorch】torch.nn.Transformer解读与应用
nn.TransformerEncoderLayer 这个类是transformer encoder的组成部分,代表encoder的一个层,而encoder就是将transformerEncoderL ...
torch.nn、(二)
参考 torch.nn.(二) - 云+社区 - 腾讯云目录 Recurrent layers RNN LSTM GRU RNNCell LSTMCell GRUCell Transformer l ...
Pytorch中 nn.Transformer的使用详解与Transformer的黑盒讲解
文章目录本文内容将Transformer看成黑盒 Transformer的推理过程 Transformer的训练过程 Pytorch中的nn.Transformer nn.Transformer简 ...
torch.nn.Embedding()中的padding_idx参数解读
torch.nn.Embedding() Word Embedding 词嵌入,就是把一个词典,随机初始化映射为一个向量矩阵. 列如:有一组词典,有两个词"hello"和" ...
torch.nn.functional.unfold 用法解读
torch.nn.Unfold(kernel_size, dilation=1, padding=0, stride=1) 参数 kernel_size,滑动窗口的大小 dilation,和空洞卷积中 ...
【torch.nn.init】初始化参数方法解读
文章目录 torch.nn.init 均匀分布正态分布常数分布全1分布全0分布对角分布 dirac 分布 xavier_uniform 分布 xavier_normal 分布 kaiming ...
PyTorch中的torch.nn.Parameter() 详解
PyTorch中的torch.nn.Parameter() 详解今天来聊一下PyTorch中的torch.nn.Parameter()这个函数,笔者第一次见的时候也是大概能理解函数的用途,但是具体实 ...
Pytorch入门实战（5）：基于nn.Transformer实现机器翻译（英译汉）
使用Google Colab运行(open In Colab) 源码地址文章目录本文涉及知识点本文内容环境配置数据预处理文本分词与构造词典 Dataset and Dataloader 模 ...
Transformer解读和实战
Transformer解读和实战背景循环神经网络(RNN),LSTM,GRU等循环结构的神经网络已经在NLP应用任务中取得了卓越的表现,循环模型通常沿输入和输出序列的符号位置考虑计算,产生对应位置 ...

torch.nn.Transformer解读与应用

nn.TransformerEncoderLayer

nn.TransformerEncoder

PostionEncoder

TransformerModel

torch.nn.Transformer解读与应用相关推荐

最新文章

热门文章