Transformer的pytorch实现
Transformer架构
Transformer模型由encoder和decoder两部分组成,decoder输出的结果,经过一个线性层,然后计算softmax。
d_model = 512 # Embedding Size
d_ff = 2048 # FeedForward dimension
d_k = d_v = 64 # dimension of K(=Q), V
n_layers = 6 # number of Encoder of Decoder Layer
n_heads = 8 # number of heads in Multi-Head Attention
class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)
def forward(self, enc_inputs, dec_inputs):
enc_outputs, enc_self_attns = self.encoder(enc_inputs)
dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs, enc_outputs)
dec_logits = self.projection(dec_outputs) # dec_logits : [batch_size x src_vocab_size x tgt_vocab_size]
return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns
实例化一个Transformer模型,输入为enc_inputs[batch_size,src_len],dec_inputs[batch_size,tgt_len]
model = Transformer()
outputs, enc_self_attns, dec_self_attns, dec_enc_attns = model(enc_inputs, dec_inputs)
Encoder
Encoder分为embedding和6层Encoderlayer,首层的Encoderlayer输入为词embedding和位置embedding相加之和enc_outputs:[batch_size,seq_len,d_model],即(1,5,512)。
Attention_mask:[batch_size,seq_lenq,seq_len],即(1,5,5),其中one is masking。
用enc_self_attns列表保存每一层encoder的attention,列表长度为6。
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.src_emb = nn.Embedding(src_vocab_size, d_model)
self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(src_len+1, d_model),freeze=True)
self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
def forward(self, enc_inputs): # enc_inputs : [batch_size x source_len]
enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))
enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs)
enc_self_attns = []
for layer in self.layers:
enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask)
enc_self_attns.append(enc_self_attn)
return enc_outputs, enc_self_attns
6层Encoderlayer
每层Encoderlayer包括Multi-head attention和 position-wise Feed Forward,以及这两个子层之间有残差连接。
class EncoderLayer(nn.Module):
def __init__(self):
super(EncoderLayer, self).__init__()
self.enc_self_attn = MultiHeadAttention()
self.pos_ffn = PoswiseFeedForwardNet()
def forward(self, enc_inputs, enc_self_attn_mask):
enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs, enc_self_attn_mask) # enc_inputs to same Q,K,V
enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size x len_q x d_model]
return enc_outputs, attn
Multi-head attention子层
输入的Q,K,V均为enc_inputs:[batch_size,seq_len,d_model],即(1,5,512)
W_Q, W_K, W_V分别经过nn.liner(d_model,d_k*heads) , 虽然维数均为512,但经过线性变换后三者的值不再相等,q_s,k_s,v_s分别将W_Q, W_K, W_V变换为[batch_size,n_heads,seq_len,d_q/d_k/d_v]
Attn_mask加入head后变为[batch_size,n_heads,seq_len,seq_len],即(1,8,5,5)
class MultiHeadAttention(nn.Module):
def __init__(self):
super(MultiHeadAttention, self).__init__()
self.W_Q = nn.Linear(d_model, d_k * n_heads)
self.W_K = nn.Linear(d_model, d_k * n_heads)
self.W_V = nn.Linear(d_model, d_v * n_heads)
def forward(self, Q, K, V, attn_mask):
# q: [batch_size x len_q x d_model], k: [batch_size x len_k x d_model], v: [batch_size x len_k x d_model]
residual, batch_size = Q, Q.size(0)
# (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)
q_s = self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1,2) # q_s: [batch_size x n_heads x len_q x d_k]
k_s = self.W_K(K).view(batch_size, -1, n_heads, d_k).transpose(1,2) # k_s: [batch_size x n_heads x len_k x d_k]
v_s = self.W_V(V).view(batch_size, -1, n_heads, d_v).transpose(1,2) # v_s: [batch_size x n_heads x len_k x d_v]
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1) # attn_mask : [batch_size x n_heads x len_q x len_k]
# context: [batch_size x n_heads x len_q x d_v], attn: [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
context, attn = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)
context = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
output = nn.Linear(n_heads * d_v, d_model)(context)
return nn.LayerNorm(d_model)(output + residual), attn # output: [batch_size x len_q x d_model]
ScaledDotProductAttention:将q_s,k_s经过matmul;然后除以一个缩放因子;再进行sequence masking除去msaking部分的干扰,masking部分不需要给予attention;经过softmax后计算attention 权重;最后与v_s 进行matmul得到atttion后的输出context:[batch_size,n_heads,seq_len,d_v]和atten:[batch_size,n_heads,seq_len,seq_len]矩阵。
class ScaledDotProductAttention(nn.Module):
def __init__(self):
super(ScaledDotProductAttention, self).__init__()
def forward(self, Q, K, V, attn_mask):
scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size x n_heads x len_q(=len_k) x len_k(=len_q)]
scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.
attn = nn.Softmax(dim=-1)(scores)
context = torch.matmul(attn, V)
return context, attn
将 context经过nn.linear变换为[batch_size,seq_len,d_model],(1,5,512),加上原先的输入(残差),进行 Layer normalization,输出enc_outputs(1,5,512), attn(1,8,5,5)
ontext = context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v) # context: [batch_size x len_q x n_heads * d_v]
output = nn.Linear(n_heads * d_v, d_model)(context)
return nn.LayerNorm(d_model)(output + residual), attn # output: [batch_size x len_q x d_model]
Position-wise Feed-Forward network子层
这是一个全连接网络,包含两个线性变换和一个非线性函数(实际上就是ReLU)。公式如下:
FFN(x) = max(0,xW_{1}+b_{1})W_{2}+b_{2}
论文中提到,这个公式还可以用两个核大小为1的一维卷积来解释,卷积的输入输出都是d_{model}=512,中间层的维度是d_{ff}=2048。
最后经过残差连接和 Layer normalization
class PoswiseFeedForwardNet(nn.Module):
def __init__(self):
super(PoswiseFeedForwardNet, self).__init__()
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
def forward(self, inputs):
residual = inputs # inputs : [batch_size, len_q, d_model]
output = nn.ReLU()(self.conv1(inputs.transpose(1, 2)))
output = self.conv2(output).transpose(1, 2)
return nn.LayerNorm(d_model)(output + residual)
至此,第一层的Encoderlayer输出为enc_outputs(1,5,512),enc_self_attns.append(attn),其中attn为第一层的attention(1,8,5,5)
enc_outputs作为下一层的输入,经过6层Encoderlayer后,Encoder端的最后输出为:enc_outputs(1,5,512),enc_self_attns(6,1,8,5,5)
Decoder
Decoder的输入为dec_imputs:[batch_size,seq_len];enc_inputs:[batch_size,seq_len],非embedding,为词典index; enc_outputs:[batch_size,seq_len,d_model],为encoder端输出。
dec_outputs, dec_self_attns, dec_enc_attns = self.decoder(dec_inputs, enc_inputs,enc_outputs)
Decoder分为embedding、6层Decodelayer。
其中embedding与encoder相同,首层的Decoderlayer输入为词embedding和位置embedding相加之和enc_outputs:[batch_size,seq_len,d_model],即(1,5,512)
lass Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)
self.pos_emb = nn.Embedding.from_pretrained(get_sinusoid_encoding_table(tgt_len+1, d_model),freeze=True)
self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])
def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs : [batch_size x target_len]
dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))
dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs)
dec_self_attn_subsequent_mask = get_attn_subsequent_mask(dec_inputs)
dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask + dec_self_attn_subsequent_mask), 0)
dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs)
dec_self_attns, dec_enc_attns = [], []
for layer in self.layers:
dec_outputs, dec_self_attn, dec_enc_attn = layer(dec_outputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask)
dec_self_attns.append(dec_self_attn)
dec_enc_attns.append(dec_enc_attn)
return dec_outputs, dec_self_attns, dec_enc_attns
6层Decoderlayer
每层Decoderlayer包括Multi-head attention(self-attention和dec-enc-attention)、 position-wise Feed Forward,以及这两个子层之间有残差连接。
lass DecoderLayer(nn.Module):
def __init__(self):
super(DecoderLayer, self).__init__()
self.dec_self_attn = MultiHeadAttention()
self.dec_enc_attn = MultiHeadAttention()
self.pos_ffn = PoswiseFeedForwardNet()
def forward(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):
dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs, dec_inputs, dec_self_attn_mask)
dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)
dec_outputs = self.pos_ffn(dec_outputs)
return dec_outputs, dec_self_attn, dec_enc_attn
Multi-head attention子层
同Encoderlayer的Multi-head attention
Dec-enc attention子层
相比于Encoder多了dec-enc-aatention;且mask部分包括dec_self_attn_mask和Dec_enc_attn_mask(上1下0上三角矩阵)
Position-wise Feed-Forward network子层
同Encoderlayer的Position-wise Feed-Forward network
Decoder每一层输出为:
dec_outputs: [batch_size,seq_len,d_model] (1,5,512)
dec_self_attn:[batch_size,n_heads,seq_len,seq_len] (1,8,5,5)
dec_enc_attn:[batch_size,n_heads,seq_len,seq_len] (1,8,5,5)
经过6层Decoderlayer后的输出为:
dec_outputs:[batch_size,seq_len,d_model] (1,5,512)
dec_self_attn:[layers,batch_size,n_heads,seq_len,seq_len] (6,1,8,5,5)
dec_enc_attn:[layers,batch_size,n_heads,seq_len,seq_len] (6,1,8,5,5)
Linear
dec_logits = self.projection(dec_outputs)
self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False)
Dec_logits为[batch_size x src_vocab_size x tgt_vocab_size]
至此,transformer model的输出为:
Outputs:[batch_size,src_vocab_size,target_vocab_size] —-> [-1,target_vocab_size]
enc_self_attns (6,1,8,5,5)
dec_self_attn:[layers,batch_size,n_heads,seq_len,seq_len] (6,1,8,5,5)
dec_enc_attn:[layers,batch_size,n_heads,seq_len,seq_len] (6,1,8,5,5)
Softmax
计算loss
target_batch为[batch_size,seq_len] (1,5)
loss = criterion(outputs, target_batch.contiguous().view(-1))
criterion = nn.CrossEntropyLoss()
参考代码:link.
该博客详细介绍了Transformer模型的实现,包括Encoder和Decoder的结构,每个部分如Multi-head attention、Position-wise Feed-Forward network子层的功能,并阐述了残差连接和Layer normalization的作用。在PyTorch中,模型的输出经过线性层和softmax计算损失。
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