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transformer.py
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import numpy as np
import torch.nn as nn
from datasets import *
d_model = 512 # 字 Embedding 的维度
d_ff = 2048 # 前向传播隐藏层维度
d_k = d_v = 64 # K(=Q), V的维度
n_layers = 6 # 有多少个encoder和decoder
n_heads = 8 # Multi-Head Attention设置为8
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)
pos_table = np.array([
[pos / np.power(10000, 2 * i / d_model) for i in range(d_model)]
if pos != 0 else np.zeros(d_model) for pos in range(max_len)])
pos_table[1:, 0::2] = np.sin(pos_table[1:, 0::2]) # 字嵌入维度为偶数时
pos_table[1:, 1::2] = np.cos(pos_table[1:, 1::2]) # 字嵌入维度为奇数时
self.pos_table = torch.FloatTensor(pos_table).cuda() # enc_inputs: [seq_len, d_model]
def forward(self, enc_inputs): # enc_inputs: [batch_size, seq_len, d_model]
enc_inputs += self.pos_table[:enc_inputs.size(1), :]
return self.dropout(enc_inputs.cuda())
def get_attn_pad_mask(seq_q, seq_k): # seq_q: [batch_size, seq_len] ,seq_k: [batch_size, seq_len]
batch_size, len_q = seq_q.size()
batch_size, len_k = seq_k.size()
pad_attn_mask = seq_k.data.eq(0).unsqueeze(1) # 判断 输入那些含有P(=0),用1标记 ,[batch_size, 1, len_k]
return pad_attn_mask.expand(batch_size, len_q, len_k) # 扩展成多维度
def get_attn_subsequence_mask(seq): # seq: [batch_size, tgt_len]
attn_shape = [seq.size(0), seq.size(1), seq.size(1)]
subsequence_mask = np.triu(np.ones(attn_shape), k=1) # 生成上三角矩阵,[batch_size, tgt_len, tgt_len]
subsequence_mask = torch.from_numpy(subsequence_mask).byte() # [batch_size, tgt_len, tgt_len]
return subsequence_mask
class ScaledDotProductAttention(nn.Module):
def __init__(self):
super(ScaledDotProductAttention, self).__init__()
def forward(self, Q, K, V, attn_mask): # Q: [batch_size, n_heads, len_q, d_k]
# K: [batch_size, n_heads, len_k, d_k]
# V: [batch_size, n_heads, len_v(=len_k), d_v]
# attn_mask: [batch_size, n_heads, seq_len, seq_len]
scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k) # scores : [batch_size, n_heads, len_q, len_k]
scores.masked_fill_(attn_mask, -1e9) # 如果时停用词P就等于 0
attn = nn.Softmax(dim=-1)(scores)
context = torch.matmul(attn, V) # [batch_size, n_heads, len_q, d_v]
return context, attn
class MultiHeadAttention(nn.Module):
def __init__(self):
super(MultiHeadAttention, self).__init__()
self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=False)
self.W_K = nn.Linear(d_model, d_k * n_heads, bias=False)
self.W_V = nn.Linear(d_model, d_v * n_heads, bias=False)
self.fc = nn.Linear(n_heads * d_v, d_model, bias=False)
def forward(self, input_Q, input_K, input_V, attn_mask): # input_Q: [batch_size, len_q, d_model]
# input_K: [batch_size, len_k, d_model]
# input_V: [batch_size, len_v(=len_k), d_model]
# attn_mask: [batch_size, seq_len, seq_len]
residual, batch_size = input_Q, input_Q.size(0)
Q = self.W_Q(input_Q).view(batch_size, -1, n_heads, d_k).transpose(1, 2) # Q: [batch_size, n_heads, len_q, d_k]
K = self.W_K(input_K).view(batch_size, -1, n_heads, d_k).transpose(1, 2) # K: [batch_size, n_heads, len_k, d_k]
V = self.W_V(input_V).view(batch_size, -1, n_heads, d_v).transpose(1,
2) # V: [batch_size, n_heads, len_v(=len_k), d_v]
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1,
1) # attn_mask : [batch_size, n_heads, seq_len, seq_len]
context, attn = ScaledDotProductAttention()(Q, K, V, attn_mask) # context: [batch_size, n_heads, len_q, d_v]
# attn: [batch_size, n_heads, len_q, len_k]
context = context.transpose(1, 2).reshape(batch_size, -1,
n_heads * d_v) # context: [batch_size, len_q, n_heads * d_v]
output = self.fc(context) # [batch_size, len_q, d_model]
return nn.LayerNorm(d_model).cuda()(output + residual), attn
class PoswiseFeedForwardNet(nn.Module):
def __init__(self):
super(PoswiseFeedForwardNet, self).__init__()
self.fc = nn.Sequential(
nn.Linear(d_model, d_ff, bias=False),
nn.ReLU(),
nn.Linear(d_ff, d_model, bias=False))
def forward(self, inputs): # inputs: [batch_size, seq_len, d_model]
residual = inputs
output = self.fc(inputs)
return nn.LayerNorm(d_model).cuda()(output + residual) # [batch_size, seq_len, d_model]
class EncoderLayer(nn.Module):
def __init__(self):
super(EncoderLayer, self).__init__()
self.enc_self_attn = MultiHeadAttention() # 多头注意力机制
self.pos_ffn = PoswiseFeedForwardNet() # 前馈神经网络
def forward(self, enc_inputs, enc_self_attn_mask): # enc_inputs: [batch_size, src_len, d_model]
# 输入3个enc_inputs分别与W_q、W_k、W_v相乘得到Q、K、V # enc_self_attn_mask: [batch_size, src_len, src_len]
enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs,
# enc_outputs: [batch_size, src_len, d_model],
enc_self_attn_mask) # attn: [batch_size, n_heads, src_len, src_len]
enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]
return enc_outputs, attn
class EncoderLayer(nn.Module):
def __init__(self):
super(EncoderLayer, self).__init__()
self.enc_self_attn = MultiHeadAttention() # 多头注意力机制
self.pos_ffn = PoswiseFeedForwardNet() # 前馈神经网络
def forward(self, enc_inputs, enc_self_attn_mask): # enc_inputs: [batch_size, src_len, d_model]
# 输入3个enc_inputs分别与W_q、W_k、W_v相乘得到Q、K、V # enc_self_attn_mask: [batch_size, src_len, src_len]
enc_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs,
# enc_outputs: [batch_size, src_len, d_model],
enc_self_attn_mask) # attn: [batch_size, n_heads, src_len, src_len]
enc_outputs = self.pos_ffn(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]
return enc_outputs, attn
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.src_emb = nn.Embedding(src_vocab_size, d_model) # 把字转换字向量
self.pos_emb = PositionalEncoding(d_model) # 加入位置信息
self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
def forward(self, enc_inputs): # enc_inputs: [batch_size, src_len]
enc_outputs = self.src_emb(enc_inputs) # enc_outputs: [batch_size, src_len, d_model]
enc_outputs = self.pos_emb(enc_outputs) # enc_outputs: [batch_size, src_len, d_model]
enc_self_attn_mask = get_attn_pad_mask(enc_inputs, enc_inputs) # enc_self_attn_mask: [batch_size, src_len, src_len]
enc_self_attns = []
for layer in self.layers:
enc_outputs, enc_self_attn = layer(enc_outputs, enc_self_attn_mask) # enc_outputs : [batch_size, src_len, d_model],
# enc_self_attn : [batch_size, n_heads, src_len, src_len]
enc_self_attns.append(enc_self_attn)
return enc_outputs, enc_self_attns
class 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_inputs: [batch_size, tgt_len, d_model]
# enc_outputs: [batch_size, src_len, d_model]
# dec_self_attn_mask: [batch_size, tgt_len, tgt_len]
# dec_enc_attn_mask: [batch_size, tgt_len, src_len]
dec_outputs, dec_self_attn = self.dec_self_attn(dec_inputs, dec_inputs,
dec_inputs,
dec_self_attn_mask) # dec_outputs: [batch_size, tgt_len, d_model]
# dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len]
dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs,
enc_outputs,
dec_enc_attn_mask) # dec_outputs: [batch_size, tgt_len, d_model]
# dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]
dec_outputs = self.pos_ffn(dec_outputs) # dec_outputs: [batch_size, tgt_len, d_model]
return dec_outputs, dec_self_attn, dec_enc_attn
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.tgt_emb = nn.Embedding(tgt_vocab_size, d_model)
self.pos_emb = PositionalEncoding(d_model)
self.layers = nn.ModuleList([DecoderLayer() for _ in range(n_layers)])
def forward(self, dec_inputs, enc_inputs, enc_outputs): # dec_inputs: [batch_size, tgt_len]
# enc_intpus: [batch_size, src_len]
# enc_outputs: [batsh_size, src_len, d_model]
dec_outputs = self.tgt_emb(dec_inputs) # [batch_size, tgt_len, d_model]
dec_outputs = self.pos_emb(dec_outputs).cuda() # [batch_size, tgt_len, d_model]
dec_self_attn_pad_mask = get_attn_pad_mask(dec_inputs, dec_inputs).cuda() # [batch_size, tgt_len, tgt_len]
dec_self_attn_subsequence_mask = get_attn_subsequence_mask(dec_inputs).cuda() # [batch_size, tgt_len, tgt_len]
dec_self_attn_mask = torch.gt((dec_self_attn_pad_mask +
dec_self_attn_subsequence_mask), 0).cuda() # [batch_size, tgt_len, tgt_len]
dec_enc_attn_mask = get_attn_pad_mask(dec_inputs, enc_inputs) # [batc_size, tgt_len, src_len]
dec_self_attns, dec_enc_attns = [], []
for layer in self.layers: # dec_outputs: [batch_size, tgt_len, d_model]
# dec_self_attn: [batch_size, n_heads, tgt_len, tgt_len]
# dec_enc_attn: [batch_size, h_heads, tgt_len, src_len]
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
class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()
self.Encoder = Encoder().cuda()
self.Decoder = Decoder().cuda()
self.projection = nn.Linear(d_model, tgt_vocab_size, bias=False).cuda()
def forward(self, enc_inputs, dec_inputs): # enc_inputs: [batch_size, src_len]
# dec_inputs: [batch_size, tgt_len]
enc_outputs, enc_self_attns = self.Encoder(enc_inputs) # enc_outputs: [batch_size, src_len, d_model],
# enc_self_attns: [n_layers, batch_size, n_heads, src_len, src_len]
dec_outputs, dec_self_attns, dec_enc_attns = self.Decoder(
dec_inputs, enc_inputs, enc_outputs) # dec_outpus : [batch_size, tgt_len, d_model],
# dec_self_attns: [n_layers, batch_size, n_heads, tgt_len, tgt_len],
# dec_enc_attn : [n_layers, batch_size, tgt_len, src_len]
dec_logits = self.projection(dec_outputs) # dec_logits: [batch_size, tgt_len, tgt_vocab_size]
return dec_logits.view(-1, dec_logits.size(-1)), enc_self_attns, dec_self_attns, dec_enc_attns