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| import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import math
import copy
# 多头注意力
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.d_k = d_model // num_heads
self.W_q = nn.Linear(d_model, d_model)
self.W_k = nn.Linear(d_model, d_model)
self.W_v = nn.Linear(d_model, d_model)
self.W_o = nn.Linear(d_model, d_model)
def scaled_dot_product_attention(self, Q, K, V, mask=None):
attn_scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.d_k)
if mask is not None:
attn_scores = attn_scores.masked_fill(mask == 0, -1e9)
attn_probs = torch.softmax(attn_scores, dim=-1)
output = torch.matmul(attn_probs, V)
return output
def split_heads(self, x):
batch_size, seq_length, d_model = x.size()
return x.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
def combine_heads(self, x):
batch_size, _, seq_length, d_k = x.size()
return x.transpose(1, 2).contiguous().view(batch_size, seq_length, self.d_model)
def forward(self, Q, K, V, mask=None):
Q = self.split_heads(self.W_q(Q))
K = self.split_heads(self.W_k(K))
V = self.split_heads(self.W_v(V))
attn_output = self.scaled_dot_product_attention(Q, K, V, mask)
output = self.W_o(self.combine_heads(attn_output))
return output
# 位置前馈网络
class PositionWiseFeedForward(nn.Module):
def __init__(self, d_model, d_ff):
super(PositionWiseFeedForward, self).__init__()
self.fc1 = nn.Linear(d_model, d_ff)
self.fc2 = nn.Linear(d_ff, d_model)
self.relu = nn.ReLU()
def forward(self, x):
return self.fc2(self.relu(self.fc1(x)))
# 位置编码
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_seq_length):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_seq_length, d_model)
position = torch.arange(0, max_seq_length, 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)
self.register_buffer('pe', pe.unsqueeze(0))
def forward(self, x):
return x + self.pe[:, :x.size(1)]
# 编码器
class EncoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask):
attn_output = self.self_attn(x, x, x, mask)
x = self.norm1(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm2(x + self.dropout(ff_output))
return x
# 解码器
class DecoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout):
super(DecoderLayer, self).__init__()
self.self_attn = MultiHeadAttention(d_model, num_heads)
self.cross_attn = MultiHeadAttention(d_model, num_heads)
self.feed_forward = PositionWiseFeedForward(d_model, d_ff)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask, tgt_mask):
attn_output = self.self_attn(x, x, x, tgt_mask)
x = self.norm1(x + self.dropout(attn_output))
attn_output = self.cross_attn(x, enc_output, enc_output, src_mask)
x = self.norm2(x + self.dropout(attn_output))
ff_output = self.feed_forward(x)
x = self.norm3(x + self.dropout(ff_output))
return x
class Transformer(nn.Module):
def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length, dropout):
super(Transformer, self).__init__()
self.encoder_embedding = nn.Embedding(src_vocab_size, d_model)
self.decoder_embedding = nn.Embedding(tgt_vocab_size, d_model)
self.positional_encoding = PositionalEncoding(d_model, max_seq_length)
self.encoder_layers = nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
self.decoder_layers = nn.ModuleList([DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
self.fc = nn.Linear(d_model, tgt_vocab_size)
self.dropout = nn.Dropout(dropout)
def generate_mask(self, src, tgt):
src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
tgt_mask = (tgt != 0).unsqueeze(1).unsqueeze(3)
seq_length = tgt.size(1)
nopeak_mask = (1 - torch.triu(torch.ones(1, seq_length, seq_length), diagonal=1)).bool()
tgt_mask = tgt_mask & nopeak_mask
return src_mask, tgt_mask
def forward(self, src, tgt):
src_mask, tgt_mask = self.generate_mask(src, tgt)
src_embedded = self.dropout(self.positional_encoding(self.encoder_embedding(src)))
tgt_embedded = self.dropout(self.positional_encoding(self.decoder_embedding(tgt)))
enc_output = src_embedded
for enc_layer in self.encoder_layers:
enc_output = enc_layer(enc_output, src_mask)
dec_output = tgt_embedded
for dec_layer in self.decoder_layers:
dec_output = dec_layer(dec_output, enc_output, src_mask, tgt_mask)
output = self.fc(dec_output)
return output
if __name__ == '__main__':
src_vocab_size = 5000
tgt_vocab_size = 5000
d_model = 512
num_heads = 8
num_layers = 6
d_ff = 2048
max_seq_length = 100
dropout = 0.1
transformer = Transformer(src_vocab_size, tgt_vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_length, dropout)
# 生成随机样本数据
src_data = torch.randint(1, src_vocab_size, (64, max_seq_length))
tgt_data = torch.randint(1, tgt_vocab_size, (64, max_seq_length))
criterion = nn.CrossEntropyLoss(ignore_index=0)
optimizer = optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)
transformer.train()
for epoch in range(100):
optimizer.zero_grad()
output = transformer(src_data, tgt_data[:, :-1])
loss = criterion(output.contiguous().view(-1, tgt_vocab_size), tgt_data[:, 1:].contiguous().view(-1))
loss.backward()
optimizer.step()
print(f"Epoch: {epoch+1}, Loss: {loss.item()}")
|
