victory的博客

长安一片月，万户捣衣声

0%

Transformer | transformer代码实现

发表于 分类于 ， ， ，

  • Transformer

    Transformer是一种基于自注意力机制的深度学习模型，由Google在2017年的论文“Attention is All You Need”提出。Transformer由编码器（Encoder）和解码器（Decoder）组成，结构如下图所示：

  • Transformer Pytorch代码实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()
        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)
        self.register_buffer('pe', pe)

    def forward(self, x):
        return x + self.pe[:, :x.size(1)].detach()


class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        assert d_model % self.num_heads == 0  # 确保 d_model 能被 num_heads 整除

        self.depth = d_model // self.num_heads

        self.wq = nn.Linear(d_model, d_model)
        self.wk = nn.Linear(d_model, d_model)
        self.wv = nn.Linear(d_model, d_model)

        self.dense = nn.Linear(d_model, d_model)

    def split_heads(self, x, batch_size):
        x = x.view(batch_size, -1, self.num_heads, self.depth)
        return x.permute(0, 2, 1, 3)

    def attention(self, query, key, value, mask=None, dropout=None):
        matmul_qk = torch.matmul(query, key.transpose(-2, -1))  # QK^T
        dk = query.size(-1)
        scaled_attention_logits = matmul_qk / math.sqrt(dk)

        if mask is not None:
            scaled_attention_logits += (mask * -1e9)  # 避免pad部分被注意到

        attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
        if dropout is not None:
            attention_weights = dropout(attention_weights)

        output = torch.matmul(attention_weights, value)
        return output, attention_weights

    def forward(self, query, key, value, mask=None, dropout=None):
        batch_size = query.size(0)

        query = self.split_heads(self.wq(query), batch_size)
        key = self.split_heads(self.wk(key), batch_size)
        value = self.split_heads(self.wv(value), batch_size)

        output, attention_weights = self.attention(query, key, value, mask, dropout)
        output = output.permute(0, 2, 1, 3).contiguous().view(batch_size, -1, self.d_model)

        return self.dense(output)


class FeedForwardNetwork(nn.Module):
    def __init__(self, d_model, d_ff=2048, dropout=0.1):
        super(FeedForwardNetwork, self).__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = F.relu(self.linear1(x))
        x = self.dropout(x)
        return self.linear2(x)


class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff=2048, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.attention = MultiHeadAttention(d_model, num_heads)
        self.ffn = FeedForwardNetwork(d_model, d_ff, dropout)
        self.layernorm1 = nn.LayerNorm(d_model)
        self.layernorm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        # 自注意力层
        attn_output = self.attention(x, x, x, mask, self.dropout)
        x = self.layernorm1(x + attn_output)  # 残差连接 + LayerNorm

        # 前馈网络层
        ffn_output = self.ffn(x)
        x = self.layernorm2(x + ffn_output)  # 残差连接 + LayerNorm

        return x


class DecoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff=2048, dropout=0.1):
        super(DecoderLayer, self).__init__()
        self.attention1 = MultiHeadAttention(d_model, num_heads)
        self.attention2 = MultiHeadAttention(d_model, num_heads)
        self.ffn = FeedForwardNetwork(d_model, d_ff, dropout)
        self.layernorm1 = nn.LayerNorm(d_model)
        self.layernorm2 = nn.LayerNorm(d_model)
        self.layernorm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, enc_output, look_ahead_mask=None, padding_mask=None):
        # 解码器中的自注意力层
        attn1_output = self.attention1(x, x, x, look_ahead_mask, self.dropout)
        x = self.layernorm1(attn1_output + x)

        # 编码器-解码器注意力层
        attn2_output = self.attention2(x, enc_output, enc_output, padding_mask, self.dropout)
        x = self.layernorm2(attn2_output + x)

        # 前馈网络层
        ffn_output = self.ffn(x)
        x = self.layernorm3(ffn_output + x)

        return x


class TransformerEncoder(nn.Module):
    def __init__(self, vocab_size, d_model, num_heads, num_layers, d_ff=2048, dropout=0.1):
        super(TransformerEncoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model)
        self.layers = nn.ModuleList([
            EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)
        ])
        self.d_model = d_model

    def forward(self, x, mask=None):
        x = self.embedding(x) * math.sqrt(self.d_model)  # 嵌入 + 缩放
        x = self.positional_encoding(x)

        for layer in self.layers:
            x = layer(x, mask)

        return x


class TransformerDecoder(nn.Module):
    def __init__(self, vocab_size, d_model, num_heads, num_layers, d_ff=2048, dropout=0.1):
        super(TransformerDecoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model)
        self.layers = nn.ModuleList([
            DecoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)
        ])
        self.d_model = d_model

    def forward(self, x, enc_output, look_ahead_mask=None, padding_mask=None):
        x = self.embedding(x) * math.sqrt(self.d_model)
        x = self.positional_encoding(x)

        for layer in self.layers:
            x = layer(x, enc_output, look_ahead_mask, padding_mask)

        return x


class Transformer(nn.Module):
    def __init__(self, vocab_size, d_model, num_heads, num_layers, d_ff=2048, dropout=0.1):
        super(Transformer, self).__init__()

        self.encoder = TransformerEncoder(vocab_size, d_model, num_heads, num_layers, d_ff, dropout)
        self.decoder = TransformerDecoder(vocab_size, d_model, num_heads, num_layers, d_ff, dropout)
        self.output_layer = nn.Linear(d_model, vocab_size)

    def forward(self, src, tgt, src_mask=None, tgt_mask=None):
        # 编码器部分
        enc_output = self.encoder(src, src_mask)

        # 解码器部分
        dec_output = self.decoder(tgt, enc_output, tgt_mask, src_mask)

        # 输出层
        return self.output_layer(dec_output)


vocab_size = 10000  # 词汇表大小
d_model = 512  # 特征维度
num_heads = 8  # 注意力头数
num_layers = 6  # 编码器和解码器层数
dropout = 0.1  # Dropout 比例

# 初始化 Transformer 模型
transformer = Transformer(vocab_size, d_model, num_heads, num_layers, dropout=dropout)

# 输入张量（batch_size, sequence_length）
src = torch.randint(0, vocab_size, (32, 100))  # 假设 source 语言输入 batch_size=32，序列长度=100
tgt = torch.randint(0, vocab_size, (32, 100))  # 假设 target 语言输入 batch_size=32，序列长度=100

# 创建遮罩（假设没有 padding）
src_mask = None
tgt_mask = None

# 前向传播
output = transformer(src, tgt, src_mask, tgt_mask)

print("Output shape:", output.shape)  # 输出的形状 (batch_size, tgt_sequence_length, vocab_size)