transformer模型构建_transfomer模型构建

作者：从前慢现在也慢 | 2024-07-17 07:14:30

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transfomer模型构建

2.6 模型构建

学习目标

掌握编码器-解码器结构的实现过程.
掌握Transformer模型的构建过程.

通过上面的小节, 我们已经完成了所有组成部分的实现, 接下来就来实现完整的编码器-解码器结构.

Transformer总体架构图:

编码器-解码器结构的代码实现


# 使用EncoderDecoder类来实现编码器-解码器结构
class EncoderDecoder(nn.Module):
    def __init__(self, encoder, decoder, source_embed, target_embed, generator):
        """初始化函数中有5个参数, 分别是编码器对象, 解码器对象, 
           源数据嵌入函数, 目标数据嵌入函数,  以及输出部分的类别生成器对象
        """
        super(EncoderDecoder, self).__init__()
        # 将参数传入到类中
        self.encoder = encoder
        self.decoder = decoder
        self.src_embed = source_embed
        self.tgt_embed = target_embed
        self.generator = generator
 
    def forward(self, source, target, source_mask, target_mask):
        """在forward函数中，有四个参数, source代表源数据, target代表目标数据, 
           source_mask和target_mask代表对应的掩码张量"""
 
        # 在函数中, 将source, source_mask传入编码函数, 得到结果后,
        # 与source_mask，target，和target_mask一同传给解码函数.
        return self.decode(self.encode(source, source_mask), source_mask,
                            target, target_mask)
 
    def encode(self, source, source_mask):
        """编码函数, 以source和source_mask为参数"""
        # 使用src_embed对source做处理, 然后和source_mask一起传给self.encoder
        return self.encoder(self.src_embed(source), source_mask)
 
    def decode(self, memory, source_mask, target, target_mask):
        """解码函数, 以memory即编码器的输出, source_mask, target, target_mask为参数"""
        # 使用tgt_embed对target做处理, 然后和source_mask, target_mask, memory一起传给self.decoder
        return self.decoder(self.tgt_embed(target), memory, source_mask, target_mask)

实例化参数


vocab_size = 1000
d_model = 512
encoder = en
decoder = de
source_embed = nn.Embedding(vocab_size, d_model)
target_embed = nn.Embedding(vocab_size, d_model)
generator = gen

输入参数:


# 假设源数据与目标数据相同, 实际中并不相同
source = target = Variable(torch.LongTensor([[100, 2, 421, 508], [491, 998, 1, 221]]))
 
# 假设src_mask与tgt_mask相同，实际中并不相同
source_mask = target_mask = Variable(torch.zeros(8, 4, 4))

调用:


ed = EncoderDecoder(encoder, decoder, source_embed, target_embed, generator)
ed_result = ed(source, target, source_mask, target_mask)
print(ed_result)
print(ed_result.shape)

输出效果:


tensor([[[ 0.2102, -0.0826, -0.0550,  ...,  1.5555,  1.3025, -0.6296],
         [ 0.8270, -0.5372, -0.9559,  ...,  0.3665,  0.4338, -0.7505],
         [ 0.4956, -0.5133, -0.9323,  ...,  1.0773,  1.1913, -0.6240],
         [ 0.5770, -0.6258, -0.4833,  ...,  0.1171,  1.0069, -1.9030]],
 
        [[-0.4355, -1.7115, -1.5685,  ..., -0.6941, -0.1878, -0.1137],
         [-0.8867, -1.2207, -1.4151,  ..., -0.9618,  0.1722, -0.9562],
         [-0.0946, -0.9012, -1.6388,  ..., -0.2604, -0.3357, -0.6436],
         [-1.1204, -1.4481, -1.5888,  ..., -0.8816, -0.6497,  0.0606]]],
       grad_fn=<AddBackward0>)
torch.Size([2, 4, 512])

接着将基于以上结构构建用于训练的模型.

Tansformer模型构建过程的代码分析


def make_model(source_vocab, target_vocab, N=6, 
               d_model=512, d_ff=2048, head=8, dropout=0.1):
    """该函数用来构建模型, 有7个参数，分别是源数据特征(词汇)总数，目标数据特征(词汇)总数，
       编码器和解码器堆叠数，词向量映射维度，前馈全连接网络中变换矩阵的维度，
       多头注意力结构中的多头数，以及置零比率dropout."""
 
    # 首先得到一个深度拷贝命令，接下来很多结构都需要进行深度拷贝，
    # 来保证他们彼此之间相互独立，不受干扰.
    c = copy.deepcopy
 
    # 实例化了多头注意力类，得到对象attn
    attn = MultiHeadedAttention(head, d_model)
 
    # 然后实例化前馈全连接类，得到对象ff 
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
 
    # 实例化位置编码类，得到对象position
    position = PositionalEncoding(d_model, dropout)
 
    # 根据结构图, 最外层是EncoderDecoder，在EncoderDecoder中，
    # 分别是编码器层，解码器层，源数据Embedding层和位置编码组成的有序结构，
    # 目标数据Embedding层和位置编码组成的有序结构，以及类别生成器层. 
    # 在编码器层中有attention子层以及前馈全连接子层，
    # 在解码器层中有两个attention子层以及前馈全连接层.
    model = EncoderDecoder(
        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
        Decoder(DecoderLayer(d_model, c(attn), c(attn), 
                             c(ff), dropout), N),
        nn.Sequential(Embeddings(d_model, source_vocab), c(position)),
        nn.Sequential(Embeddings(d_model, target_vocab), c(position)),
        Generator(d_model, target_vocab))
 
    # 模型结构完成后，接下来就是初始化模型中的参数，比如线性层中的变换矩阵
    # 这里一但判断参数的维度大于1，则会将其初始化成一个服从均匀分布的矩阵，
    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform(p)
    return model

nn.init.xavier_uniform演示:


# 结果服从均匀分布U(-a, a)
>>> w = torch.empty(3, 5)
>>> w = nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu'))
>>> w
tensor([[-0.7742,  0.5413,  0.5478, -0.4806, -0.2555],
        [-0.8358,  0.4673,  0.3012,  0.3882, -0.6375],
        [ 0.4622, -0.0794,  0.1851,  0.8462, -0.3591]])

输入参数:


source_vocab = 11
target_vocab = 11 
N = 6
# 其他参数都使用默认值

调用:


if __name__ == '__main__':
    res = make_model(source_vocab, target_vocab, N)
    print(res)

输出效果:


# 根据Transformer结构图构建的最终模型结构
EncoderDecoder(
  (encoder): Encoder(
    (layers): ModuleList(
      (0): EncoderLayer(
        (self_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (feed_forward): PositionwiseFeedForward(
          (w_1): Linear(in_features=512, out_features=2048)
          (w_2): Linear(in_features=2048, out_features=512)
          (dropout): Dropout(p=0.1)
        )
        (sublayer): ModuleList(
          (0): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (1): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
        )
      )
      (1): EncoderLayer(
        (self_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (feed_forward): PositionwiseFeedForward(
          (w_1): Linear(in_features=512, out_features=2048)
          (w_2): Linear(in_features=2048, out_features=512)
          (dropout): Dropout(p=0.1)
        )
        (sublayer): ModuleList(
          (0): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (1): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
        )
      )
    )
    (norm): LayerNorm(
    )
  )
  (decoder): Decoder(
    (layers): ModuleList(
      (0): DecoderLayer(
        (self_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (src_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (feed_forward): PositionwiseFeedForward(
          (w_1): Linear(in_features=512, out_features=2048)
          (w_2): Linear(in_features=2048, out_features=512)
          (dropout): Dropout(p=0.1)
        )
        (sublayer): ModuleList(
          (0): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (1): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (2): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
        )
      )
      (1): DecoderLayer(
        (self_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (src_attn): MultiHeadedAttention(
          (linears): ModuleList(
            (0): Linear(in_features=512, out_features=512)
            (1): Linear(in_features=512, out_features=512)
            (2): Linear(in_features=512, out_features=512)
            (3): Linear(in_features=512, out_features=512)
          )
          (dropout): Dropout(p=0.1)
        )
        (feed_forward): PositionwiseFeedForward(
          (w_1): Linear(in_features=512, out_features=2048)
          (w_2): Linear(in_features=2048, out_features=512)
          (dropout): Dropout(p=0.1)
        )
        (sublayer): ModuleList(
          (0): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (1): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
          (2): SublayerConnection(
            (norm): LayerNorm(
            )
            (dropout): Dropout(p=0.1)
          )
        )
      )
    )
    (norm): LayerNorm(
    )
  )
  (src_embed): Sequential(
    (0): Embeddings(
      (lut): Embedding(11, 512)
    )
    (1): PositionalEncoding(
      (dropout): Dropout(p=0.1)
    )
  )
  (tgt_embed): Sequential(
    (0): Embeddings(
      (lut): Embedding(11, 512)
    )
    (1): PositionalEncoding(
      (dropout): Dropout(p=0.1)
    )
  )
  (generator): Generator(
    (proj): Linear(in_features=512, out_features=11)
  )
)

小节总结

学习并实现了编码器-解码器结构的类: EncoderDecoder
- 类的初始化函数传入5个参数, 分别是编码器对象, 解码器对象, 源数据嵌入函数, 目标数据嵌入函数, 以及输出部分的类别生成器对象.
- 类中共实现三个函数, forward, encode, decode
- forward是主要逻辑函数, 有四个参数, source代表源数据, target代表目标数据, source_mask和target_mask代表对应的掩码张量.
- encode是编码函数, 以source和source_mask为参数.
- decode是解码函数, 以memory即编码器的输出, source_mask, target, target_mask为参数
学习并实现了模型构建函数: make_model
- 有7个参数，分别是源数据特征(词汇)总数，目标数据特征(词汇)总数，编码器和解码器堆叠数，词向量映射维度，前馈全连接网络中变换矩阵的维度，多头注意力结构中的多头数，以及置零比率dropout.
- 该函数最后返回一个构建好的模型对象.

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