transformers代码实现

Transformers是一种基于自注意力机制的神经网络模型，在自然语言处理领域有很多应用。以下是使用Pytorch实现Transformers模型的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.d_head = d_model // n_heads
        
        self.q_linear = nn.Linear(d_model, d_model)
        self.v_linear = nn.Linear(d_model, d_model)
        self.k_linear = nn.Linear(d_model, d_model)
        self.out_linear = nn.Linear(d_model, d_model)
        
    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        
        # perform linear operation and split into n_heads
        k = self.k_linear(k).view(bs, -1, self.n_heads, self.d_head)
        q = self.q_linear(q).view(bs, -1, self.n_heads, self.d_head)
        v = self.v_linear(v).view(bs, -1, self.n_heads, self.d_head)
        
        # transpose to get dimensions bs * n_heads * sl * d_model
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)
        
        # calculate attention using function we will define next
        scores = self.attention(q, k, v, self.d_head, mask, self.dropout)
        
        # concatenate heads and put through final linear layer
        concat = scores.transpose(1,2).contiguous().view(bs, -1, self.d_model)
        output = self.out_linear(concat)
        
        return output
    
    def attention(self, q, k, v, d_head, mask=None, dropout=None):
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_head)
        
        if mask is not None:
            mask = mask.unsqueeze(1)
            scores = scores.masked_fill(mask == 0, -1e9)
        
        scores = F.softmax(scores, dim=-1)
        
        if dropout is not None:
            scores = dropout(scores)
        
        output = torch.matmul(scores, v)
        return output

class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.linear_1 = nn.Linear(d_model, d_ff)
        self.linear_2 = nn.Linear(d_ff, d_model)
        
    def forward(self, x):
        x = self.linear_1(x)
        x = F.relu(x)
        x = self.linear_2(x)
        return x

class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout):
        super().__init__()
        self.self_attn = MultiHeadAttention(d_model, n_heads)
        self.pwff = PositionwiseFeedForward(d_model, d_ff)
        self.norm_1 = nn.LayerNorm(d_model)
        self.norm_2 = nn.LayerNorm(d_model)
        self.dropout_1 = nn.Dropout(dropout)
        self.dropout_2 = nn.Dropout(dropout)
        
    def forward(self, x, mask=None):
        x_norm = self.norm_1(x)
        x_attn = self.self_attn(x_norm, x_norm, x_norm, mask)
        x_res = x + self.dropout_1(x_attn)
        
        x_norm = self.norm_2(x_res)
        x_ff = self.pwff(x_norm)
        x_res = x_res + self.dropout_2(x_ff)
        
        return x_res

class Encoder(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout, n_layers):
        super().__init__()
        self.layers = nn.ModuleList([EncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)])
        self.norm = nn.LayerNorm(d_model)
        
    def forward(self, x, mask=None):
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)
    
class Transformer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout, n_layers):
        super().__init__()
        self.encoder = Encoder(d_model, n_heads, d_ff, dropout, n_layers)
        
    def forward(self, x, mask=None):
        x = self.encoder(x, mask)
        return x

这里实现了Transformer的核心组件：多头自注意力机制（MultiHeadAttention）、前馈网络（PositionwiseFeedForward）、编码器层（EncoderLayer）、编码器（Encoder）和Transformer模型（Transformer）。可以通过调用Transformer的forward方法来运行模型。