手把手教你用python代码transformer

好的，我可以为您提供一些关于使用Python代码实现Transformer的指导。

首先，您需要安装PyTorch和torchtext库。然后，您可以按照以下步骤实现Transformer模型：

1. 定义输入和输出的词汇表，并使用torchtext库加载数据集。
2. 定义Transformer模型的编码器和解码器部分。
3. 实现自注意力机制（self-attention）和前向神经网络（feed-forward network）。
4. 实现残差连接（residual connection）和层归一化（layer normalization）。
5. 定义Transformer模型的训练和评估过程。

下面是一个简单的示例代码，用于实现一个基本的Transformer模型：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.datasets import TranslationDataset, Multi30k
from torchtext.data import Field, BucketIterator

# 定义输入和输出的词汇表
SRC = Field(tokenize='spacy', tokenizer_language='de', init_token='<sos>', eos_token='<eos>', lower=True)
TRG = Field(tokenize='spacy', tokenizer_language='en', init_token='<sos>', eos_token='<eos>', lower=True)

# 加载数据集
train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TRG))
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)

# 定义Transformer模型的编码器和解码器部分
class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.device = device
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])
        self.dropout = nn.Dropout(dropout)
        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, src_mask):
        # src: [batch_size, src_len]
        # src_mask: [batch_size, 1, 1, src_len]
        batch_size = src.shape[0]
        src_len = src.shape[1]
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
        # pos: [batch_size, src_len]
        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
        for layer in self.layers:
            src = layer(src, src_mask)
        return src

class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):