请用pytorch实现transformer并训练

好的，以下是使用PyTorch实现Transformer并进行训练的代码：

首先，我们需要导入必要的库和设置一些超参数：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader

import random
import math
import time

# 设置超参数
BATCH_SIZE = 64
NUM_EPOCHS = 10
LR = 0.0005
D_MODEL = 512
N_HEADS = 8
N_ENCODER_LAYERS = 6
N_DECODER_LAYERS = 6
D_FF = 2048
DROPOUT = 0.1

接下来，我们需要定义Transformer中常用的几个模块，包括Multi-head Attention、Position-wise Feedforward等：

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, n_heads, dropout):
        super(MultiHeadAttention, self).__init__()
        self.d_model = d_model
        self.n_heads = n_heads

        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)
        self.output_linear = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)

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

        # 计算Q、K、V
        Q = self.query_linear(query)
        K = self.key_linear(key)
        V = self.value_linear(value)

        # 将Q、K、V分成多个头
        Q = Q.view(batch_size * self.n_heads, -1, self.d_model // self.n_heads)
        K = K.view(batch_size * self.n_heads, -1, self.d_model // self.n_heads)
        V = V.view(batch_size * self.n_heads, -1, self.d_model // self.n_heads)

        # 计算Attention分数
        attn_scores = torch.bmm(Q, K.transpose(1, 2))
        attn_scores = attn_scores / math.sqrt(self.d_model // self.n_heads)

        # 掩码操作
        if mask is not None:
            mask = mask.unsqueeze(1).repeat(1, self.n_heads, 1, 1)
            attn_scores = attn_scores.masked_fill(mask == 0, -1e9)

        # 计算Attention权重
        attn_weights = F.softmax(attn_scores, dim=-1)
        attn_weights = self.dropout(attn_weights)

        # 计算输出
        output = torch.bmm(attn_weights, V)
        output = output.view(batch_size, -1, self.n_heads * (self.d_model // self.n_heads))
        output = self.output_linear(output)

        return output


class PositionwiseFeedforward(nn.Module):
    def __init__(self, d_model, d_ff, dropout):
        super(PositionwiseFeedforward, 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 = self.linear1(x)
        x = F.relu(x)
        x = self.dropout(x)
        x = self.linear2(x)
        return x

接下来，我们需要定义Transformer的编码器和解码器：

class Encoder(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout, n_layers):
        super(Encoder, self).__init__()
        self.layers = nn.ModuleList()
        for _ in range(n_layers):
            self.layers.append(nn.Sequential(
                nn.LayerNorm(d_model),
                MultiHeadAttention(d_model, n_heads, dropout),
                nn.Dropout(dropout),
                nn.LayerNorm(d_model),
                PositionwiseFeedforward(d_model, d_ff, dropout),
                nn.Dropout(dropout)
            ))

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


class Decoder(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout, n_layers):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList()
        for _ in range(n_layers):
            self.layers.append(nn.Sequential(
                nn.LayerNorm(d_model),
                MultiHeadAttention(d_model, n_heads, dropout),
                nn.Dropout(dropout),
                nn.LayerNorm(d_model),
                MultiHeadAttention(d_model, n_heads, dropout),
                nn.Dropout(dropout),
                nn.LayerNorm(d_model),
                PositionwiseFeedforward(d_model, d_ff, dropout),
                nn.Dropout(dropout)
            ))

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, memory, tgt_mask)
        return x

最后，我们需要定义完整的Transformer模型：

class Transformer(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, n_heads, d_ff, dropout, n_encoder_layers, n_decoder_layers):
        super(Transformer, self).__init__()
        self.src_embedding = nn.Embedding(src_vocab_size, d_model)
        self.tgt_embedding = nn.Embedding(tgt_vocab_size, d_model)
        self.encoder = Encoder(d_model, n_heads, d_ff, dropout, n_encoder_layers)
        self.decoder = Decoder(d_model, n_heads, d_ff, dropout, n_decoder_layers)
        self.output_linear = nn.Linear(d_model, tgt_vocab_size)

    def forward(self, src, tgt, src_mask, tgt_mask):
        src_emb = self.src_embedding(src)
        tgt_emb = self.tgt_embedding(tgt)

        memory = self.encoder(src_emb, src_mask)
        output = self.decoder(tgt_emb, memory, src_mask, tgt_mask)

        output = self.output_linear(output)
        return output

接下来，我们需要定义数据集和数据加载器：

class TranslationDataset(Dataset):
    def __init__(self, src_data, tgt_data):
        self.src_data = src_data
        self.tgt_data = tgt_data

    def __getitem__(self, i):
        return self.src_data[i], self.tgt_data[i]

    def __len__(self):
        return len(self.src_data)


def collate_fn(batch):
    src_batch, tgt_batch = zip(*batch)
    src_batch = nn.utils.rnn.pad_sequence(src_batch, batch_first=True)
    tgt_batch = nn.utils.rnn.pad_sequence(tgt_batch, batch_first=True)
    src_mask = (src_batch != 0).unsqueeze(-2)
    tgt_mask = (tgt_batch != 0).unsqueeze(-2)
    return src_batch, tgt_batch, src_mask, tgt_mask


train_dataset = TranslationDataset(train_src_data, train_tgt_data)
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_fn)

接下来，我们需要定义损失函数和优化器：

criterion = nn.CrossEntropyLoss(ignore_index=0)
optimizer = optim.Adam(model.parameters(), lr=LR)

最后，我们可以开始训练模型了：

for epoch in range(NUM_EPOCHS):
    total_loss = 0
    start_time = time.time()

    for i, (src_batch, tgt_batch, src_mask, tgt_mask) in enumerate(train_loader):
        src_batch = src_batch.to(device)
        tgt_batch = tgt_batch.to(device)
        src_mask = src_mask.to(device)
        tgt_mask = tgt_mask.to(device)

        optimizer.zero_grad()

        output = model(src_batch, tgt_batch[:, :-1], src_mask, tgt_mask[:, :-1])
        output = output.contiguous().view(-1, output.size(-1))
        tgt_batch = tgt_batch[:, 1:].contiguous().view(-1)

        loss = criterion(output, tgt_batch)
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

        if (i + 1) % 100 == 0:
            print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch+1, NUM_EPOCHS, i+1, len(train_loader), total_loss / (i+1)))

    end_time = time.time()
    print('Epoch [%d/%d], Loss: %.4f, Time: %.2fs' % (epoch+1, NUM_EPOCHS, total_loss / len(train_loader), end_time - start_time))

在训练完成后，我们就可以使用训练好的模型进行推理了，具体方法可以参考这篇文章：[使用Transformer进行翻译](https://www.cnblogs.com/pinard/p/11131803.html)。