Sure, here's an implementation of Transformer with a copy mechanism in Pytorch:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerCopy(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super(TransformerCopy, self).__init__()

        self.encoder_embed = nn.Embedding(src_vocab_size, d_model)
        self.decoder_embed = nn.Embedding(tgt_vocab_size, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.pos_decoder = PositionalEncoding(d_model, dropout)

        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        decoder_layer = nn.TransformerDecoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        self.fc = nn.Linear(d_model, tgt_vocab_size)
        self.copy_fc = nn.Linear(d_model, 1)

    def forward(self, src, tgt):
        src_mask = self.generate_square_subsequent_mask(src.size(1)).to(src.device)
        tgt_mask = self.generate_square_subsequent_mask(tgt.size(1)).to(tgt.device)

        encoder_embed = self.encoder_embed(src) * math.sqrt(self.d_model)
        encoder_embed = self.pos_encoder(encoder_embed)
        encoder_output = self.encoder(encoder_embed, src_mask)

        decoder_embed = self.decoder_embed(tgt) * math.sqrt(self.d_model)
        decoder_embed = self.pos_decoder(decoder_embed)
        decoder_output = self.decoder(decoder_embed, encoder_output, tgt_mask, src_mask)

        logits = self.fc(decoder_output)

        copy_logits = self.copy_fc(encoder_output)
        copy_logits = copy_logits.squeeze(-1)
        copy_probs = F.softmax(copy_logits, dim=-1)

        src_words = src.unsqueeze(-1).expand_as(copy_logits)
        tgt_words = tgt.unsqueeze(-1).expand_as(copy_logits)

        copy_mask = (src_words == tgt_words).float()
        copy_probs = copy_probs * copy_mask
        copy_probs = copy_probs / copy_probs.sum(dim=-1, keepdim=True)

        combined_probs = copy_probs + F.softmax(logits, dim=-1)
        combined_logits = torch.log(combined_probs + 1e-9)

        return combined_logits

    def generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask
```
In this implementation, the Transformer model takes as input a source sequence and a target sequence. The source sequence is embedded and fed into the encoder, while the target sequence is embedded and fed into the decoder. The output of the decoder is then passed through a linear layer to get the final logits.

The copy mechanism is implemented by computing copy probabilities over the source sequence. The copy probabilities are computed using a linear layer followed by a softmax. The copy probabilities are then used to modify the probabilities from the softmax over the target vocabulary, by adding the copy probabilities to the corresponding positions in the target vocabulary distribution. The resulting probabilities are then renormalized to ensure that they sum to 1.

Note that in this implementation, the copy mechanism is only used during inference. During training, the model is trained using standard cross-entropy loss.
물론, 다음은 파이토치에서 복사 메커니즘이 있는 트랜스포머를 구현한 것입니다:
'''python
import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerCopy(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, nhead, num_layers, dropout=0.1):
        super(TransformerCopy, self).__init__()

        self.encoder_embed = nn.Embedding(src_vocab_size, d_model)
        self.decoder_embed = nn.Embedding(tgt_vocab_size, d_model)
        self.pos_encoder = 포지셔널인코딩(d_model, 드롭아웃)
        self.pos_decoder = 포지셔널인코딩(d_model, 드롭아웃)

        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        decoder_layer = nn.TransformerDecoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
        self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        self.fc = nn.Linear(d_model, tgt_vocab_size)
        self.copy_fc = nn.Linear(d_model, 1)

    def forward(self, src, tgt):
        src_mask = self.generate_square_subsequent_mask(src.size(1)).to(src.device)
        tgt_mask = self.generate_square_subsequent_mask(tgt.size(1)).to(tgt.device)

        encoder_embed = self.encoder_embed(src) * math.sqrt(self.d_model)
        encoder_embed = self.pos_encoder(encoder_embed)
        인코더_출력 = self.encoder(encoder_embed, src_mask)

        decoder_embed = self.decoder_embed(tgt) * math.sqrt(self.d_model)
        decoder_embed = self.pos_decoder(decoder_embed)
        decoder_output = self.decoder(decoder_embed, encoder_output, tgt_mask, src_mask)

        logits = self.fc(decoder_output)

        copy_logits = self.copy_fc(encoder_output)
        copy_logits = copy_logits.squeeze(-1)
        copy_probs = F.softmax(copy_logits, dim=-1)

        src_words = src.unsqueeze(-1).expand_as(copy_logits)
        tgt_words = tgt.unsqueeze(-1).expand_as(copy_logits)

        copy_mask = (src_words == tgt_words).float()
        copy_probs = copy_probs * copy_mask
        copy_probs = copy_probs / copy_probs.sum(dim=-1, keepdim=True)

        combined_probs = copy_probs + F.softmax(logits, dim=-1)
        combined_logits = torch.log(combined_probs + 1e-9)

        반환 결합_로그

    def generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        반환 마스크
```
이 구현에서 트랜스포머 모델은 소스 시퀀스와 타깃 시퀀스를 입력으로 받습니다. 소스 시퀀스는 임베드되어 인코더에 공급되고, 타깃 시퀀스는 임베드되어 디코더에 공급됩니다. 그런 다음 디코더의 출력은 선형 레이어를 통과하여 최종 로그를 얻습니다.

복사 메커니즘은 소스 시퀀스에 대한 복사 확률을 계산하여 구현됩니다. 복사 확률은 선형 레이어와 소프트맥스를 사용하여 계산됩니다. 그런 다음 대상 어휘 분포의 해당 위치에 복사 확률을 더하여 대상 어휘에 대한 소프트맥스의 확률을 수정하는 데 복사 확률을 사용합니다. 그런 다음 결과 확률을 다시 정규화하여 합이 1이 되도록 합니다.

이 구현에서는 복사 메커니즘이 추론 중에만 사용된다는 점에 유의하세요. 훈련 중에는 표준 교차 엔트로피 손실을 사용하여 모델을 훈련합니다.