给我一个输入8组数输出8组数的pytorch的transformer源码

### 回答1：
以下是一份简化的 PyTorch Transformer 的代码，可以输入 8 组数并输出 8 组数：

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

class Transformer(nn.Module):
    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward):
        super(Transformer, self).__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward)
        self.encoder = nn.TransformerEncoder(self.encoder_layer, num_encoder_layers)
        self.decoder_layer = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward)
        self.decoder = nn.TransformerDecoder(self.decoder_layer, num_decoder_layers)
        self.fc = nn.Linear(d_model, 8)

    def forward(self, src, tgt):
        # src shape: (batch_size, sequence_length, 8)
        # tgt shape: (batch_size, sequence_length, 8)
        memory = self.encoder(src)
        output = self.decoder(tgt, memory)
        return self.fc(output)

d_model = 8
nhead = 2
num_encoder_layers = 2
num_decoder_layers = 2
dim_feedforward = 20
model = Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward)

src = torch.randn(1, 8, 8)
tgt = torch.randn(1, 8, 8)
output = model(src, tgt)
print(output.shape) # torch.Size([1, 8, 8])

这是一份简化版代码，你可以根据你的具体需求进行修改和扩展。

### 回答2：
import torch
import torch.nn as nn
import torch.nn.functional as F

class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()

self.embedding = nn.Embedding(num_embeddings=100, embedding_dim=256)
self.transformer = nn.Transformer(d_model=256, nhead=8, num_encoder_layers=6,
num_decoder_layers=6, dim_feedforward=1024,
dropout=0.1, activation='relu')
self.fc = nn.Linear(256, 100)

def forward(self, x):
x = self.embedding(x)
x = x.permute(1, 0, 2) # Transpose to (seq_len, batch_size, embedding_size)
mask = self.generate_square_subsequent_mask(x.size(0)).to(x.device)
x = self.transformer(x, x, tgt_mask=mask)
x = x.permute(1, 0, 2) # Transpose back to (batch_size, seq_len, embedding_size)
x = self.fc(x)
return x

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

input_data = torch.randn(8, 10, dtype=torch.long) # 输入的8个样本，每个样本有10个数
model = Transformer()
output_data = model(input_data)
print(output_data.size()) # 输出为8组数，每组数有100个维度