transformer code

Sure! Here's a sample code for implementing a basic Transformer model in Python using the PyTorch library:

import torch
import torch.nn as nn

class Transformer(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads, dropout):
        super(Transformer, self).__init__()
        
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.positional_encoding = PositionalEncoding(hidden_dim)
        
        encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)
        
        self.fc = nn.Linear(hidden_dim, input_dim)
        
    def forward(self, src):
        src = self.embedding(src)
        src = self.positional_encoding(src)
        
        output = self.encoder(src)
        
        output = self.fc(output)
        
        return output

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_len=5000):
        super(PositionalEncoding, self).__init__()
        
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_dim, 2) * (-math.log(10000.0) / hidden_dim))
        
        pe = torch.zeros(max_len, hidden_dim)
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        
        pe = pe.unsqueeze(0).transpose(0, 1)
        
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x

This code defines a basic Transformer model for sequence-to-sequence tasks. It includes an embedding layer, positional encoding layer, transformer encoder, and a fully connected layer for the output. You can customize the number of layers, hidden dimensions, number of attention heads, and dropout rate according to your specific task.