How do you call the transformer method in PyTorch?

In PyTorch, we can use the torch.nn.Transformer class to invoke a Transformer model. Here is an example code using the Transformer model.

import torch
import torch.nn as nn

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, num_heads):
        super(TransformerModel, self).__init__()
        
        self.transformer = nn.Transformer(
            d_model=input_dim,
            nhead=num_heads,
            num_encoder_layers=num_layers,
            num_decoder_layers=num_layers,
            dim_feedforward=hidden_dim
        )
        
    def forward(self, src, tgt):
        output = self.transformer(src, tgt)
        return output

# 创建Transformer模型实例
input_dim = 512
hidden_dim = 2048
num_layers = 6
num_heads = 8
model = TransformerModel(input_dim, hidden_dim, num_layers, num_heads)

# 准备输入数据
batch_size = 16
src_seq_len = 10
tgt_seq_len = 5
src = torch.randn(batch_size, src_seq_len, input_dim)
tgt = torch.randn(batch_size, tgt_seq_len, input_dim)

# 前向传播
output = model(src, tgt)

In this example, we first define a custom Transformer model class TransformerModel that inherits from nn.Module. In the __init__ method, we use the nn.Transformer class to create a Transformer model and specify the input dimension, hidden layer dimension, number of layers in the encoder and decoder, and number of attention heads. In the forward method, we pass the input data into the Transformer model for forward propagation and return the output.

Then, we instantiated a Transformer model and prepared the input data. Finally, we performed forward propagation by calling the model’s forward method and obtained the output result.