使用Python实现GLM解码器的示例（带有Tensor Shape标注）

本文主要是介绍使用Python实现GLM解码器的示例（带有Tensor Shape标注），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

下面是一个示例，演示了如何使用Python和PyTorch实现一个基于GLM（Glancing Language Model）原理的解码器，包括对每个Tensor的shape进行标注。

代码示例

import torch
import torch.nn as nn
import torch.nn.functional as Fclass GlancingDecoder(nn.Module):def __init__(self, vocab_size, hidden_dim, num_layers, glance_rate=0.3):super(GlancingDecoder, self).__init__()self.embedding = nn.Embedding(vocab_size, hidden_dim)  # (vocab_size, hidden_dim)self.rnn = nn.GRU(hidden_dim, hidden_dim, num_layers, batch_first=True)  # (hidden_dim, hidden_dim)self.fc = nn.Linear(hidden_dim, vocab_size)  # (hidden_dim, vocab_size)self.glance_rate = glance_ratedef forward(self, encoder_output, target, teacher_forcing_ratio=0.5):batch_size, seq_len = target.size()  # (batch_size, seq_len)hidden = torch.zeros(self.rnn.num_layers, batch_size, self.rnn.hidden_size).to(target.device)  # (num_layers, batch_size, hidden_dim)inputs = self.embedding(target[:, 0])  # (batch_size, hidden_dim)outputs = torch.zeros(batch_size, seq_len, self.fc.out_features).to(target.device)  # (batch_size, seq_len, vocab_size)for t in range(1, seq_len):rnn_output, hidden = self.rnn(inputs.unsqueeze(1), hidden)  # inputs: (batch_size, 1, hidden_dim), hidden: (num_layers, batch_size, hidden_dim)output = self.fc(rnn_output.squeeze(1))  # rnn_output: (batch_size, 1, hidden_dim) -> squeeze: (batch_size, hidden_dim) -> output: (batch_size, vocab_size)outputs[:, t, :] = output  # (batch_size, seq_len, vocab_size)teacher_force = torch.rand(1).item() < teacher_forcing_ratioinputs = self.embedding(target[:, t]) if teacher_force else output  # (batch_size, hidden_dim)# Glancing mechanism: randomly replace some inputs with ground truth tokensif torch.rand(1).item() < self.glance_rate:glance_mask = torch.rand(batch_size).to(target.device) < self.glance_rateinputs[glance_mask] = self.embedding(target[:, t][glance_mask])  # (batch_size, hidden_dim)return outputs  # (batch_size, seq_len, vocab_size)# 假设一些参数
vocab_size = 1000
hidden_dim = 256
num_layers = 2
seq_len = 10# 假设一些输入
encoder_output = torch.randn(32, seq_len, hidden_dim)  # (batch_size, seq_len, hidden_dim)
target = torch.randint(0, vocab_size, (32, seq_len))  # (batch_size, seq_len)# 创建解码器实例
decoder = GlancingDecoder(vocab_size, hidden_dim, num_layers)
output = decoder(encoder_output, target)print(output.shape)  # (batch_size, seq_len, vocab_size)

代码解释

初始化：
- GlancingDecoder 类初始化了嵌入层、GRU层和全连接层。
- glance_rate 参数决定了在每次迭代中有多少比例的输入会被真实的目标词替换。
前向传播：
- 使用 embedding 将目标序列嵌入到隐层空间。
- 使用 GRU 层对嵌入进行处理，并通过全连接层生成预测。
- 在每次时间步，使用teacher forcing来决定下一个输入是模型的输出还是实际的目标词。
- glance_rate 决定了在每次时间步中，有多大比例的输入会被真实目标词替换。