gpt搭配自蒸馏的代码样例

以下是一个使用自蒸馏训练GPT模型的代码示例，供您参考：

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from transformers import DistilGPT2Tokenizer, DistilGPT2Model

# 加载GPT2模型和Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
model = GPT2LMHeadModel.from_pretrained('gpt2-medium')

# 自蒸馏
distil_tokenizer = DistilGPT2Tokenizer.from_pretrained('distilgpt2')
distil_model = DistilGPT2Model.from_pretrained('distilgpt2')
for param, distil_param in zip(model.parameters(), distil_model.parameters()):
    distil_param.data.copy_(param.data)

# 在自蒸馏模型上微调
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
distil_model.to(device)
distil_model.train()

optimizer = torch.optim.AdamW(distil_model.parameters(), lr=1e-5)
for epoch in range(3):
    for batch in training_data:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)
        outputs = distil_model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs[0]
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
distil_model.eval()

在这个示例中，我们使用了GPT2-medium模型和DistilGPT2模型进行自蒸馏，然后在DistilGPT2模型上微调。自蒸馏的过程是将GPT2模型的参数复制到DistilGPT2模型中，然后在DistilGPT2模型上进行微调以提高性能。在微调过程中，我们使用AdamW优化器和交叉熵损失函数来训练模型。