请写一个在GPU上进行bert增量预训练的代码

在GPU上进行BERT模型的增量预训练通常涉及深度学习框架如PyTorch或TensorFlow。这里提供一个简化的例子，假设使用PyTorch和Hugging Face的Transformers库。请注意，这只是一个基本框架，实际部署需要更复杂的配置和数据处理步骤。

import torch
from transformers import BertForPreTraining, BertTokenizerFast, AdamW
from torch.utils.data import DataLoader, Dataset

class IncrementalPretrainingDataset(Dataset):
    # 实现自定义的数据加载和处理逻辑
    def __init__(...):
        ...

def train_on_gpu(model, dataloader, optimizer, device):
    model.train()
    for batch in dataloader:
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()

        # 控制梯度更新频率（如每n步一次）
        if step % n_steps == 0:
            optimizer.step()
            optimizer.zero_grad()

# 初始化模型、tokenizer和优化器
model = BertForPreTraining.from_pretrained('bert-base-uncased')
optimizer = AdamW(model.parameters(), lr=your_learning_rate)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 数据集预处理和分块
dataloader = DataLoader(IncrementalPretrainingDataset(), batch_size=batch_size, shuffle=True)

for epoch in range(num_epochs):
    for step, batch in enumerate(train_on_gpu(model, dataloader, optimizer, device)):
        # ... (其他训练过程)