你需要为下面给定的架构编写自定义 CUDA 内核来替换 PyTorch 原生算子以获得加速。你可以自由选择要替换的算子，保留不需要替换的部分，或进行算子融合。

给定架构：

```python
import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

    def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
        x = logits
        t = targets
        ax = torch.abs(x)
        loss = torch.maximum(x, torch.zeros_like(x)) - x * t + torch.log1p(torch.exp(-ax))
        return loss.mean(dim=1)

batch_size = 16
dim = 16384

def get_inputs():
    logits = torch.randn(batch_size, dim).cuda()
    targets = torch.rand(batch_size, dim).round().cuda()
    return [logits, targets]

def get_init_inputs():
    return []
```

