Write a custom CUDA kernel to optimize `LogLU` (Logarithmic Linear Unit).

Formula:
  f(x) = x               if x >= 0
  f(x) = -log(-x + 1)    if x < 0

Problem Analysis:
1. Memory Bound: This is an element-wise activation. Performance is strictly limited by memory bandwidth.
2. Operator Chaining: A PyTorch implementation using `torch.where` creates intermediate tensors for the mask and the results of the log operation.

Optimization Strategy: Fused Element-wise Kernel with Vectorization

1. One-Thread-per-Element: Map each element to a CUDA thread.

2. Vectorized Loads (float4): Use `float4` to process 128 bits per memory transaction to maximize throughput.

3. Fused Branching Logic:
   - For each element `x`, check `if (x < 0)`.
   - If true, compute `-logf(-x + 1.0f)`.
   - If false, the result is `x`.
   - This logic is fused in-register.

4. Fast Math: Use the `__logf` intrinsic for faster logarithm computation if precision allows.
  
Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:   
  
```python
import torch
import torch.nn as nn

BATCH_SIZE = 4096
HIDDEN_DIM = 4096
SHAPE = (BATCH_SIZE, HIDDEN_DIM)

class LogLU(nn.Module):
    """
    Logarithmic Linear Unit (LogLU).
    https://openreview.net/forum?id=1D3TjFidCS
    Formula:
      f(x) = x               if x >= 0
      f(x) = -log(-x + 1)    if x < 0
    """
    def __init__(self):
        super(LogLU, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        pos_part = x
        neg_part = -torch.log(-x + 1.0)
        return torch.where(x >= 0, pos_part, neg_part)

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.act = LogLU()
    
    def forward(self, x):
        return self.act(x)

def get_inputs():
    input_tensor = torch.randn(SHAPE, dtype=torch.float32)
    input_tensor = torch.clamp(input_tensor, max=0.999)
    return [input_tensor.contiguous()]

def get_init_inputs():
    return []