Write a custom CUDA kernel to optimize `Wing Loss`.

Wing Loss is defined by the formula:
loss(d) = w * ln(1 + |d|/epsilon) if |d| < w
loss(d) = |d| - C              otherwise
where d = target - prediction, and C = w - w * ln(1 + w/epsilon).

Problem Analysis:
1. Memory Bottleneck: The standard PyTorch implementation relies on element-wise operations chained together (subtraction, abs, comparison, log, masking/where). This creates multiple intermediate tensors that must be written to and read from global memory, consuming significant bandwidth.
2. Branching Overhead: The conditional logic creates control flow divergence if not handled efficiently, though the memory access is the primary constraint.

Optimization Strategy: Fused Element-wise Kernel with Vectorization

The strategy is to fuse the entire loss calculation logic into a single CUDA kernel pass.

1. Constant Pre-calculation: The constant 'C' depends only on hyperparameters 'w' and 'epsilon'. It should be pre-calculated on the CPU and passed as a scalar argument to the kernel to save redundant computation.

2. One-Thread-per-Element: Launch a grid where each thread processes one element of the input tensors.

3. Vectorized Loads (float4): Use `float4` data types to load 4 floats (128 bits) at a time per thread. This drastically reduces the number of memory transactions and improves instruction throughput for this memory-bound operation.

4. In-Register Computation: Perform the difference calculation, absolute value, conditional check, and final formula application entirely within registers. This eliminates all intermediate global memory writes.

5. Reduction Handling: The kernel produces element-wise losses. Final reduction (mean or sum) is handled by the C++ wrapper using optimized ATen primitives.
  
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
import math

# 维度：68 points * 2 coordinates (x, y) = 136
NUM_LANDMARKS = 136 
BATCH_SIZE = 32768 
SHAPE = (BATCH_SIZE, NUM_LANDMARKS)

W_VAL = 10.0
EPS_VAL = 2.0

class WingLoss(nn.Module):
    def __init__(self, w=10.0, epsilon=2.0, reduction='mean'):
        super(WingLoss, self).__init__()
        self.w = w
        self.epsilon = epsilon
        self.reduction = reduction
        # Constant C: w - w * ln(1 + w/epsilon)
        self.C = self.w - self.w * math.log(1 + self.w / self.epsilon)

    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        delta = (target - pred).abs()
        # Case 1: Small errors (|x| < w) -> ln form
        loss_small = self.w * torch.log(1 + delta / self.epsilon)
        # Case 2: Large errors (|x| >= w) -> linear form
        loss_large = delta - self.C
        # Combine
        loss = torch.where(delta < self.w, loss_small, loss_large)

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        return loss

class Model(nn.Module):
    def __init__(self, w=10.0, epsilon=2.0, reduction='mean'):
        super(Model, self).__init__()
        self.loss_fn = WingLoss(w=w, epsilon=epsilon, reduction=reduction)
    
    def forward(self, pred, target):
        return self.loss_fn(pred, target)

def get_inputs():
    
    pred = torch.randn(SHAPE, dtype=torch.float32)
    target = torch.randn(SHAPE, dtype=torch.float32)
    
    return [pred.contiguous(), target.contiguous()]

def get_init_inputs():
    return [W_VAL, EPS_VAL, 'none']