You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups.  

You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.  

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 torch.nn.functional as F

class Model(nn.Module):
def init(self) -> None:
super().init()

def forward(self, a, b):
    return a + b
def get_inputs():
# randomly generate input tensors based on the model architecture
a = torch.randn(1, 128).cuda()
b = torch.randn(1, 128).cuda()
return [a, b]

def get_init_inputs():
# randomly generate tensors required for initialization based on the model architecture
return []


  
The example new arch with custom CUDA kernels looks like this:   
python
import torch
from torch.utils.cpp_extension import load_inline

… CUDA C++ source code for the kernel …
relu_source = """""
…
""""

relu_cpp_source = """""
torch::Tensor relu_cuda(torch::Tensor x);
""""

Compile the inline CUDA code
relu = load_inline(
name="relu",
cpp_sources=relu_cpp_source,
cuda_sources=relu_source,
functions=["relu_cuda"],
verbose=True
)

class ModelNew(torch.nn.Module):
def init(self):
super(ModelNew, self).init()
self.relu = relu # The module containing the kernel

def forward(self, x):
    return self.relu.relu_cuda(x)


You are given the following architecture:   
  
python
import torch
import torch.nn as nn

class Model(nn.Module):
"""
Bray-Curtis Adaptive Triplet Loss implementation.
Computes triplet loss using Bray-Curtis distance with adaptive margin based on sample difficulty.
"""
def init(self, base_margin=1.0, adaptive_factor=0.5, min_margin=0.1, max_margin=2.0):
super(Model, self).init()
self.base_margin = base_margin
self.adaptive_factor = adaptive_factor
self.min_margin = min_margin
self.max_margin = max_margin

def forward(self, anchor: torch.Tensor, positive: torch.Tensor, negative: torch.Tensor) -> torch.Tensor:  
    """  
    Compute Bray-Curtis Adaptive Triplet Loss.

    Args:  
        anchor (torch.Tensor): Anchor samples [batch_size, feature_dim]
        positive (torch.Tensor): Positive samples [batch_size, feature_dim]
        negative (torch.Tensor): Negative samples [batch_size, feature_dim]

    Returns:  
        torch.Tensor: Adaptive triplet loss [batch_size]
    """  
    # Input validation
    if anchor.shape != positive.shape or anchor.shape != negative.shape:
        raise ValueError(f"All input tensors must have the same shape")
    
    if anchor.dim() != 2:
        raise ValueError(f"Input tensors must be 2D, got {anchor.dim()}D")
    
    # Compute Bray-Curtis distances using highly optimized built-in functions
    # Anchor-Positive distance - fully fused computation
    pos_distance = torch.where(
        (torch.abs(anchor) + torch.abs(positive)).sum(dim=1) > 0,
        torch.abs(anchor - positive).sum(dim=1) / (torch.abs(anchor) + torch.abs(positive)).sum(dim=1),
        torch.zeros_like(anchor, device=anchor.device).sum(dim=1)
    )
    
    # Anchor-Negative distance - fully fused computation
    neg_distance = torch.where(
        (torch.abs(anchor) + torch.abs(negative)).sum(dim=1) > 0,
        torch.abs(anchor - negative).sum(dim=1) / (torch.abs(anchor) + torch.abs(negative)).sum(dim=1),
        torch.zeros_like(anchor, device=anchor.device).sum(dim=1)
    )
    
    # Compute adaptive margin based on negative distance (harder samples get larger margin)
    # Harder negatives (smaller neg_distance) get larger margins
    adaptive_margin = self.base_margin + self.adaptive_factor * (1.0 - neg_distance)
    adaptive_margin = torch.clamp(adaptive_margin, self.min_margin, self.max_margin)
    
    # Adaptive triplet loss using built-in ReLU
    loss = torch.relu(pos_distance - neg_distance + adaptive_margin)
    
    return loss

batch_size = 256
feature_dim = 1024

def get_inputs():
# Generate three sets of positive vectors (ecological data is typically non-negative)
anchor = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
positive = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
negative = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
return [anchor, positive, negative]

def get_init_inputs():
return [1.0, 0.5, 0.1, 2.0] # base_margin, adaptive_factor, min_margin, max_margin



Your task is to write a new file `braycurtis_adaptive_triplet_cudacode.py` that defines a new model `ModelNew` which uses a custom CUDA kernel to accelerate the Bray-Curtis Adaptive Triplet Loss calculation. The goal is to achieve a reasonable speedup while maintaining numerical precision.

The recommended implementation strategy is to use a **parallel reduction pattern with adaptive margin computation**:
1.  Launch one thread block for each sample in the batch (`batch_size` number of blocks).
2.  Within each block, have multiple threads collaborate to compute both Bray-Curtis distances for that single sample.
3.  Each thread should iterate over the feature dimension with a stride equal to the block size, accumulating partial sums for both distances simultaneously.
4.  For each element, compute both distance terms in parallel:
     - Anchor-Positive: |anchor_i - positive_i| / (|anchor_i| + |positive_i|)
     - Anchor-Negative: |anchor_i - negative_i| / (|anchor_i| + |negative_i|)
5.  Accumulate four partial sums: pos_num, pos_den, neg_num, neg_den
6.  Use shared memory to store these partial sums (need 4 * block_size space) and then perform parallel reductions to get the final distances for that sample.
7.  The first thread of the block should compute the adaptive margin and final loss:
     - adaptive_margin = base_margin + adaptive_factor * (1.0 - neg_distance)
     - Clamp margin to [min_margin, max_margin] using if-else statements
     - triplet_loss = max(0, pos_distance - neg_distance + adaptive_margin) using conditional expression
8.  Write the final adaptive triplet loss to the output tensor.

Key implementation details:
- Use extern __shared__ float sdata[] for dynamic shared memory allocation
- Load anchor, positive, and negative values simultaneously for better memory coalescing
- Store partial sums in separate regions of shared memory: [pos_num, pos_den, neg_num, neg_den]
- Perform parallel reduction separately for all four arrays in a single loop
- Handle the adaptive margin computation within the kernel using arithmetic operations
- Implement margin clamping using if-else statements (not built-in functions)
- Use conditional expression (triplet_loss > 0.0f) ? triplet_loss : 0.0f for the ReLU operation
- Use TORCH_CHECK macros for comprehensive input validation including all three input tensors
- Use extra_cuda_cflags=["-O3"] for performance (avoid aggressive optimizations that might affect precision)
- Use a standard block size of 256 for optimal performance
- Ensure the ModelNew class properly calls torch.cuda.synchronize() for accurate timing

The implementation should be robust, handle input validation, and focus on complete operator fusion to eliminate all intermediate tensor operations. The adaptive margin computation should provide better training dynamics by adjusting margins based on sample difficulty. The final output should be a single python file containing the CUDA kernel, the compilation logic via `load_inline`, and the new `ModelNew` class.
