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
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 []


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

class Model(nn.Module):
"""
合理优化的PyTorch L1 Loss实现
使用PyTorch内置函数，避免不必要的中间张量创建
"""
def init(self):
super(Model, self).init()

def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:  
    """  
    使用PyTorch内置的l1_loss函数  
    L1 Loss = |input - target|  

    Args:  
        input (torch.Tensor): 预测值  
        target (torch.Tensor): 真实值  

    Returns:  
        torch.Tensor: L1 Loss标量值  
    """  
    # 直接使用内置函数，让PyTorch处理优化  
    return torch.nn.functional.l1_loss(  
        input,  
        target,  
        reduction='sum'  
    )  
batch_size = 128
num_features = 2000

def get_inputs():
"""
生成合理的测试数据
"""
input_vals = torch.randn(batch_size, num_features)
target_vals = torch.randn(batch_size, num_features)
return [input_vals, target_vals]

def get_init_inputs():
return [] # 没有特殊的初始化输入需求


IMPORTANT FUSION REQUIREMENTS:

The current L1 Loss implementation involves multiple steps:
1. Compute difference: diff = input - target
2. Compute absolute value: abs_diff = |diff|
3. Sum reduction: loss = sum(abs_diff)

YOUR TASK: Create a FUSED CUDA kernel that combines ALL these steps into a SINGLE kernel to maximize performance. The fusion should:

1. ELIMINATE intermediate tensor creation (no separate diff or abs_diff tensors)
2. REDUCE memory bandwidth usage by computing diff and abs in-place
3. MINIMIZE kernel launch overhead by using one kernel instead of multiple operations
4. OPTIMIZE for GPU parallelism with efficient reduction strategies

Key fusion strategies to implement:
- Compute (input - target) and |result| in the same thread loop
- Use shared memory for efficient block-level reduction
- Consider warp-level reduction for better performance
- Implement vectorized memory access when possible

The fused kernel should achieve the same numerical result as the original PyTorch implementation while providing significant speedup through operator fusion.

Generate the complete CUDA implementation with:
1. A fused kernel that combines diff + abs + sum operations
2. Multiple optimization variants (basic fusion, warp-level, vectorized)
3. Proper error checking and tensor validation
4. Efficient memory access patterns
5. Comprehensive performance optimizations