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:   
  
Given Local Response Normalization (LRN) Architecture (Base PyTorch Implementation)
python
运行
import torch
import torch.nn as nn

class SimpleLRNTorch(nn.Module):
    def __init__(self):
        super().__init__()
        # 使用PyTorch原生LRN（固定参数）
        self.lrn = nn.LocalResponseNorm(size=3, alpha=1e-4, beta=0.75, k=2.0)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Computes Local Response Normalization on the input tensor.
        Args:
            x (torch.Tensor): Input tensor with fixed shape (N, C, H, W)
                              where N=8 (batch size), C=32 (number of channels),
                              H=64 (feature map height), W=64 (feature map width).
        Returns:
            torch.Tensor: Normalized output tensor with the same shape as input (N, C, H, W).
        """
        # 输入形状：(N, C, H, W)，仅对通道维度归一化
        return self.lrn(x)

# 测试输入生成
def get_inputs():
    # Randomly generate input tensor matching the fixed (N, C, H, W) shape
    return [torch.randn(8, 32, 64, 64).cuda()]  # 8批次，32通道，64x64特征图

def get_init_inputs():
    # No special initialization tensors needed (model has no trainable parameters)
    return []
New Architecture with Custom CUDA Kernels (LRN Optimization)
python
运行
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

# Define custom LRN CUDA kernel and load it inline
cuda_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void simple_lrn_kernel(
    const float* __restrict__ x, 
    float* __restrict__ y, 
    int N, int C, int H, int W,
    float alpha, float beta, float k
) {
    // 计算全局索引：n(批次), c(通道), h(高), w(宽)
    int w = threadIdx.x;
    int h = blockIdx.x;
    int c = blockIdx.y;
    int n = blockIdx.z;
    
    if (h >= H || w >= W || c >= C) return;
    
    int idx = n * C * H * W + c * H * W + h * W + w;
    float val = x[idx];
    
    // 计算相邻通道的平方和（窗口大小3：左右各1个通道）
    float sum_sq = 0.0f;
    for (int j = max(0, c-1); j <= min(C-1, c+1); ++j) {
        int j_idx = n * C * H * W + j * H * W + h * W + w;
        sum_sq += x[j_idx] * x[j_idx];
    }
    
    // 归一化计算：y = x / (k + alpha * sum_sq)^beta
    y[idx] = val / powf(k + alpha * sum_sq, beta);
}

torch::Tensor simple_lrn_cuda(torch::Tensor x) {
    x = x.contiguous().cuda();
    auto dims = x.sizes(); 
    int N = dims[0], C = dims[1], H = dims[2], W = dims[3];
    auto y = torch::empty_like(x);
    
    dim3 block(W);
    dim3 grid(H, C, N);
    
    simple_lrn_kernel<<<grid, block>>>(
        x.data_ptr<float>(),
        y.data_ptr<float>(),
        N, C, H, W,
        1e-4f, 0.75f, 2.0f  
    );
    return y;
}
"""

cpp_source = "torch::Tensor simple_lrn_cuda(torch::Tensor x);"

# 编译CUDA代码
simple_lrn = load_inline(
    name="simple_lrn",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["simple_lrn_cuda"],
    extra_cuda_cflags=["-O3", "--use_fast_math"]
)

class SimpleLRNCUDA(nn.Module):
    def __init__(self):
        super().__init__()
        self.op = simple_lrn
    
    def forward(self, x):
        """
        Computes LRN using custom CUDA kernel, with same input/output as base implementation.
        Args:
            x (torch.Tensor): Input tensor of shape (N, C, H, W) (CUDA tensor).
        Returns:
            torch.Tensor: Normalized output tensor of shape (N, C, H, W).
        """
        return self.op.simple_lrn_cuda(x)

def get_inputs():
    # 保持与base实现一致的输入生成方式
    return [torch.randn(8, 32, 64, 64).cuda()]

def get_init_inputs():
    # 无需要初始化的参数
    return []
