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 bilinear Architecture (Base PyTorch Implementation)
python
运行
import torch
import torch.nn.functional as F

class Model(torch.nn.Module):

    def __init__(self):
        super(Model, self).__init__()

    def forward(self, input_image: torch.Tensor, grid: torch.Tensor) -> torch.Tensor:

        return F.grid_sample(input_image, grid, mode='bilinear', padding_mode='zeros', align_corners=True)

def get_inputs():
    # 创建测试数据
    N, C, H_in, W_in = 1, 3, 256, 256
    H_out, W_out = 512, 512
    
    input_image = torch.randn(N, C, H_in, W_in)
    
    # 创建一个简单的放大网格，从(-1, -1)到(1, 1)
    grid_y, grid_x = torch.meshgrid(
        torch.linspace(-1, 1, H_out),
        torch.linspace(-1, 1, W_out),
        indexing='ij'
    )
    grid = torch.stack((grid_x, grid_y), dim=-1) # Shape: (H_out, W_out, 2)
    grid = grid.unsqueeze(0).repeat(N, 1, 1, 1) # Shape: (N, H_out, W_out, 2)
    
    return [input_image, grid]

def get_init_inputs():
    return [] 

New Architecture with Custom CUDA Kernels (bilinear Optimization)
python
运行
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

cuda_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void bilateral_filter_kernel(
    const float* __restrict__ x, 
    float* __restrict__ y, 
    const float* __restrict__ spatial_kernel,
    int N, int C, int H, int W,
    int kernel_size, float sigma_range
) {
    // 线程索引：(n, h, w) -> 处理每个像素
    int w = threadIdx.x;
    int h = blockIdx.x;
    int n = blockIdx.y;
    if (n >= N || h >= H || w >= W) return;

    int padding = kernel_size / 2;
    int center = padding;
    float center_val = x[n * C * H * W + 0 * H * W + h * W + w];  // 单通道
    float sum_weight = 0.0f;
    float sum_val = 0.0f;

    // 遍历3x3窗口
    for (int i = -padding; i <= padding; ++i) {
        for (int j = -padding; j <= padding; ++j) {
            // 边界检查
            int h_idx = h + i;
            int w_idx = w + j;
            if (h_idx < 0 || h_idx >= H || w_idx < 0 || w_idx >= W)
                continue;

            // 计算空间权重（预计算的高斯核）
            int sk_idx = (i + center) * kernel_size + (j + center);
            float sk_weight = spatial_kernel[sk_idx];

            // 计算值域权重
            float val = x[n * C * H * W + 0 * H * W + h_idx * W + w_idx];
            float diff = val - center_val;
            float range_weight = expf(-diff * diff / (2 * sigma_range * sigma_range));

            // 累加加权值和权重
            float weight = sk_weight * range_weight;
            sum_val += val * weight;
            sum_weight += weight;
        }
    }

    // 归一化并输出
    y[n * C * H * W + 0 * H * W + h * W + w] = sum_val / sum_weight;
}

torch::Tensor bilateral_filter_cuda(
    torch::Tensor x, 
    torch::Tensor spatial_kernel,
    int kernel_size, 
    float sigma_range
) {
    x = x.contiguous();
    auto dims = x.sizes();
    int N = dims[0], C = dims[1], H = dims[2], W = dims[3];
    auto y = torch::empty_like(x);

    // 线程配置：W为线程数，H×N为网格数（高效利用线程）
    dim3 block(W);
    dim3 grid(H, N);

    bilateral_filter_kernel<<<grid, block>>>(
        x.data_ptr<float>(),
        y.data_ptr<float>(),
        spatial_kernel.data_ptr<float>(),
        N, C, H, W,
        kernel_size, sigma_range
    );
    return y;
}
"""

cpp_source = """
torch::Tensor bilateral_filter_cuda(
    torch::Tensor x, 
    torch::Tensor spatial_kernel,
    int kernel_size, 
    float sigma_range
);
"""

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

class ModelNew(nn.Module):
    def __init__(self, kernel_size=3, sigma_spatial=1.0, sigma_range=0.1):
        super().__init__()
        self.kernel_size = kernel_size
        self.sigma_range = sigma_range
        # 预计算空间高斯核（修正：移除C++的float关键字）
        center = kernel_size // 2
        kernel = torch.zeros(kernel_size, kernel_size)
        for i in range(kernel_size):
            for j in range(kernel_size):
                # 错误点修正：将float dist改为dist（Python中无需类型声明）
                dist = (i - center) * (i - center) + (j - center) * (j - center)
                kernel[i, j] = torch.exp(-dist / (2 * sigma_spatial * sigma_spatial))
        self.spatial_kernel = kernel / kernel.sum()
        self.spatial_kernel = self.spatial_kernel.cuda()  # 移至GPU

    def forward(self, x):
        return bilateral_filter.bilateral_filter_cuda(
            x, self.spatial_kernel, self.kernel_size, self.sigma_range
        )

def get_inputs():
    return [torch.randn(8, 1, 64, 64).cuda()]  # 确保输入在GPU上

def get_init_inputs():
    return []