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

class Model(nn.Module):
    def __init__(self, in_features):
        super().__init__()
        torch.manual_seed(42)
        self.linear1 = nn.Linear(in_features, in_features)  # 特征变换
        self.linear2 = nn.Linear(in_features, in_features)  # 门控变换

    def forward(self, x):
        x1 = self.linear1(x)  # 主分支
        x2 = self.linear2(x)  # 门控分支
        return x1 * torch.sigmoid(1.702 * x2)  # SwiGLU公式

def get_inputs():
    batch_size = 512
    in_features = 512
    x = torch.randn(batch_size, in_features)
    return [x]

def get_init_inputs():
    return [512]  # in_features

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

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

__device__ __forceinline__ float sigmoid(float x) {
    // 针对1.702系数优化的sigmoid计算
    return 1.0f / (1.0f + expf(-1.702f * x));
}

__global__ void swiglu_kernel(
    const float* __restrict__ x1,  // 主分支
    const float* __restrict__ x2,  // 门控分支
    float* __restrict__ output,
    const int size
) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= size) return;
    output[idx] = x1[idx] * sigmoid(x2[idx]);
}

torch::Tensor swiglu_cuda(torch::Tensor x1, torch::Tensor x2) {
    TORCH_CHECK(x1.is_cuda() && x2.is_cuda(), "inputs must be CUDA tensors");
    TORCH_CHECK(x1.dtype() == torch::kFloat32 && x2.dtype() == torch::kFloat32, "must be float32");
    TORCH_CHECK(x1.sizes() == x2.sizes(), "x1 and x2 must have same shape");

    x1 = x1.contiguous();
    x2 = x2.contiguous();
    const int total_size = x1.numel();
    auto output = torch::empty_like(x1);

    const int threads_per_block = 256;
    const int blocks = (total_size + threads_per_block - 1) / threads_per_block;

    swiglu_kernel<<<blocks, threads_per_block>>>(
        x1.data_ptr<float>(),
        x2.data_ptr<float>(),
        output.data_ptr<float>(),
        total_size
    );

    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {
        throw std::runtime_error("CUDA error: " + std::string(cudaGetErrorString(err)));
    }

    return output;
}
"""

swiglu_cpp_source = """
torch::Tensor swiglu_cuda(torch::Tensor x1, torch::Tensor x2);
"""

swiglu_module = load_inline(
    name="swiglu_final",
    cpp_sources=swiglu_cpp_source,
    cuda_sources=swiglu_source,
    functions=["swiglu_cuda"],
    extra_cuda_cflags=["-O2"],
    verbose=False
)

class ModelNew(nn.Module):
    def __init__(self, in_features):
        super().__init__()
        torch.manual_seed(42)
        self.linear1 = nn.Linear(in_features, in_features)
        self.linear2 = nn.Linear(in_features, in_features)
        self.swiglu = swiglu_module.swiglu_cuda

    def forward(self, x):
        x1 = self.linear1(x)
        x2 = self.linear2(x)
        return self.swiglu(x1, x2)