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 swish_layernorm 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, hidden_size):
        super().__init__()
        torch.manual_seed(42)
        self.hidden_size = hidden_size
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.eps = 1e-5
    
    def forward(self, x):
        swish_out = x * torch.sigmoid(x)

        mean = swish_out.mean(-1, keepdim=True)
        var = swish_out.var(-1, keepdim=True, unbiased=False)
        swish_out = (swish_out - mean) / torch.sqrt(var + self.eps)

        output = swish_out * self.weight + self.bias
        
        return output

def get_inputs():
    batch_size = 4096
    seq_len = 128
    hidden_size = 768  # 典型的BERT hidden size
    x = torch.randn(batch_size, seq_len, hidden_size)
    return [x]

def get_init_inputs():
    return [768]
New Architecture with Custom CUDA Kernels (swish_layernorm Optimization)
python
运行
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

# Swish + LayerNorm融合的CUDA实现
swish_layernorm_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <cmath>

__device__ __forceinline__ float fast_sigmoid(float x) {
    return 1.0f / (1.0f + expf(-x));
}

__device__ __forceinline__ float swish(float x) {
    return x * fast_sigmoid(x);
}

__global__ void swish_layernorm_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    const int batch_size,
    const int seq_len,
    const int hidden_size,
    const float eps
) {
    int batch_idx = blockIdx.x;
    int seq_idx = blockIdx.y;
    
    if (batch_idx >= batch_size || seq_idx >= seq_len) return;

    int token_start = (batch_idx * seq_len + seq_idx) * hidden_size;

    extern __shared__ float sdata[];

    float sum = 0.0f;
    float sum_sq = 0.0f;
    
    for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
        int idx = token_start + i;
        float val = input[idx];
        float swish_val = swish(val);

        output[idx] = swish_val;
        
        sum += swish_val;
        sum_sq += swish_val * swish_val;
    }
    
    sdata[threadIdx.x] = sum;
    sdata[threadIdx.x + blockDim.x] = sum_sq;
    __syncthreads();

    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
        if (threadIdx.x < stride) {
            sdata[threadIdx.x] += sdata[threadIdx.x + stride];
            sdata[threadIdx.x + blockDim.x] += sdata[threadIdx.x + stride + blockDim.x];
        }
        __syncthreads();
    }
    
    float mean = sdata[0] / hidden_size;
    float var = sdata[blockDim.x] / hidden_size - mean * mean;
    float inv_std = rsqrtf(var + eps);
    
    // 第二遍：应用LayerNorm
    for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
        int idx = token_start + i;
        float normalized = (output[idx] - mean) * inv_std;
        output[idx] = normalized * weight[i] + bias[i];
    }
}

torch::Tensor swish_layernorm_cuda(
    torch::Tensor input,
    torch::Tensor weight,
    torch::Tensor bias,
    float eps = 1e-5f
) {
    TORCH_CHECK(input.is_cuda(), "input must be a CUDA tensor");
    TORCH_CHECK(weight.is_cuda(), "weight must be a CUDA tensor");
    TORCH_CHECK(bias.is_cuda(), "bias must be a CUDA tensor");
    TORCH_CHECK(input.dtype() == torch::kFloat32, "input must be float32");
    TORCH_CHECK(input.dim() == 3, "input must be a 3D tensor");
    
    const int batch_size = input.size(0);
    const int seq_len = input.size(1);
    const int hidden_size = input.size(2);
    
    auto output = torch::empty_like(input);
    
    // 使用warp大小（32）作为线程块大小
    const int threads_per_block = 256;
    const int shared_mem_size = threads_per_block * 2 * sizeof(float);
    
    dim3 blocks(batch_size, seq_len);
    
    swish_layernorm_kernel<<<blocks, threads_per_block, shared_mem_size>>>(
        input.data_ptr<float>(),
        weight.data_ptr<float>(),
        bias.data_ptr<float>(),
        output.data_ptr<float>(),
        batch_size,
        seq_len,
        hidden_size,
        eps
    );
    
    cudaError_t err = cudaGetLastError();
    if (err != cudaSuccess) {
        throw std::runtime_error("CUDA error: " + std::string(cudaGetErrorString(err)));
    }
    
    return output;
}
"""

swish_layernorm_cpp_source = """
torch::Tensor swish_layernorm_cuda(torch::Tensor input, torch::Tensor weight, torch::Tensor bias, float eps);
"""

swish_layernorm_module = load_inline(
    name="swish_layernorm_final",
    cpp_sources=swish_layernorm_cpp_source,
    cuda_sources=swish_layernorm_source,
    functions=["swish_layernorm_cuda"],
    extra_cuda_cflags=["-O3", "--use_fast_math"],
    verbose=False
)

class ModelNew(nn.Module):
    def __init__(self, hidden_size):
        super(ModelNew, self).__init__()
        torch.manual_seed(42)
        self.hidden_size = hidden_size
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.eps = 1e-5
        self.swish_layernorm = swish_layernorm_module.swish_layernorm_cuda
    
    def forward(self, x):
        return self.swish_layernorm(x, self.weight, self.bias, self.eps)
