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.utils.cpp_extension
from torch.utils.cpp_extension import load_inline

add_source = “”"
#include <torch/extension.h>
#include <cuda_runtime.h>

global void add_kernel(const float* a, const float* b, float* c, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
c[idx] = a[idx] + b[idx];
}
}

torch::Tensor add_cuda(torch::Tensor a, torch::Tensor b) {
auto size = a.numel();
auto c = torch::empty_like(a);
const int block_size = 256;
int num_blocks = (size + block_size - 1) / block_size;
add_kernel<<<num_blocks, block_size>>>(a.data_ptr<float>(), b.data_ptr<float>(), c.data_ptr<float>(), size);
return c;
}
“”"

add_cpp_source = “”"
torch::Tensor add_cuda(torch::Tensor a, torch::Tensor b);
“”"

Compile the inline CUDA code
add = load_inline(
name=“add”,
cpp_sources=add_cpp_source,
cuda_sources=add_source,
functions=[“add_cuda”],
verbose=True
)

class Model(nn.Module):
def init(self) -> None:
super().init()
self.add = add # The module containing the kernel

def forward(self, a, b):
    return self.add.add_cuda(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):
“”"
Simple model that performs InstanceNorm operation.
“”"
def init(self, num_features=64, eps=1e-5, affine=True, track_running_stats=False):
super(Model, self).init()
self.num_features = num_features
self.eps = eps
self.affine = affine
self.track_running_stats = track_running_stats

    # building InstanceNorm 
    self.instance_norm = nn.InstanceNorm2d(  
        num_features=num_features,  
        eps=eps,  
        affine=affine,  
        track_running_stats=track_running_stats  
    )  

def forward(self, x: torch.Tensor) -> torch.Tensor:  
    """  
    Applies InstanceNorm to the input tensor.  

    Args:  
        x (torch.Tensor): Input tensor of shape [batch_size, num_features, height, width]  

    Returns:  
        torch.Tensor: Output tensor after instance normalization, same shape as input.  
    """  
    return self.instance_norm(x)  

batch_size = 32
num_features = 64
height = 128
width = 128

def get_inputs():
“”"
生成InstanceNorm的输入张量。

Returns:  
    list: 包含一个形状为 [batch_size, num_features, height, width] 的张量  
"""  
x = torch.randn(batch_size, num_features, height, width)  
return [x]  
def get_init_inputs():
“”"
获取模型初始化所需的输入（空列表，因为不需要特殊初始化）。

Returns:  
    list: 空列表  
"""  
return []  # No special initialization inputs needed  