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.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 []


  
You are given the following architecture:   
  
python
import torch
import torch.nn as nn

class Model(nn.Module):
“”"
Model with Linear layer followed by Mish activation.
“”"
def init(self, in_features: int):
super(Model, self).init()
self.linear = nn.Linear(in_features, in_features)

def forward(self, x: torch.Tensor) -> torch.Tensor:  
    """  
    Applies Linear transformation followed by Mish activation.  

    Args:  
        x (torch.Tensor): Input tensor of shape (batch_size, in_features).  

    Returns:  
        torch.Tensor: Output tensor with same shape as input.  
    """  
    x = self.linear(x)  
    return x * torch.tanh(torch.nn.functional.softplus(x))  
batch_size = 4096
in_features = 1024

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

def get_init_inputs():
return [1024] # in_features parameter for initialization


example_torchcode.py


import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model with Linear layer followed by Mish activation.
    """
    def __init__(self, in_features: int):
        super(Model, self).__init__()
        self.linear = nn.Linear(in_features, in_features)
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Applies Linear transformation followed by Mish activation.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_features).

        Returns:
            torch.Tensor: Output tensor with same shape as input.
        """
        x = self.linear(x)
        return x * torch.tanh(torch.nn.functional.softplus(x))

batch_size = 4096
in_features = 1024

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

def get_init_inputs():
    return [1024]  # in_features parameter for initialization
example_cudacode.py


import torch
from torch.utils.cpp_extension import load_inline

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

__device__ __forceinline__ float mish_impl(float x) {
    float softplus_val = (x > 20.0f) ? x : ((x < -20.0f) ? expf(x) : log1pf(expf(x)));
    float tanh_val = tanhf(softplus_val);
    return x * tanh_val;
}

__global__ void mish_kernel(const float* x, float* y, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        y[idx] = mish_impl(x[idx]);
    }
}

torch::Tensor mish_cuda(torch::Tensor x) {
    auto size = x.numel();
    auto y = torch::empty_like(x);
    const int block_size = 256;
    int num_blocks = (size + block_size - 1) / block_size;
    mish_kernel<<<num_blocks, block_size>>>(x.data_ptr<float>(), y.data_ptr<float>(), size);
    return y;
}
"""

mish_cpp_source = """
torch::Tensor mish_cuda(torch::Tensor x);
"""

# Compile the inline CUDA code
mish = load_inline(
    name="mish",
    cpp_sources=mish_cpp_source,
    cuda_sources=mish_source,
    functions=["mish_cuda"],
    verbose=True
)

class ModelNew(torch.nn.Module):
    def __init__(self, in_features: int):
        super(ModelNew, self).__init__()
        torch.manual_seed(42)  # Ensure reproducibility
        self.linear = torch.nn.Linear(in_features, in_features)
        self.mish = mish  # The module containing the kernel

    def forward(self, x):
        x = self.linear(x)
        return self.mish.mish_cuda(x)